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https://proceedings.mlr.press/v235/abad-rocamora24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/abad-rocamora24a/abad-rocamora24a.pdf | https://openreview.net/forum?id=AZWqXfM6z9 | Revisiting Character-level Adversarial Attacks for Language Models | https://proceedings.mlr.press/v235/abad-rocamora24a.html | Elias Abad Rocamora, Yongtao Wu, Fanghui Liu, Grigorios Chrysos, Volkan Cevher | https://proceedings.mlr.press/v235/abad-rocamora24a.html | ICML 2024 | Adversarial attacks in Natural Language Processing apply perturbations in the character or token levels. Token-level attacks, gaining prominence for their use of gradient-based methods, are susceptible to altering sentence semantics, leading to invalid adversarial examples. While character-level attacks easily maintain... |
https://proceedings.mlr.press/v235/abe24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/abe24a/abe24a.pdf | https://openreview.net/forum?id=9U29U3cDKq | Adaptively Perturbed Mirror Descent for Learning in Games | https://proceedings.mlr.press/v235/abe24a.html | Kenshi Abe, Kaito Ariu, Mitsuki Sakamoto, Atsushi Iwasaki | https://proceedings.mlr.press/v235/abe24a.html | ICML 2024 | This paper proposes a payoff perturbation technique for the Mirror Descent (MD) algorithm in games where the gradient of the payoff functions is monotone in the strategy profile space, potentially containing additive noise. The optimistic family of learning algorithms, exemplified by optimistic MD, successfully achieve... |
https://proceedings.mlr.press/v235/abhyankar24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/abhyankar24a/abhyankar24a.pdf | https://openreview.net/forum?id=wDDGQabYPQ | InferCept: Efficient Intercept Support for Augmented Large Language Model Inference | https://proceedings.mlr.press/v235/abhyankar24a.html | Reyna Abhyankar, Zijian He, Vikranth Srivatsa, Hao Zhang, Yiying Zhang | https://proceedings.mlr.press/v235/abhyankar24a.html | ICML 2024 | Large language models are increasingly integrated with external environments, tools, and agents like ChatGPT plugins to extend their capability beyond language-centric tasks. However, today’s LLM inference systems are designed for standalone LLMs. They treat each external interaction as the end of LLM generation and fo... |
https://proceedings.mlr.press/v235/acharya24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/acharya24a/acharya24a.pdf | https://openreview.net/forum?id=MurkwIl0h3 | Balancing Feature Similarity and Label Variability for Optimal Size-Aware One-shot Subset Selection | https://proceedings.mlr.press/v235/acharya24a.html | Abhinab Acharya, Dayou Yu, Qi Yu, Xumin Liu | https://proceedings.mlr.press/v235/acharya24a.html | ICML 2024 | Subset or core-set selection offers a data-efficient way for training deep learning models. One-shot subset selection poses additional challenges as subset selection is only performed once and full set data become unavailable after the selection. However, most existing methods tend to choose either diverse or difficult... |
https://proceedings.mlr.press/v235/achituve24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/achituve24a/achituve24a.pdf | https://openreview.net/forum?id=GiHo83ozsF | Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning | https://proceedings.mlr.press/v235/achituve24a.html | Idan Achituve, Idit Diamant, Arnon Netzer, Gal Chechik, Ethan Fetaya | https://proceedings.mlr.press/v235/achituve24a.html | ICML 2024 | As machine learning becomes more prominent there is a growing demand to perform several inference tasks in parallel. Multi-task learning (MTL) addresses this challenge by learning a single model that solves several tasks simultaneously and efficiently. Often optimizing MTL models entails first computing the gradient of... |
https://proceedings.mlr.press/v235/achtibat24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/achtibat24a/achtibat24a.pdf | https://openreview.net/forum?id=emtXYlBrNF | AttnLRP: Attention-Aware Layer-Wise Relevance Propagation for Transformers | https://proceedings.mlr.press/v235/achtibat24a.html | Reduan Achtibat, Sayed Mohammad Vakilzadeh Hatefi, Maximilian Dreyer, Aakriti Jain, Thomas Wiegand, Sebastian Lapuschkin, Wojciech Samek | https://proceedings.mlr.press/v235/achtibat24a.html | ICML 2024 | Large Language Models are prone to biased predictions and hallucinations, underlining the paramount importance of understanding their model-internal reasoning process. However, achieving faithful attributions for the entirety of a black-box transformer model and maintaining computational efficiency is an unsolved chall... |
https://proceedings.mlr.press/v235/adcock24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/adcock24a/adcock24a.pdf | https://openreview.net/forum?id=wG2SgnH6Zv | A Unified Framework for Learning with Nonlinear Model Classes from Arbitrary Linear Samples | https://proceedings.mlr.press/v235/adcock24a.html | Ben Adcock, Juan M. Cardenas, Nick Dexter | https://proceedings.mlr.press/v235/adcock24a.html | ICML 2024 | This work considers the fundamental problem of learning an unknown object from training data using a given model class. We introduce a framework that allows for objects in arbitrary Hilbert spaces, general types of (random) linear measurements as training data and general types of nonlinear model classes. We establish ... |
https://proceedings.mlr.press/v235/adepu24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/adepu24a/adepu24a.pdf | https://openreview.net/forum?id=xPypr0kufs | FrameQuant: Flexible Low-Bit Quantization for Transformers | https://proceedings.mlr.press/v235/adepu24a.html | Harshavardhan Adepu, Zhanpeng Zeng, Li Zhang, Vikas Singh | https://proceedings.mlr.press/v235/adepu24a.html | ICML 2024 | Transformers are the backbone of powerful foundation models for many Vision and Natural Language Processing tasks. But their compute and memory/storage footprint is large, and so, serving such models is expensive often requiring high-end hardware. To mitigate this difficulty, Post-Training Quantization seeks to modify ... |
https://proceedings.mlr.press/v235/adhikary24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/adhikary24a/adhikary24a.pdf | https://openreview.net/forum?id=myCgfQZzbc | BeigeMaps: Behavioral Eigenmaps for Reinforcement Learning from Images | https://proceedings.mlr.press/v235/adhikary24a.html | Sandesh Adhikary, Anqi Li, Byron Boots | https://proceedings.mlr.press/v235/adhikary24a.html | ICML 2024 | Training reinforcement learning (RL) agents directly from high-dimensional image observations continues to be a challenging problem. Recent line of work on behavioral distances proposes to learn representations that encode behavioral similarities quantified by the bisimulation metric. By learning an isometric mapping t... |
https://proceedings.mlr.press/v235/adila24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/adila24a/adila24a.pdf | https://openreview.net/forum?id=dztd61efGy | Discovering Bias in Latent Space: An Unsupervised Debiasing Approach | https://proceedings.mlr.press/v235/adila24a.html | Dyah Adila, Shuai Zhang, Boran Han, Bernie Wang | https://proceedings.mlr.press/v235/adila24a.html | ICML 2024 | The question-answering (QA) capabilities of foundation models are highly sensitive to prompt variations, rendering their performance susceptible to superficial, non-meaning-altering changes. This vulnerability often stems from the model’s preference or bias towards specific input characteristics, such as option positio... |
https://proceedings.mlr.press/v235/afshani24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/afshani24a/afshani24a.pdf | https://openreview.net/forum?id=8iWDWQKxJ1 | Optimal Coresets for Low-Dimensional Geometric Median | https://proceedings.mlr.press/v235/afshani24a.html | Peyman Afshani, Chris Schwiegelshohn | https://proceedings.mlr.press/v235/afshani24a.html | ICML 2024 | We investigate coresets for approximating the cost with respect to median queries. In this problem, we are given a set of points $P\subset \mathbb{R}^d$ and median queries are $\sum_{p\in P} ||p-c||$ for any point $c\in \mathbb{R}^d$. Our goal is to compute a small weighted summary $S\subset P$ such that the cost of an... |
https://proceedings.mlr.press/v235/afzal24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/afzal24a/afzal24a.pdf | https://openreview.net/forum?id=9GbAea74O6 | REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates | https://proceedings.mlr.press/v235/afzal24a.html | Arshia Afzal, Grigorios Chrysos, Volkan Cevher, Mahsa Shoaran | https://proceedings.mlr.press/v235/afzal24a.html | ICML 2024 | EEG-based seizure detection models face challenges in terms of inference speed and memory efficiency, limiting their real-time implementation in clinical devices. This paper introduces a novel graph-based residual state update mechanism (REST) for real-time EEG signal analysis in applications such as epileptic seizure ... |
https://proceedings.mlr.press/v235/agarwal24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/agarwal24a/agarwal24a.pdf | https://openreview.net/forum?id=xcDRx8vzCa | CHAI: Clustered Head Attention for Efficient LLM Inference | https://proceedings.mlr.press/v235/agarwal24a.html | Saurabh Agarwal, Bilge Acun, Basil Hosmer, Mostafa Elhoushi, Yejin Lee, Shivaram Venkataraman, Dimitris Papailiopoulos, Carole-Jean Wu | https://proceedings.mlr.press/v235/agarwal24a.html | ICML 2024 | Large Language Models (LLMs) with hundreds of billions of parameters have transformed the field of machine learning. However, serving these models at inference time is both compute and memory intensive, where a single request can require multiple GPUs and tens of Gigabytes of memory. Multi-head attention is one of the ... |
https://proceedings.mlr.press/v235/agarwal24b.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/agarwal24b/agarwal24b.pdf | https://openreview.net/forum?id=w8BnKGFIYN | Learning to Play Atari in a World of Tokens | https://proceedings.mlr.press/v235/agarwal24b.html | Pranav Agarwal, Sheldon Andrews, Samira Ebrahimi Kahou | https://proceedings.mlr.press/v235/agarwal24b.html | ICML 2024 | Model-based reinforcement learning agents utilizing transformers have shown improved sample efficiency due to their ability to model extended context, resulting in more accurate world models. However, for complex reasoning and planning tasks, these methods primarily rely on continuous representations. This complicates ... |
https://proceedings.mlr.press/v235/agarwal24c.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/agarwal24c/agarwal24c.pdf | https://openreview.net/forum?id=EqFxIbGWRU | Probabilistic Generating Circuits - Demystified | https://proceedings.mlr.press/v235/agarwal24c.html | Sanyam Agarwal, Markus Bläser | https://proceedings.mlr.press/v235/agarwal24c.html | ICML 2024 | Zhang et al. (ICML 2021, PLMR 139, pp. 12447–12457) introduced probabilistic generating circuits (PGCs) as a probabilistic model to unify probabilistic circuits (PCs) and determinantal point processes (DPPs). At a first glance, PGCs store a distribution in a very different way, they compute the probability generating p... |
https://proceedings.mlr.press/v235/agarwal24d.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/agarwal24d/agarwal24d.pdf | https://openreview.net/forum?id=xl2yU3dsHK | Improved Differentially Private and Lazy Online Convex Optimization: Lower Regret without Smoothness Requirements | https://proceedings.mlr.press/v235/agarwal24d.html | Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Guha Thakurta | https://proceedings.mlr.press/v235/agarwal24d.html | ICML 2024 | We design differentially private regret-minimizing algorithms in the online convex optimization (OCO) framework. Unlike recent results, our algorithms and analyses do not require smoothness, thus yielding the first private regret bounds with an optimal leading-order term for non-smooth loss functions. Additionally, eve... |
https://proceedings.mlr.press/v235/agarwal24e.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/agarwal24e/agarwal24e.pdf | https://openreview.net/forum?id=MMMHufVc2v | The Non-linear $F$-Design and Applications to Interactive Learning | https://proceedings.mlr.press/v235/agarwal24e.html | Alekh Agarwal, Jian Qian, Alexander Rakhlin, Tong Zhang | https://proceedings.mlr.press/v235/agarwal24e.html | ICML 2024 | We propose a generalization of the classical G-optimal design concept to non-linear function classes. The criterion, termed F -design, coincides with G-design in the linear case. We compute the value of the optimal design, termed the F-condition number, for several non-linear function classes. We further provide algori... |
https://proceedings.mlr.press/v235/agnihotri24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/agnihotri24a/agnihotri24a.pdf | https://openreview.net/forum?id=dmfvHU1LNF | ACPO: A Policy Optimization Algorithm for Average MDPs with Constraints | https://proceedings.mlr.press/v235/agnihotri24a.html | Akhil Agnihotri, Rahul Jain, Haipeng Luo | https://proceedings.mlr.press/v235/agnihotri24a.html | ICML 2024 | Reinforcement Learning (RL) for constrained MDPs (CMDPs) is an increasingly important problem for various applications. Often, the average criterion is more suitable than the discounted criterion. Yet, RL for average-CMDPs (ACMDPs) remains a challenging problem. Algorithms designed for discounted constrained RL problem... |
https://proceedings.mlr.press/v235/agnihotri24b.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/agnihotri24b/agnihotri24b.pdf | https://openreview.net/forum?id=CXZqGJonmt | CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks | https://proceedings.mlr.press/v235/agnihotri24b.html | Shashank Agnihotri, Steffen Jung, Margret Keuper | https://proceedings.mlr.press/v235/agnihotri24b.html | ICML 2024 | While neural networks allow highly accurate predictions in many tasks, their lack of robustness towards even slight input perturbations often hampers their deployment. Adversarial attacks such as the seminal projected gradient descent (PGD) offer an effective means to evaluate a model’s robustness and dedicated solutio... |
https://proceedings.mlr.press/v235/agostinelli-iii24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/agostinelli-iii24a/agostinelli-iii24a.pdf | https://openreview.net/forum?id=XhH1OKLANY | LeaPformer: Enabling Linear Transformers for Autoregressive and Simultaneous Tasks via Learned Proportions | https://proceedings.mlr.press/v235/agostinelli-iii24a.html | Victor Agostinelli Iii, Sanghyun Hong, Lizhong Chen | https://proceedings.mlr.press/v235/agostinelli-iii24a.html | ICML 2024 | A promising approach to preserving model performance in linearized transformers is to employ position-based re-weighting functions. However, state-of-the-art re-weighting functions rely heavily on target sequence lengths, making it difficult or impossible to apply them to autoregressive and simultaneous tasks, where th... |
https://proceedings.mlr.press/v235/agrawal24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/agrawal24a/agrawal24a.pdf | https://openreview.net/forum?id=bID9PiBFpT | Policy Evaluation for Variance in Average Reward Reinforcement Learning | https://proceedings.mlr.press/v235/agrawal24a.html | Shubhada Agrawal, Prashanth L A, Siva Theja Maguluri | https://proceedings.mlr.press/v235/agrawal24a.html | ICML 2024 | We consider an average reward reinforcement learning (RL) problem and work with asymptotic variance as a risk measure to model safety-critical applications. We design a temporal-difference (TD) type algorithm tailored for policy evaluation in this context. Our algorithm is based on linear stochastic approximation of an... |
https://proceedings.mlr.press/v235/ahdritz24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/ahdritz24a/ahdritz24a.pdf | https://openreview.net/forum?id=ud4GSrqUKI | Distinguishing the Knowable from the Unknowable with Language Models | https://proceedings.mlr.press/v235/ahdritz24a.html | Gustaf Ahdritz, Tian Qin, Nikhil Vyas, Boaz Barak, Benjamin L. Edelman | https://proceedings.mlr.press/v235/ahdritz24a.html | ICML 2024 | We study the feasibility of identifying epistemic uncertainty (reflecting a lack of knowledge), as opposed to aleatoric uncertainty (reflecting entropy in the underlying distribution), in the outputs of large language models (LLMs) over free-form text. In the absence of ground-truth probabilities, we explore a setting ... |
https://proceedings.mlr.press/v235/ahmadian24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/ahmadian24a/ahmadian24a.pdf | https://openreview.net/forum?id=jaJxpKkBcL | Unmasking Vulnerabilities: Cardinality Sketches under Adaptive Inputs | https://proceedings.mlr.press/v235/ahmadian24a.html | Sara Ahmadian, Edith Cohen | https://proceedings.mlr.press/v235/ahmadian24a.html | ICML 2024 | Cardinality sketches are popular data structures that enhance the efficiency of working with large data sets. The sketches are randomized representations of sets that are only of logarithmic size but can support set merges and approximate cardinality (i.e., distinct count) queries. When queries are not adaptive, that i... |
https://proceedings.mlr.press/v235/ahmaditeshnizi24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/ahmaditeshnizi24a/ahmaditeshnizi24a.pdf | https://openreview.net/forum?id=YT1dtdLvSN | OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Models | https://proceedings.mlr.press/v235/ahmaditeshnizi24a.html | Ali Ahmaditeshnizi, Wenzhi Gao, Madeleine Udell | https://proceedings.mlr.press/v235/ahmaditeshnizi24a.html | ICML 2024 | Optimization problems are pervasive in sectors from manufacturing and distribution to healthcare. However, most such problems are still solved heuristically by hand rather than optimally by state-of-the-art solvers because the expertise required to formulate and solve these problems limits the widespread adoption of op... |
https://proceedings.mlr.press/v235/ahn24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/ahn24a/ahn24a.pdf | https://openreview.net/forum?id=tpYHbEl7P1 | How to Escape Sharp Minima with Random Perturbations | https://proceedings.mlr.press/v235/ahn24a.html | Kwangjun Ahn, Ali Jadbabaie, Suvrit Sra | https://proceedings.mlr.press/v235/ahn24a.html | ICML 2024 | Modern machine learning applications have witnessed the remarkable success of optimization algorithms that are designed to find flat minima. Motivated by this design choice, we undertake a formal study that (i) formulates the notion of flat minima, and (ii) studies the complexity of finding them. Specifically, we adopt... |
https://proceedings.mlr.press/v235/ahn24b.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/ahn24b/ahn24b.pdf | https://openreview.net/forum?id=iE2lMjeXRR | Understanding Adam Optimizer via Online Learning of Updates: Adam is FTRL in Disguise | https://proceedings.mlr.press/v235/ahn24b.html | Kwangjun Ahn, Zhiyu Zhang, Yunbum Kook, Yan Dai | https://proceedings.mlr.press/v235/ahn24b.html | ICML 2024 | Despite the success of the Adam optimizer in practice, the theoretical understanding of its algorithmic components still remains limited. In particular, most existing analyses of Adam show the convergence rate that can be simply achieved by non-adative algorithms like SGD. In this work, we provide a different perspecti... |
https://proceedings.mlr.press/v235/ai24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/ai24a/ai24a.pdf | https://openreview.net/forum?id=1v1oFF3aw0 | Not all distributional shifts are equal: Fine-grained robust conformal inference | https://proceedings.mlr.press/v235/ai24a.html | Jiahao Ai, Zhimei Ren | https://proceedings.mlr.press/v235/ai24a.html | ICML 2024 | We introduce a fine-grained framework for uncertainty quantification of predictive models under distributional shifts. This framework distinguishes the shift in covariate distributions from that in the conditional relationship between the outcome ($Y$) and the covariates ($X$). We propose to reweight the training sampl... |
https://proceedings.mlr.press/v235/akbari24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/akbari24a/akbari24a.pdf | https://openreview.net/forum?id=yzNEkTmcoF | Triple Changes Estimator for Targeted Policies | https://proceedings.mlr.press/v235/akbari24a.html | Sina Akbari, Negar Kiyavash | https://proceedings.mlr.press/v235/akbari24a.html | ICML 2024 | The renowned difference-in-differences (DiD) estimator relies on the assumption of ’parallel trends,’ which may not hold in many practical applications. To address this issue, economists are increasingly considering the triple difference estimator as a more credible alternative. Both DiD and triple difference are limit... |
https://proceedings.mlr.press/v235/akbarian24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/akbarian24a/akbarian24a.pdf | https://openreview.net/forum?id=KwgAThfxEd | Improving Computational Complexity in Statistical Models with Local Curvature Information | https://proceedings.mlr.press/v235/akbarian24a.html | Pedram Akbarian, Tongzheng Ren, Jiacheng Zhuo, Sujay Sanghavi, Nhat Ho | https://proceedings.mlr.press/v235/akbarian24a.html | ICML 2024 | It is known that when the statistical models are singular, i.e., the Fisher information matrix at the true parameter is degenerate, the fixed step-size gradient descent algorithm takes polynomial number of steps in terms of the sample size $n$ to converge to a final statistical radius around the true parameter, which c... |
https://proceedings.mlr.press/v235/akeweje24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/akeweje24a/akeweje24a.pdf | https://openreview.net/forum?id=J5Yg7HMy39 | Learning Mixtures of Gaussian Processes through Random Projection | https://proceedings.mlr.press/v235/akeweje24a.html | Emmanuel Akeweje, Mimi Zhang | https://proceedings.mlr.press/v235/akeweje24a.html | ICML 2024 | We propose an ensemble clustering framework to uncover latent cluster labels in functional data generated from a Gaussian process mixture. Our method exploits the fact that the projection coefficients of the functional data onto any given projection function follow a univariate Gaussian mixture model (GMM). By conducti... |
https://proceedings.mlr.press/v235/akhauri24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/akhauri24a/akhauri24a.pdf | https://openreview.net/forum?id=fqPH6ejwGi | Encodings for Prediction-based Neural Architecture Search | https://proceedings.mlr.press/v235/akhauri24a.html | Yash Akhauri, Mohamed S Abdelfattah | https://proceedings.mlr.press/v235/akhauri24a.html | ICML 2024 | Predictor-based methods have substantially enhanced Neural Architecture Search (NAS) optimization. The efficacy of these predictors is largely influenced by the method of encoding neural network architectures. While traditional encodings used an adjacency matrix describing the graph structure of a neural network, novel... |
https://proceedings.mlr.press/v235/akhound-sadegh24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/akhound-sadegh24a/akhound-sadegh24a.pdf | https://openreview.net/forum?id=gVjMwLDFoQ | Iterated Denoising Energy Matching for Sampling from Boltzmann Densities | https://proceedings.mlr.press/v235/akhound-sadegh24a.html | Tara Akhound-Sadegh, Jarrid Rector-Brooks, Joey Bose, Sarthak Mittal, Pablo Lemos, Cheng-Hao Liu, Marcin Sendera, Siamak Ravanbakhsh, Gauthier Gidel, Yoshua Bengio, Nikolay Malkin, Alexander Tong | https://proceedings.mlr.press/v235/akhound-sadegh24a.html | ICML 2024 | Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-body systems, is a foundational problem in science. In this paper, we propose Iterated Denoising Energy Matching (iDEM), an iterative algorithm that uses a novel stochastic score m... |
https://proceedings.mlr.press/v235/akyurek24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/akyurek24a/akyurek24a.pdf | https://openreview.net/forum?id=3Z9CRr5srL | In-Context Language Learning: Architectures and Algorithms | https://proceedings.mlr.press/v235/akyurek24a.html | Ekin Akyürek, Bailin Wang, Yoon Kim, Jacob Andreas | https://proceedings.mlr.press/v235/akyurek24a.html | ICML 2024 | Some neural language models (LMs) exhibit a remarkable capacity for in-context learning (ICL): they can fit predictors to datasets provided as input. While the mechanisms underlying ICL are well-studied in the context of synthetic problems like in-context linear regression, there is still some divergence between these ... |
https://proceedings.mlr.press/v235/al-jarrah24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/al-jarrah24a/al-jarrah24a.pdf | https://openreview.net/forum?id=blzDxD6bKt | Nonlinear Filtering with Brenier Optimal Transport Maps | https://proceedings.mlr.press/v235/al-jarrah24a.html | Mohammad Al-Jarrah, Niyizhen Jin, Bamdad Hosseini, Amirhossein Taghvaei | https://proceedings.mlr.press/v235/al-jarrah24a.html | ICML 2024 | This paper is concerned with the problem of nonlinear filtering, i.e., computing the conditional distribution of the state of a stochastic dynamical system given a history of noisy partial observations. Conventional sequential importance resampling (SIR) particle filters suffer from fundamental limitations, in scenario... |
https://proceedings.mlr.press/v235/alacaoglu24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/alacaoglu24a/alacaoglu24a.pdf | https://openreview.net/forum?id=lWy2lCTyJa | Revisiting Inexact Fixed-Point Iterations for Min-Max Problems: Stochasticity and Structured Nonconvexity | https://proceedings.mlr.press/v235/alacaoglu24a.html | Ahmet Alacaoglu, Donghwan Kim, Stephen Wright | https://proceedings.mlr.press/v235/alacaoglu24a.html | ICML 2024 | We focus on constrained, $L$-smooth, potentially stochastic and nonconvex-nonconcave min-max problems either satisfying $\rho$-cohypomonotonicity or admitting a solution to the $\rho$-weakly Minty Variational Inequality (MVI), where larger values of the parameter $\rho>0$ correspond to a greater degree of nonconvexity.... |
https://proceedings.mlr.press/v235/alain24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/alain24a/alain24a.pdf | https://openreview.net/forum?id=afnyJfQddk | Gaussian Processes on Cellular Complexes | https://proceedings.mlr.press/v235/alain24a.html | Mathieu Alain, So Takao, Brooks Paige, Marc Peter Deisenroth | https://proceedings.mlr.press/v235/alain24a.html | ICML 2024 | In recent years, there has been considerable interest in developing machine learning models on graphs to account for topological inductive biases. In particular, recent attention has been given to Gaussian processes on such structures since they can additionally account for uncertainty. However, graphs are limited to m... |
https://proceedings.mlr.press/v235/alamdari24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/alamdari24a/alamdari24a.pdf | https://openreview.net/forum?id=4BIOZSz7zU | Remembering to Be Fair: Non-Markovian Fairness in Sequential Decision Making | https://proceedings.mlr.press/v235/alamdari24a.html | Parand A. Alamdari, Toryn Q. Klassen, Elliot Creager, Sheila A. Mcilraith | https://proceedings.mlr.press/v235/alamdari24a.html | ICML 2024 | Fair decision making has largely been studied with respect to a single decision. Here we investigate the notion of fairness in the context of sequential decision making where multiple stakeholders can be affected by the outcomes of decisions. We observe that fairness often depends on the history of the sequential decis... |
https://proceedings.mlr.press/v235/albergo24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/albergo24a/albergo24a.pdf | https://openreview.net/forum?id=FFILRGD0jG | Stochastic Interpolants with Data-Dependent Couplings | https://proceedings.mlr.press/v235/albergo24a.html | Michael Samuel Albergo, Mark Goldstein, Nicholas Matthew Boffi, Rajesh Ranganath, Eric Vanden-Eijnden | https://proceedings.mlr.press/v235/albergo24a.html | ICML 2024 | Generative models inspired by dynamical transport of measure – such as flows and diffusions – construct a continuous-time map between two probability densities. Conventionally, one of these is the target density, only accessible through samples, while the other is taken as a simple base density that is data-agnostic. I... |
https://proceedings.mlr.press/v235/albuquerque24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/albuquerque24a/albuquerque24a.pdf | https://openreview.net/forum?id=idyUNsoZ75 | Evaluating Model Bias Requires Characterizing its Mistakes | https://proceedings.mlr.press/v235/albuquerque24a.html | Isabela Albuquerque, Jessica Schrouff, David Warde-Farley, Ali Taylan Cemgil, Sven Gowal, Olivia Wiles | https://proceedings.mlr.press/v235/albuquerque24a.html | ICML 2024 | The ability to properly benchmark model performance in the face of spurious correlations is important to both build better predictors and increase confidence that models are operating as intended. We demonstrate that characterizing (as opposed to simply quantifying) model mistakes across subgroups is pivotal to properl... |
https://proceedings.mlr.press/v235/alder24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/alder24a/alder24a.pdf | https://openreview.net/forum?id=v9tIJW1fzt | Energy-Efficient Gaussian Processes Using Low-Precision Arithmetic | https://proceedings.mlr.press/v235/alder24a.html | Nicolas Alder, Ralf Herbrich | https://proceedings.mlr.press/v235/alder24a.html | ICML 2024 | The widespread use of artificial intelligence requires finding energy-efficient paradigms for the field. We propose to reduce the energy consumption of Gaussian process regression using low-precision floating-point representations. We explore how low-precision representations impact the results of Gaussian process regr... |
https://proceedings.mlr.press/v235/alfarra24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/alfarra24a/alfarra24a.pdf | https://openreview.net/forum?id=6FtAXU4ean | Evaluation of Test-Time Adaptation Under Computational Time Constraints | https://proceedings.mlr.press/v235/alfarra24a.html | Motasem Alfarra, Hani Itani, Alejandro Pardo, Shyma Yaser Alhuwaider, Merey Ramazanova, Juan Camilo Perez, Zhipeng Cai, Matthias Müller, Bernard Ghanem | https://proceedings.mlr.press/v235/alfarra24a.html | ICML 2024 | This paper proposes a novel online evaluation protocol for Test Time Adaptation (TTA) methods, which penalizes slower methods by providing them with fewer samples for adaptation. TTA methods leverage unlabeled data at test time to adapt to distribution shifts. Though many effective methods have been proposed, their imp... |
https://proceedings.mlr.press/v235/ali-mehmeti-gopel24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/ali-mehmeti-gopel24a/ali-mehmeti-gopel24a.pdf | https://openreview.net/forum?id=AzUCfhJ9Bs | On the Weight Dynamics of Deep Normalized Networks | https://proceedings.mlr.press/v235/ali-mehmeti-gopel24a.html | Christian H.X. Ali Mehmeti-Göpel, Michael Wand | https://proceedings.mlr.press/v235/ali-mehmeti-gopel24a.html | ICML 2024 | Recent studies have shown that high disparities in effective learning rates (ELRs) across layers in deep neural networks can negatively affect trainability. We formalize how these disparities evolve over time by modeling weight dynamics (evolution of expected gradient and weight norms) of networks with normalization la... |
https://proceedings.mlr.press/v235/alishahi24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/alishahi24a/alishahi24a.pdf | https://openreview.net/forum?id=jS3CMHtYJD | No Dimensional Sampling Coresets for Classification | https://proceedings.mlr.press/v235/alishahi24a.html | Meysam Alishahi, Jeff M. Phillips | https://proceedings.mlr.press/v235/alishahi24a.html | ICML 2024 | We refine and generalize what is known about coresets for classification problems via the sensitivity sampling framework. Such coresets seek the smallest possible subsets of input data, so one can optimize a loss function on the coreset and ensure approximation guarantees with respect to the original data. Our analysis... |
https://proceedings.mlr.press/v235/allamanis24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/allamanis24a/allamanis24a.pdf | https://openreview.net/forum?id=YnFuUX08CE | Unsupervised Evaluation of Code LLMs with Round-Trip Correctness | https://proceedings.mlr.press/v235/allamanis24a.html | Miltiadis Allamanis, Sheena Panthaplackel, Pengcheng Yin | https://proceedings.mlr.press/v235/allamanis24a.html | ICML 2024 | To evaluate code large language models (LLMs), research has relied on a few small manually curated benchmarks, such as HumanEval and MBPP, which represent a narrow part of the real-world software domains. In this work, we introduce round-trip correctness (RTC) as an alternative evaluation method. RTC allows Code LLM ev... |
https://proceedings.mlr.press/v235/allen-zhu24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/allen-zhu24a/allen-zhu24a.pdf | https://openreview.net/forum?id=5x788rqbcj | Physics of Language Models: Part 3.1, Knowledge Storage and Extraction | https://proceedings.mlr.press/v235/allen-zhu24a.html | Zeyuan Allen-Zhu, Yuanzhi Li | https://proceedings.mlr.press/v235/allen-zhu24a.html | ICML 2024 | Large language models (LLMs) can store a vast amount of world knowledge, often extractable via question-answering (e.g., "What is Abraham Lincoln’s birthday?”). However, do they answer such questions based on exposure to similar questions during training (i.e., cheating), or by genuinely learning to extract knowledge f... |
https://proceedings.mlr.press/v235/allouah24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/allouah24a/allouah24a.pdf | https://openreview.net/forum?id=Izv7gBnap3 | Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates | https://proceedings.mlr.press/v235/allouah24a.html | Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, Geovani Rizk, Sasha Voitovych | https://proceedings.mlr.press/v235/allouah24a.html | ICML 2024 | The possibility of adversarial (a.k.a., Byzantine) clients makes federated learning (FL) prone to arbitrary manipulation. The natural approach to robustify FL against adversarial clients is to replace the simple averaging operation at the server in the standard $\mathsf{FedAvg}$ algorithm by a robust averaging rule. Wh... |
https://proceedings.mlr.press/v235/allouah24b.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/allouah24b/allouah24b.pdf | https://openreview.net/forum?id=5JrlywYHRi | The Privacy Power of Correlated Noise in Decentralized Learning | https://proceedings.mlr.press/v235/allouah24b.html | Youssef Allouah, Anastasia Koloskova, Aymane El Firdoussi, Martin Jaggi, Rachid Guerraoui | https://proceedings.mlr.press/v235/allouah24b.html | ICML 2024 | Decentralized learning is appealing as it enables the scalable usage of large amounts of distributed data and resources without resorting to any central entity, while promoting privacy since every user minimizes the direct exposure of their data. Yet, without additional precautions, curious users can still leverage mod... |
https://proceedings.mlr.press/v235/alonso-campana24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/alonso-campana24a/alonso-campana24a.pdf | https://openreview.net/forum?id=MDAg5Q7IsI | Predicting Dose-Response Curves with Deep Neural Networks | https://proceedings.mlr.press/v235/alonso-campana24a.html | Pedro Alonso Campana, Paul Prasse, Tobias Scheffer | https://proceedings.mlr.press/v235/alonso-campana24a.html | ICML 2024 | Dose-response curves characterize the relationship between the concentration of drugs and their inhibitory effect on the growth of specific types of cells. The predominant Hill-equation model of an ideal enzymatic inhibition unduly simplifies the biochemical reality of many drugs; and for these drugs the widely-used dr... |
https://proceedings.mlr.press/v235/altamirano24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/altamirano24a/altamirano24a.pdf | https://openreview.net/forum?id=5WnKLIAX4q | Robust and Conjugate Gaussian Process Regression | https://proceedings.mlr.press/v235/altamirano24a.html | Matias Altamirano, Francois-Xavier Briol, Jeremias Knoblauch | https://proceedings.mlr.press/v235/altamirano24a.html | ICML 2024 | To enable closed form conditioning, a common assumption in Gaussian process (GP) regression is independent and identically distributed Gaussian observation noise. This strong and simplistic assumption is often violated in practice, which leads to unreliable inferences and uncertainty quantification. Unfortunately, exis... |
https://proceedings.mlr.press/v235/altieri24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/altieri24a/altieri24a.pdf | https://openreview.net/forum?id=YqIIhl2ToH | Beyond the Norms: Detecting Prediction Errors in Regression Models | https://proceedings.mlr.press/v235/altieri24a.html | Andres Altieri, Marco Romanelli, Georg Pichler, Florence Alberge, Pablo Piantanida | https://proceedings.mlr.press/v235/altieri24a.html | ICML 2024 | This paper tackles the challenge of detecting unreliable behavior in regression algorithms, which may arise from intrinsic variability (e.g., aleatoric uncertainty) or modeling errors (e.g., model uncertainty). First, we formally introduce the notion of unreliability in regression, i.e., when the output of the regresso... |
https://proceedings.mlr.press/v235/altmeyer24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/altmeyer24a/altmeyer24a.pdf | https://openreview.net/forum?id=AIXUuLCuMe | Position: Stop Making Unscientific AGI Performance Claims | https://proceedings.mlr.press/v235/altmeyer24a.html | Patrick Altmeyer, Andrew M. Demetriou, Antony Bartlett, Cynthia C. S. Liem | https://proceedings.mlr.press/v235/altmeyer24a.html | ICML 2024 | Developments in the field of Artificial Intelligence (AI), and particularly large language models (LLMs), have created a ’perfect storm’ for observing ’sparks’ of Artificial General Intelligence (AGI) that are spurious. Like simpler models, LLMs distill meaningful representations in their latent embeddings that have be... |
https://proceedings.mlr.press/v235/alvarado24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/alvarado24a/alvarado24a.pdf | https://openreview.net/forum?id=kZKopcDp2q | Hyperbolic Optimizer as a Dynamical System | https://proceedings.mlr.press/v235/alvarado24a.html | Nico Alvarado, Hans Lobel | https://proceedings.mlr.press/v235/alvarado24a.html | ICML 2024 | During the last few years, the field of dynamical systems has been developing innovative tools to study the asymptotic behavior of different optimizers in the context of neural networks. In this work, we redefine an extensively studied optimizer, employing classical techniques from hyperbolic geometry. This new definit... |
https://proceedings.mlr.press/v235/ambrogioni24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/ambrogioni24a/ambrogioni24a.pdf | https://openreview.net/forum?id=6CV1N7hhpA | Stationarity without mean reversion in improper Gaussian processes | https://proceedings.mlr.press/v235/ambrogioni24a.html | Luca Ambrogioni | https://proceedings.mlr.press/v235/ambrogioni24a.html | ICML 2024 | The behavior of a GP regression depends on the choice of covariance function. Stationary covariance functions are preferred in machine learning applications. However, (non-periodic) stationary covariance functions are always mean reverting and can therefore exhibit pathological behavior when applied to data that does n... |
https://proceedings.mlr.press/v235/ameen24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/ameen24a/ameen24a.pdf | https://openreview.net/forum?id=WJn1BAx9aj | Robust Graph Matching when Nodes are Corrupt | https://proceedings.mlr.press/v235/ameen24a.html | Taha Ameen, Bruce Hajek | https://proceedings.mlr.press/v235/ameen24a.html | ICML 2024 | Two models are introduced to study the problem of matching two correlated graphs when some of the nodes are corrupt. In the weak model, a random subset of nodes in one or both graphs can interact randomly with their network. For this model, it is shown that no estimator can correctly recover a positive fraction of the ... |
https://proceedings.mlr.press/v235/ameranis24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/ameranis24a/ameranis24a.pdf | https://openreview.net/forum?id=sfQH4JJ4We | Fast Algorithms for Hypergraph PageRank with Applications to Semi-Supervised Learning | https://proceedings.mlr.press/v235/ameranis24a.html | Konstantinos Ameranis, Adela Frances Depavia, Lorenzo Orecchia, Erasmo Tani | https://proceedings.mlr.press/v235/ameranis24a.html | ICML 2024 | A fundamental approach to semi-supervised learning is to leverage the structure of the sample space to diffuse label information from annotated examples to unlabeled points. Traditional methods model the input data points as a graph and rely on fast algorithms for solving Laplacian systems of equations, such as those d... |
https://proceedings.mlr.press/v235/amin24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/amin24a/amin24a.pdf | https://openreview.net/forum?id=5M4Qa9AqY7 | Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency | https://proceedings.mlr.press/v235/amin24a.html | Alan Nawzad Amin, Andrew Gordon Wilson | https://proceedings.mlr.press/v235/amin24a.html | ICML 2024 | To make accurate predictions, understand mechanisms, and design interventions in systems of many variables, we wish to learn causal graphs from large scale data. Unfortunately the space of all possible causal graphs is enormous so scalably and accurately searching for the best fit to the data is a challenge. In princip... |
https://proceedings.mlr.press/v235/aminian24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/aminian24a/aminian24a.pdf | https://openreview.net/forum?id=8h0x12p3zq | Generalization Error of Graph Neural Networks in the Mean-field Regime | https://proceedings.mlr.press/v235/aminian24a.html | Gholamali Aminian, Yixuan He, Gesine Reinert, Lukasz Szpruch, Samuel N. Cohen | https://proceedings.mlr.press/v235/aminian24a.html | ICML 2024 | This work provides a theoretical framework for assessing the generalization error of graph neural networks in the over-parameterized regime, where the number of parameters surpasses the quantity of data points. We explore two widely utilized types of graph neural networks: graph convolutional neural networks and messag... |
https://proceedings.mlr.press/v235/amortila24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/amortila24a/amortila24a.pdf | https://openreview.net/forum?id=C64clssMVU | Scalable Online Exploration via Coverability | https://proceedings.mlr.press/v235/amortila24a.html | Philip Amortila, Dylan J Foster, Akshay Krishnamurthy | https://proceedings.mlr.press/v235/amortila24a.html | ICML 2024 | Exploration is a major challenge in reinforcement learning, especially for high-dimensional domains that require function approximation. We propose exploration objectives—policy optimization objectives that enable downstream maximization of any reward function—as a conceptual framework to systematize the study of explo... |
https://proceedings.mlr.press/v235/an24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/an24a/an24a.pdf | https://openreview.net/forum?id=URtUYfC3GA | WAVES: Benchmarking the Robustness of Image Watermarks | https://proceedings.mlr.press/v235/an24a.html | Bang An, Mucong Ding, Tahseen Rabbani, Aakriti Agrawal, Yuancheng Xu, Chenghao Deng, Sicheng Zhu, Abdirisak Mohamed, Yuxin Wen, Tom Goldstein, Furong Huang | https://proceedings.mlr.press/v235/an24a.html | ICML 2024 | In the burgeoning age of generative AI, watermarks act as identifiers of provenance and artificial content. We present WAVES (Watermark Analysis via Enhanced Stress-testing), a benchmark for assessing image watermark robustness, overcoming the limitations of current evaluation methods. WAVES integrates detection and id... |
https://proceedings.mlr.press/v235/an24b.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/an24b/an24b.pdf | https://openreview.net/forum?id=If4xW9vF7U | Training-Free Long-Context Scaling of Large Language Models | https://proceedings.mlr.press/v235/an24b.html | Chenxin An, Fei Huang, Jun Zhang, Shansan Gong, Xipeng Qiu, Chang Zhou, Lingpeng Kong | https://proceedings.mlr.press/v235/an24b.html | ICML 2024 | The ability of Large Language Models (LLMs) to process and generate coherent text is markedly weakened when the number of input tokens exceeds their pretraining length. Given the expensive overhead of finetuning large-scale models with longer sequences, we propose a training-free approach named Dual Chunk Attention (DC... |
https://proceedings.mlr.press/v235/anagnostidis24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/anagnostidis24a/anagnostidis24a.pdf | https://openreview.net/forum?id=3KxPo62PYn | Navigating Scaling Laws: Compute Optimality in Adaptive Model Training | https://proceedings.mlr.press/v235/anagnostidis24a.html | Sotiris Anagnostidis, Gregor Bachmann, Imanol Schlag, Thomas Hofmann | https://proceedings.mlr.press/v235/anagnostidis24a.html | ICML 2024 | In recent years, the state-of-the-art in deep learning has been dominated by very large models that have been pre-trained on vast amounts of data. The paradigm is very simple: investing more computational resources (optimally) leads to better performance, and even predictably so; neural scaling laws have been derived t... |
https://proceedings.mlr.press/v235/anani24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/anani24a/anani24a.pdf | https://openreview.net/forum?id=iOEReiiTit | Adaptive Hierarchical Certification for Segmentation using Randomized Smoothing | https://proceedings.mlr.press/v235/anani24a.html | Alaa Anani, Tobias Lorenz, Bernt Schiele, Mario Fritz | https://proceedings.mlr.press/v235/anani24a.html | ICML 2024 | Certification for machine learning is proving that no adversarial sample can evade a model within a range under certain conditions, a necessity for safety-critical domains. Common certification methods for segmentation use a flat set of fine-grained classes, leading to high abstain rates due to model uncertainty across... |
https://proceedings.mlr.press/v235/anders24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/anders24a/anders24a.pdf | https://openreview.net/forum?id=dSrdnhLS2h | Adaptive Observation Cost Control for Variational Quantum Eigensolvers | https://proceedings.mlr.press/v235/anders24a.html | Christopher J. Anders, Kim Andrea Nicoli, Bingting Wu, Naima Elosegui, Samuele Pedrielli, Lena Funcke, Karl Jansen, Stefan Kühn, Shinichi Nakajima | https://proceedings.mlr.press/v235/anders24a.html | ICML 2024 | The objective to be minimized in the variational quantum eigensolver (VQE) has a restricted form, which allows a specialized sequential minimal optimization (SMO) that requires only a few observations in each iteration. However, the SMO iteration is still costly due to the observation noise—one observation at a point t... |
https://proceedings.mlr.press/v235/angell24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/angell24a/angell24a.pdf | https://openreview.net/forum?id=gqA8ZHO0j8 | Fast, Scalable, Warm-Start Semidefinite Programming with Spectral Bundling and Sketching | https://proceedings.mlr.press/v235/angell24a.html | Rico Angell, Andrew Mccallum | https://proceedings.mlr.press/v235/angell24a.html | ICML 2024 | While semidefinite programming (SDP) has traditionally been limited to moderate-sized problems, recent algorithms augmented with matrix sketching techniques have enabled solving larger SDPs. However, these methods achieve scalability at the cost of an increase in the number of necessary iterations, resulting in slower ... |
https://proceedings.mlr.press/v235/angelopoulos24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/angelopoulos24a/angelopoulos24a.pdf | https://openreview.net/forum?id=2XkRIijUKw | Online conformal prediction with decaying step sizes | https://proceedings.mlr.press/v235/angelopoulos24a.html | Anastasios Nikolas Angelopoulos, Rina Barber, Stephen Bates | https://proceedings.mlr.press/v235/angelopoulos24a.html | ICML 2024 | We introduce a method for online conformal prediction with decaying step sizes. Like previous methods, ours possesses a retrospective guarantee of coverage for arbitrary sequences. However, unlike previous methods, we can simultaneously estimate a population quantile when it exists. Our theory and experiments indicate ... |
https://proceedings.mlr.press/v235/apostolopoulou24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/apostolopoulou24a/apostolopoulou24a.pdf | https://openreview.net/forum?id=zMGUDsPopK | A Rate-Distortion View of Uncertainty Quantification | https://proceedings.mlr.press/v235/apostolopoulou24a.html | Ifigeneia Apostolopoulou, Benjamin Eysenbach, Frank Nielsen, Artur Dubrawski | https://proceedings.mlr.press/v235/apostolopoulou24a.html | ICML 2024 | In supervised learning, understanding an input’s proximity to the training data can help a model decide whether it has sufficient evidence for reaching a reliable prediction. While powerful probabilistic models such as Gaussian Processes naturally have this property, deep neural networks often lack it. In this paper, w... |
https://proceedings.mlr.press/v235/archer24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/archer24a/archer24a.pdf | https://openreview.net/forum?id=S3xqyEaST9 | Practical Performance Guarantees for Pipelined DNN Inference | https://proceedings.mlr.press/v235/archer24a.html | Aaron Archer, Matthew Fahrbach, Kuikui Liu, Prakash Prabhu | https://proceedings.mlr.press/v235/archer24a.html | ICML 2024 | We optimize pipeline parallelism for deep neural network (DNN) inference by partitioning model graphs into $k$ stages and minimizing the running time of the bottleneck stage, including communication. We give practical and effective algorithms for this NP-hard problem, but our emphasis is on tackling the practitioner’s ... |
https://proceedings.mlr.press/v235/arefin24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/arefin24a/arefin24a.pdf | https://openreview.net/forum?id=lQzmDFlsHX | Unsupervised Concept Discovery Mitigates Spurious Correlations | https://proceedings.mlr.press/v235/arefin24a.html | Md Rifat Arefin, Yan Zhang, Aristide Baratin, Francesco Locatello, Irina Rish, Dianbo Liu, Kenji Kawaguchi | https://proceedings.mlr.press/v235/arefin24a.html | ICML 2024 | Models prone to spurious correlations in training data often produce brittle predictions and introduce unintended biases. Addressing this challenge typically involves methods relying on prior knowledge and group annotation to remove spurious correlations, which may not be readily available in many applications. In this... |
https://proceedings.mlr.press/v235/arisaka24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/arisaka24a/arisaka24a.pdf | https://openreview.net/forum?id=yh6Y7ppf46 | Accelerating Legacy Numerical Solvers by Non-intrusive Gradient-based Meta-solving | https://proceedings.mlr.press/v235/arisaka24a.html | Sohei Arisaka, Qianxiao Li | https://proceedings.mlr.press/v235/arisaka24a.html | ICML 2024 | Scientific computing is an essential tool for scientific discovery and engineering design, and its computational cost is always a main concern in practice. To accelerate scientific computing, it is a promising approach to use machine learning (especially meta-learning) techniques for selecting hyperparameters of tradit... |
https://proceedings.mlr.press/v235/armengol-urpi-24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/armengol-urpi-24a/armengol-urpi-24a.pdf | https://openreview.net/forum?id=6Zl9rv6PDx | Causal Action Influence Aware Counterfactual Data Augmentation | https://proceedings.mlr.press/v235/armengol-urpi-24a.html | Núria Armengol Urpı́, Marco Bagatella, Marin Vlastelica, Georg Martius | https://proceedings.mlr.press/v235/armengol-urpi-24a.html | ICML 2024 | Offline data are both valuable and practical resources for teaching robots complex behaviors. Ideally, learning agents should not be constrained by the scarcity of available demonstrations, but rather generalize beyond the training distribution. However, the complexity of real-world scenarios typically requires huge am... |
https://proceedings.mlr.press/v235/arnaboldi24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/arnaboldi24a/arnaboldi24a.pdf | https://openreview.net/forum?id=ZSQAf5YlvN | Online Learning and Information Exponents: The Importance of Batch size & Time/Complexity Tradeoffs | https://proceedings.mlr.press/v235/arnaboldi24a.html | Luca Arnaboldi, Yatin Dandi, Florent Krzakala, Bruno Loureiro, Luca Pesce, Ludovic Stephan | https://proceedings.mlr.press/v235/arnaboldi24a.html | ICML 2024 | We study the impact of the batch size $n_b$ on the iteration time $T$ of training two-layer neural networks with one-pass stochastic gradient descent (SGD) on multi-index target functions of isotropic covariates. We characterize the optimal batch size minimizing the iteration time as a function of the hardness of the t... |
https://proceedings.mlr.press/v235/arora24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/arora24a/arora24a.pdf | https://openreview.net/forum?id=e93ffDcpH3 | Simple linear attention language models balance the recall-throughput tradeoff | https://proceedings.mlr.press/v235/arora24a.html | Simran Arora, Sabri Eyuboglu, Michael Zhang, Aman Timalsina, Silas Alberti, James Zou, Atri Rudra, Christopher Re | https://proceedings.mlr.press/v235/arora24a.html | ICML 2024 | Recent work has shown that attention-based language models excel at "recall", the ability to ground generations in tokens previously seen in context. However, the efficiency of attention-based models is bottle-necked during inference by the KV-cache’s aggressive memory consumption. In this work, we explore whether we c... |
https://proceedings.mlr.press/v235/arpino24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/arpino24a/arpino24a.pdf | https://openreview.net/forum?id=1JgCpZS17T | Inferring Change Points in High-Dimensional Linear Regression via Approximate Message Passing | https://proceedings.mlr.press/v235/arpino24a.html | Gabriel Arpino, Xiaoqi Liu, Ramji Venkataramanan | https://proceedings.mlr.press/v235/arpino24a.html | ICML 2024 | We consider the problem of localizing change points in high-dimensional linear regression. We propose an Approximate Message Passing (AMP) algorithm for estimating both the signals and the change point locations. Assuming Gaussian covariates, we give an exact asymptotic characterization of its estimation performance in... |
https://proceedings.mlr.press/v235/arruda24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/arruda24a/arruda24a.pdf | https://openreview.net/forum?id=uCdcXRuHnC | An amortized approach to non-linear mixed-effects modeling based on neural posterior estimation | https://proceedings.mlr.press/v235/arruda24a.html | Jonas Arruda, Yannik Schälte, Clemens Peiter, Olga Teplytska, Ulrich Jaehde, Jan Hasenauer | https://proceedings.mlr.press/v235/arruda24a.html | ICML 2024 | Non-linear mixed-effects models are a powerful tool for studying heterogeneous populations in various fields, including biology, medicine, economics, and engineering. Here, the aim is to find a distribution over the parameters that describe the whole population using a model that can generate simulations for an individ... |
https://proceedings.mlr.press/v235/asadi24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/asadi24a/asadi24a.pdf | https://openreview.net/forum?id=jP1zeEqHli | Learning the Target Network in Function Space | https://proceedings.mlr.press/v235/asadi24a.html | Kavosh Asadi, Yao Liu, Shoham Sabach, Ming Yin, Rasool Fakoor | https://proceedings.mlr.press/v235/asadi24a.html | ICML 2024 | We focus on the task of learning the value function in the reinforcement learning (RL) setting. This task is often solved by updating a pair of online and target networks while ensuring that the parameters of these two networks are equivalent. We propose Lookahead-Replicate (LR), a new value-function approximation algo... |
https://proceedings.mlr.press/v235/ashman24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/ashman24a/ashman24a.pdf | https://openreview.net/forum?id=pftXzp6Yn3 | Translation Equivariant Transformer Neural Processes | https://proceedings.mlr.press/v235/ashman24a.html | Matthew Ashman, Cristiana Diaconu, Junhyuck Kim, Lakee Sivaraya, Stratis Markou, James Requeima, Wessel P Bruinsma, Richard E. Turner | https://proceedings.mlr.press/v235/ashman24a.html | ICML 2024 | The effectiveness of neural processes (NPs) in modelling posterior prediction maps—the mapping from data to posterior predictive distributions—has significantly improved since their inception. This improvement can be attributed to two principal factors: (1) advancements in the architecture of permutation invariant set ... |
https://proceedings.mlr.press/v235/asi24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/asi24a/asi24a.pdf | https://openreview.net/forum?id=PTGJOUlQ68 | Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages | https://proceedings.mlr.press/v235/asi24a.html | Hilal Asi, Vitaly Feldman, Jelani Nelson, Huy Nguyen, Kunal Talwar, Samson Zhou | https://proceedings.mlr.press/v235/asi24a.html | ICML 2024 | We study the problem of private vector mean estimation in the shuffle model of privacy where $n$ users each have a unit vector $v^{(i)} \in \mathbb{R}^d$. We propose a new multi-message protocol that achieves the optimal error using $O(\min(n\varepsilon^2,d))$ messages per user. Moreover, we show that any (unbiased) pr... |
https://proceedings.mlr.press/v235/athiwaratkun24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/athiwaratkun24a/athiwaratkun24a.pdf | https://openreview.net/forum?id=JPNBFWQ9H2 | Bifurcated Attention for Single-Context Large-Batch Sampling | https://proceedings.mlr.press/v235/athiwaratkun24a.html | Ben Athiwaratkun, Sujan Kumar Gonugondla, Sanjay Krishna Gouda, Haifeng Qian, Hantian Ding, Qing Sun, Jun Wang, Jiacheng Guo, Liangfu Chen, Parminder Bhatia, Ramesh Nallapati, Sudipta Sengupta, Bing Xiang | https://proceedings.mlr.press/v235/athiwaratkun24a.html | ICML 2024 | In our study, we present bifurcated attention, a method developed for language model inference in single-context batch sampling contexts. This approach aims to reduce redundant memory IO costs, a significant factor in latency for high batch sizes and long context lengths. Bifurcated attention achieves this by dividing ... |
https://proceedings.mlr.press/v235/attali24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/attali24a/attali24a.pdf | https://openreview.net/forum?id=uyhjKoaIQa | Delaunay Graph: Addressing Over-Squashing and Over-Smoothing Using Delaunay Triangulation | https://proceedings.mlr.press/v235/attali24a.html | Hugo Attali, Davide Buscaldi, Nathalie Pernelle | https://proceedings.mlr.press/v235/attali24a.html | ICML 2024 | GNNs rely on the exchange of messages to distribute information along the edges of the graph. This approach makes the efficiency of architectures highly dependent on the specific structure of the input graph. Certain graph topologies lead to inefficient information propagation, resulting in a phenomenon known as over-s... |
https://proceedings.mlr.press/v235/attia24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/attia24a/attia24a.pdf | https://openreview.net/forum?id=6L4K5jmSJq | How Free is Parameter-Free Stochastic Optimization? | https://proceedings.mlr.press/v235/attia24a.html | Amit Attia, Tomer Koren | https://proceedings.mlr.press/v235/attia24a.html | ICML 2024 | We study the problem of parameter-free stochastic optimization, inquiring whether, and under what conditions, do fully parameter-free methods exist: these are methods that achieve convergence rates competitive with optimally tuned methods, without requiring significant knowledge of the true problem parameters. Existing... |
https://proceedings.mlr.press/v235/attias24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/attias24a/attias24a.pdf | https://openreview.net/forum?id=CyEJn71Z00 | Information Complexity of Stochastic Convex Optimization: Applications to Generalization, Memorization, and Tracing | https://proceedings.mlr.press/v235/attias24a.html | Idan Attias, Gintare Karolina Dziugaite, Mahdi Haghifam, Roi Livni, Daniel M. Roy | https://proceedings.mlr.press/v235/attias24a.html | ICML 2024 | In this work, we investigate the interplay between memorization and learning in the context of stochastic convex optimization (SCO). We define memorization via the information a learning algorithm reveals about its training data points. We then quantify this information using the framework of conditional mutual informa... |
https://proceedings.mlr.press/v235/attias24b.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/attias24b/attias24b.pdf | https://openreview.net/forum?id=71ktaA3ihI | Agnostic Sample Compression Schemes for Regression | https://proceedings.mlr.press/v235/attias24b.html | Idan Attias, Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi | https://proceedings.mlr.press/v235/attias24b.html | ICML 2024 | We obtain the first positive results for bounded sample compression in the agnostic regression setting with the $\ell_p$ loss, where $p\in [1,\infty]$. We construct a generic approximate sample compression scheme for real-valued function classes exhibiting exponential size in the fat-shattering dimension but independen... |
https://proceedings.mlr.press/v235/axiotis24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/axiotis24a/axiotis24a.pdf | https://openreview.net/forum?id=WUQ4YzIQt2 | Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond | https://proceedings.mlr.press/v235/axiotis24a.html | Kyriakos Axiotis, Vincent Cohen-Addad, Monika Henzinger, Sammy Jerome, Vahab Mirrokni, David Saulpic, David Woodruff, Michael Wunder | https://proceedings.mlr.press/v235/axiotis24a.html | ICML 2024 | We study the data selection problem, whose aim is to select a small representative subset of data that can be used to efficiently train a machine learning model. We present a new data selection approach based on $k$-means clustering and sensitivity sampling. Assuming access to an embedding representation of the data wi... |
https://proceedings.mlr.press/v235/ayme24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/ayme24a/ayme24a.pdf | https://openreview.net/forum?id=B5g6y7JlMw | Random features models: a way to study the success of naive imputation | https://proceedings.mlr.press/v235/ayme24a.html | Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet | https://proceedings.mlr.press/v235/ayme24a.html | ICML 2024 | Constant (naive) imputation is still widely used in practice as this is a first easy-to-use technique to deal with missing data. Yet, this simple method could be expected to induce a large bias for prediction purposes, as the imputed input may strongly differ from the true underlying data. However, recent works suggest... |
https://proceedings.mlr.press/v235/ayoub24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/ayoub24a/ayoub24a.pdf | https://openreview.net/forum?id=7PXSc5fURu | Switching the Loss Reduces the Cost in Batch Reinforcement Learning | https://proceedings.mlr.press/v235/ayoub24a.html | Alex Ayoub, Kaiwen Wang, Vincent Liu, Samuel Robertson, James Mcinerney, Dawen Liang, Nathan Kallus, Csaba Szepesvari | https://proceedings.mlr.press/v235/ayoub24a.html | ICML 2024 | We propose training fitted Q-iteration with log-loss (FQI-LOG) for batch reinforcement learning (RL). We show that the number of samples needed to learn a near-optimal policy with FQI-LOG scales with the accumulated cost of the optimal policy, which is zero in problems where acting optimally achieves the goal and incur... |
https://proceedings.mlr.press/v235/azarmehr24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/azarmehr24a/azarmehr24a.pdf | https://openreview.net/forum?id=EDEISRmi6X | Bipartite Matching in Massive Graphs: A Tight Analysis of EDCS | https://proceedings.mlr.press/v235/azarmehr24a.html | Amir Azarmehr, Soheil Behnezhad, Mohammad Roghani | https://proceedings.mlr.press/v235/azarmehr24a.html | ICML 2024 | Maximum matching is one of the most fundamental combinatorial optimization problems with applications in various contexts such as balanced clustering, data mining, resource allocation, and online advertisement. In many of these applications, the input graph is massive. The sheer size of these inputs makes it impossible... |
https://proceedings.mlr.press/v235/azizian24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/azizian24a/azizian24a.pdf | https://openreview.net/forum?id=vsOF7qDNhl | What is the Long-Run Distribution of Stochastic Gradient Descent? A Large Deviations Analysis | https://proceedings.mlr.press/v235/azizian24a.html | Waı̈ss Azizian, Franck Iutzeler, Jerome Malick, Panayotis Mertikopoulos | https://proceedings.mlr.press/v235/azizian24a.html | ICML 2024 | In this paper, we examine the long-run distribution of stochastic gradient descent (SGD) in general, non-convex problems. Specifically, we seek to understand which regions of the problem’s state space are more likely to be visited by SGD, and by how much. Using an approach based on the theory of large deviations and ra... |
https://proceedings.mlr.press/v235/babu24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/babu24a/babu24a.pdf | https://openreview.net/forum?id=8STOjGCkfH | HyperFields: Towards Zero-Shot Generation of NeRFs from Text | https://proceedings.mlr.press/v235/babu24a.html | Sudarshan Babu, Richard Liu, Avery Zhou, Michael Maire, Greg Shakhnarovich, Rana Hanocka | https://proceedings.mlr.press/v235/babu24a.html | ICML 2024 | We introduce HyperFields, a method for generating text-conditioned Neural Radiance Fields (NeRFs) with a single forward pass and (optionally) some fine-tuning. Key to our approach are: (i) a dynamic hypernetwork, which learns a smooth mapping from text token embeddings to the space of NeRFs; (ii) NeRF distillation trai... |
https://proceedings.mlr.press/v235/baby24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/baby24a/baby24a.pdf | https://openreview.net/forum?id=7XZKzQtooN | Online Matrix Completion: A Collaborative Approach with Hott Items | https://proceedings.mlr.press/v235/baby24a.html | Dheeraj Baby, Soumyabrata Pal | https://proceedings.mlr.press/v235/baby24a.html | ICML 2024 | We investigate the low rank matrix completion problem in an online setting with ${M}$ users, ${N}$ items, ${T}$ rounds, and an unknown rank-$r$ reward matrix ${R}\in \mathbb{R}^{{M}\times {N}}$. This problem has been well-studied in the literature and has several applications in practice. In each round, we recommend ${... |
https://proceedings.mlr.press/v235/bacellar24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/bacellar24a/bacellar24a.pdf | https://openreview.net/forum?id=GBxflz0qdX | Differentiable Weightless Neural Networks | https://proceedings.mlr.press/v235/bacellar24a.html | Alan Tendler Leibel Bacellar, Zachary Susskind, Mauricio Breternitz Jr, Eugene John, Lizy Kurian John, Priscila Machado Vieira Lima, Felipe M.G. França | https://proceedings.mlr.press/v235/bacellar24a.html | ICML 2024 | We introduce the Differentiable Weightless Neural Network (DWN), a model based on interconnected lookup tables. Training of DWNs is enabled by a novel Extended Finite Difference technique for approximate differentiation of binary values. We propose Learnable Mapping, Learnable Reduction, and Spectral Regularization to ... |
https://proceedings.mlr.press/v235/bachmann24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/bachmann24a/bachmann24a.pdf | https://openreview.net/forum?id=76zq8Wkl6Z | The Pitfalls of Next-Token Prediction | https://proceedings.mlr.press/v235/bachmann24a.html | Gregor Bachmann, Vaishnavh Nagarajan | https://proceedings.mlr.press/v235/bachmann24a.html | ICML 2024 | Can a mere next-token predictor faithfully model human thinking? Our work is aimed at crystallizing this intuitive concern, which is currently fragmented in the literature. First, we emphasize isolating the two phases of next-token prediction that are often conflated: autoregression during inference vs. teacher-forcing... |
https://proceedings.mlr.press/v235/back-de-luca24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/back-de-luca24a/back-de-luca24a.pdf | https://openreview.net/forum?id=aA2326y3hf | Simulation of Graph Algorithms with Looped Transformers | https://proceedings.mlr.press/v235/back-de-luca24a.html | Artur Back De Luca, Kimon Fountoulakis | https://proceedings.mlr.press/v235/back-de-luca24a.html | ICML 2024 | The execution of graph algorithms using neural networks has recently attracted significant interest due to promising empirical progress. This motivates further understanding of how neural networks can replicate reasoning steps with relational data. In this work, we study the ability of transformer networks to simulate ... |
https://proceedings.mlr.press/v235/bai24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/bai24a/bai24a.pdf | https://openreview.net/forum?id=PYDCwWvbG7 | QBMK: Quantum-based Matching Kernels for Un-attributed Graphs | https://proceedings.mlr.press/v235/bai24a.html | Lu Bai, Lixin Cui, Ming Li, Yue Wang, Edwin Hancock | https://proceedings.mlr.press/v235/bai24a.html | ICML 2024 | In this work, we develop a new Quantum-based Matching Kernel (QBMK) for un-attributed graphs, by computing the kernel-based similarity between the quantum Shannon entropies of aligned vertices through the Continuous-time Quantum Walk (CTQW). The theoretical analysis reveals that the proposed QBMK kernel not only addres... |
https://proceedings.mlr.press/v235/bai24b.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/bai24b/bai24b.pdf | https://openreview.net/forum?id=2NUGeV64y2 | Diffusion Models Demand Contrastive Guidance for Adversarial Purification to Advance | https://proceedings.mlr.press/v235/bai24b.html | Mingyuan Bai, Wei Huang, Tenghui Li, Andong Wang, Junbin Gao, Cesar F Caiafa, Qibin Zhao | https://proceedings.mlr.press/v235/bai24b.html | ICML 2024 | In adversarial defense, adversarial purification can be viewed as a special generation task with the purpose to remove adversarial attacks and diffusion models excel in adversarial purification for their strong generative power. With different predetermined generation requirements, various types of guidance have been p... |
https://proceedings.mlr.press/v235/bai24c.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/bai24c/bai24c.pdf | https://openreview.net/forum?id=leJGQCron2 | On the Complexity of Finite-Sum Smooth Optimization under the Polyak–Łojasiewicz Condition | https://proceedings.mlr.press/v235/bai24c.html | Yunyan Bai, Yuxing Liu, Luo Luo | https://proceedings.mlr.press/v235/bai24c.html | ICML 2024 | This paper considers the optimization problem of the form $\min_{{\bf x}\in{\mathbb R}^d} f({\bf x})\triangleq \frac{1}{n}\sum_{i=1}^n f_i({\bf x})$, where $f(\cdot)$ satisfies the Polyak–Łojasiewicz (PL) condition with parameter $\mu$ and $\{f_i(\cdot)\}_{i=1}^n$ is $L$-mean-squared smooth. We show that any gradient m... |
https://proceedings.mlr.press/v235/bai24d.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/bai24d/bai24d.pdf | https://openreview.net/forum?id=AOJCCFTlfJ | Constrained Ensemble Exploration for Unsupervised Skill Discovery | https://proceedings.mlr.press/v235/bai24d.html | Chenjia Bai, Rushuai Yang, Qiaosheng Zhang, Kang Xu, Yi Chen, Ting Xiao, Xuelong Li | https://proceedings.mlr.press/v235/bai24d.html | ICML 2024 | Unsupervised Reinforcement Learning (RL) provides a promising paradigm for learning useful behaviors via reward-free per-training. Existing methods for unsupervised RL mainly conduct empowerment-driven skill discovery or entropy-based exploration. However, empowerment often leads to static skills, and pure exploration ... |
https://proceedings.mlr.press/v235/bailey24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/bailey24a/bailey24a.pdf | https://openreview.net/forum?id=8ho1l6RZNB | Image Hijacks: Adversarial Images can Control Generative Models at Runtime | https://proceedings.mlr.press/v235/bailey24a.html | Luke Bailey, Euan Ong, Stuart Russell, Scott Emmons | https://proceedings.mlr.press/v235/bailey24a.html | ICML 2024 | Are foundation models secure against malicious actors? In this work, we focus on the image input to a vision-language model (VLM). We discover image hijacks, adversarial images that control the behaviour of VLMs at inference time, and introduce the general Behaviour Matching algorithm for training image hijacks. From t... |
https://proceedings.mlr.press/v235/baker24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/baker24a/baker24a.pdf | https://openreview.net/forum?id=SZ0JnRxi0x | An Explicit Frame Construction for Normalizing 3D Point Clouds | https://proceedings.mlr.press/v235/baker24a.html | Justin Baker, Shih-Hsin Wang, Tommaso De Fernex, Bao Wang | https://proceedings.mlr.press/v235/baker24a.html | ICML 2024 | Many real-world datasets are represented as 3D point clouds – yet they often lack a predefined reference frame, posing a challenge for machine learning or general data analysis. Traditional methods for determining reference frames and normalizing 3D point clouds often struggle with specific inputs, lack theoretical gua... |
https://proceedings.mlr.press/v235/balabin24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/balabin24a/balabin24a.pdf | https://openreview.net/forum?id=q0lxAs5GGO | Disentanglement Learning via Topology | https://proceedings.mlr.press/v235/balabin24a.html | Nikita Balabin, Daria Voronkova, Ilya Trofimov, Evgeny Burnaev, Serguei Barannikov | https://proceedings.mlr.press/v235/balabin24a.html | ICML 2024 | We propose TopDis (Topological Disentanglement), a method for learning disentangled representations via adding a multi-scale topological loss term. Disentanglement is a crucial property of data representations substantial for the explainability and robustness of deep learning models and a step towards high-level cognit... |
https://proceedings.mlr.press/v235/balasubramanian24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/balasubramanian24a/balasubramanian24a.pdf | https://openreview.net/forum?id=0tPBk24xNj | Adversarial Attacks on Combinatorial Multi-Armed Bandits | https://proceedings.mlr.press/v235/balasubramanian24a.html | Rishab Balasubramanian, Jiawei Li, Prasad Tadepalli, Huazheng Wang, Qingyun Wu, Haoyu Zhao | https://proceedings.mlr.press/v235/balasubramanian24a.html | ICML 2024 | We study reward poisoning attacks on Combinatorial Multi-armed Bandits (CMAB). We first provide a sufficient and necessary condition for the attackability of CMAB, a notion to capture the vulnerability and robustness of CMAB. The attackability condition depends on the intrinsic properties of the corresponding CMAB inst... |
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ICML 2024 International Conference on Machine Learning 2024 Accepted Paper Meta Info Dataset
This dataset is collect from the ICML 2024 OpenReview website (https://openreview.net/group?id=ICML.cc/2024/Conference#tab-accept-oral) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/icml2024). For researchers who are interested in doing analysis of ICML 2024 accepted papers and potential trends, you can use the already cleaned up json files. Each row contains the meta information of a paper in the ICML 2024 conference. To explore more AI & Robotic papers (NIPS/ICML/ICLR/IROS/ICRA/etc) and AI equations, feel free to navigate the Equation Search Engine (http://www.deepnlp.org/search/equation) as well as the AI Agent Search Engine to find the deployed AI Apps and Agents (http://www.deepnlp.org/search/agent) in your domain.
Meta Information of Json File
{
"abs": "https://proceedings.mlr.press/v235/abad-rocamora24a.html",
"Download PDF": "https://raw.githubusercontent.com/mlresearch/v235/main/assets/abad-rocamora24a/abad-rocamora24a.pdf",
"OpenReview": "https://openreview.net/forum?id=AZWqXfM6z9",
"title": "Revisiting Character-level Adversarial Attacks for Language Models",
"url": "https://proceedings.mlr.press/v235/abad-rocamora24a.html",
"authors": "Elias Abad Rocamora, Yongtao Wu, Fanghui Liu, Grigorios Chrysos, Volkan Cevher",
"detail_url": "https://proceedings.mlr.press/v235/abad-rocamora24a.html",
"tags": "ICML 2024",
"abstract": "Adversarial attacks in Natural Language Processing apply perturbations in the character or token levels. Token-level attacks, gaining prominence for their use of gradient-based methods, are susceptible to altering sentence semantics, leading to invalid adversarial examples. While character-level attacks easily maintain semantics, they have received less attention as they cannot easily adopt popular gradient-based methods, and are thought to be easy to defend. Challenging these beliefs, we introduce Charmer, an efficient query-based adversarial attack capable of achieving high attack success rate (ASR) while generating highly similar adversarial examples. Our method successfully targets both small (BERT) and large (Llama 2) models. Specifically, on BERT with SST-2, Charmer improves the ASR in $4.84$% points and the USE similarity in $8$% points with respect to the previous art. Our implementation is available in https://github.com/LIONS-EPFL/Charmer."
}
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