ResearchArcade
Collection
23 items • Updated
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values | paper_openreview_id stringlengths 9 13 | title stringlengths 4 192 ⌀ | abstract stringlengths 2 4.99k | paper_decision stringclasses 41
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ICLR.cc/2024/Conference | WNSjteBJd9 | Who Leaked the Model? Tracking IP Infringers in Accountable Federated Learning | Federated learning (FL) emerges as an effective collaborative learning framework to coordinate data and computation resources from massive and distributed clients in training. Such collaboration results in non-trivial intellectual property (IP) represented by the model parameters that should be protected and shared by ... | Rejected_Submission | /pdf/7f7e308a6b9983fe6995f6d2ec33988de0eb0fb6.pdf |
ICLR.cc/2024/Conference | gZRfDWLlGY | Exact Path Kernels Naturally Decompose Model Predictions | This paper proposes a generalized exact path kernel gEPK which naturally decomposes model predictions into localized input gradients or parameter gradients. Many cutting edge out-of-distribution (OOD) detection methods are in effect projections onto a reduced representation of the gEPK parameter gradient subspace. This... | Rejected_Submission | /pdf/1c1b44e0a6171231c3ae37e4aaed2b013955f6a9.pdf |
ICLR.cc/2025/Conference | vdUYa7N8Mt | The Rate-Distortion-Perception Trade-Off with Algorithmic Realism | Realism constraints (or constraints on perceptual quality) have received considerable recent attention within the context of lossy compression, particularly of images. Theoretical studies of lossy compression indicate that high-rate common randomness between the compressor and the decompressor is a valuable resource fo... | Rejected_Submission | /pdf/df5f78f3432f64cbaed51951fa2c15e246aaeee4.pdf |
ICLR.cc/2025/Conference | YaRzuMaubS | Defining Deception in Decision Making | With the growing capabilities of machine learning systems, particularly those that interact with humans, there is an increased risk of systems that can easily deceive and manipulate people. Preventing unintended behaviors therefore represents an important challenge for creating aligned AI systems. To approach this chal... | Rejected_Submission | /pdf/e30923c81d69302a4b47a0821e184e52b4d6d294.pdf |
ICLR.cc/2025/Conference | ONfWFluZBI | Self-supervised contrastive learning performs non-linear system identification | Self-supervised learning (SSL) approaches have brought tremendous success across many tasks and domains. It has been argued that these successes can be attributed to a link between SSL and identifiable representation learning: Temporal structure and auxiliary variables ensure that latent representations are related to ... | ICLR 2025 Poster | /pdf/a0e89f340d21f75df21f82b519f7eafed7b8ea88.pdf |
ICLR.cc/2025/Conference | PwxYoMvmvy | Beyond Random Masking: When Dropout meets Graph Convolutional Networks | Graph Convolutional Networks (GCNs) have emerged as powerful tools for learning on graph-structured data, yet the behavior of dropout in these models remains poorly understood. This paper presents a comprehensive theoretical analysis of dropout in GCNs, revealing that its primary role differs fundamentally from standar... | ICLR 2025 Poster | /pdf/2630e5206aeeb0a02cc48e38f5890bb07d7b7f77.pdf |
ICLR.cc/2024/Conference | 3xHDeA8Noi | Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training | Given the massive cost of language model pre-training, a non-trivial improvement of the optimization algorithm would lead to a material reduction on the time and cost of training. Adam and its variants have been state-of-the-art for years, and more sophisticated second-order (Hessian-based) optimizers often incur too m... | ICLR 2024 poster | /pdf/394bc531aa1bd41f10457a74817768d87b04566b.pdf |
ICLR.cc/2024/Conference | yRrPfKyJQ2 | Conversational Drug Editing Using Retrieval and Domain Feedback | Recent advancements in conversational large language models (LLMs), such as ChatGPT, have demonstrated remarkable promise in various domains, including drug discovery. However, existing works mainly focus on investigating the capabilities of conversational LLMs on chemical reactions and retrosynthesis. While drug editi... | ICLR 2024 poster | /pdf/857cda25f605f8054f538d39823dd8328fc64da4.pdf |
ICLR.cc/2024/Conference | iHcTLIor0m | Poly-View Contrastive Learning | Contrastive learning typically matches pairs of related views among a number of unrelated negative views. Views can be generated (e.g. by augmentations) or be observed. We investigate matching when there are more than two related views which we call poly-view tasks,
and derive new representation learning objectives usi... | ICLR 2024 poster | /pdf/c9436edbf2873e4b620287d0d65e7203b16ea79b.pdf |
ICLR.cc/2024/Conference | Jhu4dQv5rY | Contextual Biasing with the Knuth-Morris-Pratt Matching Algorithm | Contextual biasing refers to the problem of biasing the automatic speech recognition (ASR) systems towards rare entities that are relevant to the specific user or application scenarios. We propose algorithms for contextual biasing based on the Knuth-Morris-Pratt algorithm for pattern matching. During beam search, we bo... | Rejected_Submission | /pdf/630ffbf28b0277a140b18248beb9ceb69846f029.pdf |
ICLR.cc/2025/Conference | 0vtftmYQGV | SNAP-TTA: Sparse Test-Time Adaptation for Latency-Sensitive Applications | Test-Time Adaptation (TTA) methods use unlabeled test data to dynamically adjust models in response to distribution changes. However, existing TTA methods are not tailored for practical use on edge devices with limited computational capacity, resulting in a latency-accuracy trade-off. To address this problem, we propos... | Rejected_Submission | /pdf/15d5559569f030abafe634516791ac27749674bd.pdf |
ICLR.cc/2025/Conference | cDdeTXOnAK | AutoCoder: Enhancing Code Large Language Model with AIEV-INSTRUCT | We introduce AutoCoder, an open-source Large Language Model to surpass GPT-4 Turbo and GPT-4o in pass@1 on the Human Eval benchmark test (90.9\% vs. 90.2). In addition, AutoCoder offers a more versatile code interpreter compared to GPT-4 Turbo and GPT-4o. It's code interpreter can install external packages instead of l... | Rejected_Submission | /pdf/8258f5d5510a6bd7a70688f3dd8589659f3b8618.pdf |
ICLR.cc/2024/Conference | bLpUtGyf9g | Boundary Denoising for Video Activity Localization | Video activity localization aims at understanding the semantic content in long, untrimmed videos and retrieving actions of interest. The retrieved action with its start and end locations can be used for highlight generation, temporal action detection, etc. Unfortunately, learning the exact boundary location of activiti... | ICLR 2024 poster | /pdf/1063c6f297ce00850de90397c8fd956657d6ee24.pdf |
ICLR.cc/2024/Conference | cWiEN1plhJ | Few-Shot Detection of Machine-Generated Text using Style Representations | The advent of instruction-tuned language models that convincingly mimic human writing poses a significant risk of abuse. For example, such models could be used for plagiarism, disinformation, spam, or phishing. However, such abuse may be counteracted with the ability to detect whether a piece of text was composed by a ... | ICLR 2024 poster | /pdf/184e7f39e19aac108b138c8b6676a369abcb2fd0.pdf |
ICLR.cc/2025/Conference | f7VXdQTbyW | ThreadsGAN: Enhancing Coherence and Diversity in Discussion Thread Generation | Current research on generating discussion threads faces challenges in coherence, interactivity, and multi-topic handling, which are crucial for meaningful responses. This paper introduces threadsGAN, a model that enhances thread generation by incorporating multi-topic and social response intention tags. By leveraging B... | Rejected_Submission | /pdf/e00e9fce21e4a82589355263427aeefdfba2281f.pdf |
ICLR.cc/2025/Conference | 9DrPvYCETp | Shared Memory for Multi-agent Lifelong Pathfinding | Multi-agent reinforcement learning (MARL) demonstrates significant progress in solving cooperative and competitive multi-agent problems in various environments. One of the main challenges in MARL is the need to explicitly predict other agents' behavior to achieve cooperation. As a solution to this problem, we propose t... | Rejected_Submission | /pdf/aa602f8fb67bffa9115836c5a35ebd178662a3ac.pdf |
ICLR.cc/2025/Conference | odjMSBSWRt | DarkBench: Benchmarking Dark Patterns in Large Language Models | We introduce DarkBench, a comprehensive benchmark for detecting dark design patterns—manipulative techniques that influence user behavior—in interactions with large language models (LLMs). Our benchmark comprises 660 prompts across six categories: brand bias, user retention, sycophancy, anthropomorphism, harmful genera... | ICLR 2025 Oral | /pdf/66d66215ed6e8821cf14e0c9c0e83be089660c40.pdf |
ICLR.cc/2025/Conference | qK6U4Ahfms | OpenCity: A Scalable Platform to Simulate Urban Activities with Massive LLM Agents | Agent-based models (ABMs) have long been employed to explore how individual behaviors aggregate into complex societal phenomena in urban space. Unlike black-box predictive models, ABMs excel at explaining the micro-macro linkages that drive such emergent behaviors. The recent rise of Large Language Models (LLMs) has le... | Rejected_Submission | /pdf/e4f2bbe9d411cd85f82f963fe7f97ac125c6f5e8.pdf |
ICLR.cc/2024/Conference | PCm1oT8pZI | Safe and Robust Watermark Injection with a Single OoD Image | Training a high-performance deep neural network requires large amounts of data and computational resources.
Protecting the intellectual property (IP) and commercial ownership of a deep model is challenging yet increasingly crucial.
A major stream of watermarking strategies implants verifiable backdoor triggers by poi... | ICLR 2024 poster | /pdf/2f25edcb7a348a94dc4f219ebc634278953c1afe.pdf |
ICLR.cc/2024/Conference | L6L1CJQ2PE | Massive Editing for Large Language Models via Meta Learning | While large language models (LLMs) have enabled learning knowledge from the pre-training corpora, the acquired knowledge may be fundamentally incorrect or outdated over time, which necessitates rectifying the knowledge of the language model (LM) after the training. A promising approach involves employing a hyper-networ... | ICLR 2024 poster | /pdf/3ebd770d398a3555f52f8496fa83c188663e9720.pdf |
ICLR.cc/2025/Conference | gaa7gWPZBz | Mitigating Privacy Risk of Adversarial Examples with Counterfactual Explanations | Robustness and privacy are two fundamental security properties that
machine learning models require. Without the balance between robustness and privacy leads to
robust models with high privacy risks. Obtaining machine learning models with high adversarial robustness and
privacy performance remains an open problem. I... | Rejected_Submission | /pdf/9b079bcb6cddaa227ff7201793468ffe4ba4240f.pdf |
ICLR.cc/2025/Conference | hWF0HH8Rr9 | Large-Scale Multi-Agent Reinforcement Learning for Traffic Signal Optimization | We present a novel approach to Traffic Signal Control (TSC) in a multi-agent environment by modeling communication among agents as a sequence problem, enabling intersections within road networks to communicate with one another. Taking inspiration from point cloud processing and graph neural networks, we make our archit... | Rejected_Submission | /pdf/754f34692223b879acd8c2b6a69d87e2882dc9c8.pdf |
ICLR.cc/2025/Conference | imT03YXlG2 | Sparse autoencoders reveal selective remapping of visual concepts during adaptation | Adapting foundation models for specific purposes has become a standard approach to build machine learning systems for downstream applications. Yet, it is an open question which mechanisms take place during adaptation. Here we develop a new Sparse Autoencoder (SAE) for the CLIP vision transformer, named PatchSAE, to ext... | ICLR 2025 Poster | /pdf/469d7576c891b95a4ab30850be28e467ff37f566.pdf |
ICLR.cc/2024/Conference | AwX6ON5A0V | On Gaussian Mixture Models | We investigate the sample complexity of Gaussian mixture models (GMMs). Our results provide the optimal upper bound, in the context of uniform spherical Gaussian mixtures. Furthermore, we highlight the relationship between the sample complexity of GMMs and the distribution of spacings among their means. | Rejected_Submission | /pdf/b9ef4ff4ec8d86ecbdeb1090964a3ab6bfc4da4a.pdf |
ICLR.cc/2024/Conference | XMaPp8CIXq | Always-Sparse Training with Guided Stochastic Exploration | The excessive computational requirements of modern artificial neural networks (ANNs) are posing limitations on the machines that can run them. Sparsification of ANNs is often motivated by time, memory and energy savings only during model inference, yielding no benefits during training. A growing body of work is now foc... | Rejected_Submission | /pdf/5024ceba5bb9c2a8525b3efee1dcad8c4236fdcc.pdf |
ICLR.cc/2024/Conference | RRKggDJxo2 | Real-time learning of decay trajectory of Higgs boson using reservoir-in-reservoir architecture | Real-time learning of the decay trajectory in Higgs bosons as they interact in the Higgs Field is the key to understanding and furthering of the mass providing mechanism and particle interaction mechanism beyond the Standard model in particle physics. We propose a novel machine learning architecture called reservoir-in... | Rejected_Submission | /pdf/b6d26442c27735de43baa940a7dc9f9bdb62326c.pdf |
ICLR.cc/2024/Conference | RwI7ZEfR27 | BrainLM: A foundation model for brain activity recordings | We introduce the Brain Language Model (BrainLM), a foundation model for brain activity dynamics trained on 6,700 hours of fMRI recordings. Utilizing self-supervised masked-prediction training, BrainLM demonstrates proficiency in both fine-tuning and zero-shot inference tasks. Fine-tuning allows for the accurate predict... | ICLR 2024 poster | /pdf/9b47441fd8280d26dca4ee62f9ee211888cd42d6.pdf |
ICLR.cc/2025/Conference | qnAZqlMGTB | StreamingBench: Assessing the Gap for MLLMs to Achieve Streaming Video Understanding | The rapid development of Multimodal Large Language Models (MLLMs) has expanded their capabilities from image comprehension to video understanding. However, most of these MLLMs focus primarily on offline video comprehension, necessitating extensive processing of all video frames before any queries can be made. This prese... | Rejected_Submission | /pdf/510ba84e9bb94cbbd1bcb5060d0bf89b4703ddb7.pdf |
ICLR.cc/2025/Conference | UlAkM88Vum | Action-Constrained Imitation Learning | Policy learning under action constraints plays a central role in ensuring safe behaviors in various robot control and resource allocation applications.
In this paper, we study a new problem setting termed Action-Constrained Imitation Learning (ACIL), where an action-constrained imitator aims to learn from a demonstrati... | Rejected_Submission | /pdf/f27c214f21d1e66e810d1c20c8224e4449ca3f46.pdf |
ICLR.cc/2024/Conference | wOb0xFwdpr | On Sarcasm Detection with OpenAI GPT-based Models | Sarcasm is a form of irony that requires readers or listeners to interpret its intended meaning by considering context and social cues. Machine learning classification models have long had difficulty detecting sarcasm due to its social complexity and contradictory nature.
This paper explores the applications of the Ge... | Rejected_Submission | /pdf/cd2ef3726e341357ec8e7ecf229fe6426b7f99f9.pdf |
ICLR.cc/2024/Conference | VTF8yNQM66 | SWE-bench: Can Language Models Resolve Real-world Github Issues? | Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities. We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models. To this ... | ICLR 2024 oral | /pdf/c2a76eb44300a738cbd7cb95f5bc04df621f4d25.pdf |
ICLR.cc/2025/Conference | pcnq7fZs4t | Common Feature Learning for Zero-shot Image Recognition | The key issue of zero-shot image recognition (ZIR) is how to infer the relationship between visual space and semantic space from seen classes, and then effectively transfer the relationship to unseen classes. Recently, most methods have focused on how to use images and class semantic vectors or class names to learn t... | Rejected_Submission | /pdf/71a2dd1ea1db1d4539578457d0c0f7dc767e3018.pdf |
ICLR.cc/2025/Conference | wE5xp3zBaQ | The Good, the Bad and the Ugly: Watermarks, Transferable Attacks and Adversarial Defenses | We formalize and extend existing definitions of backdoor-based watermarks and adversarial defenses as *interactive protocols* between two players. The existence of these schemes is inherently tied to the learning tasks for which they are designed. Our main result shows that for *almost every* discriminative learning ta... | Rejected_Submission | /pdf/d6cdcd378e7f5598120b57a097b333df83f03766.pdf |
ICLR.cc/2025/Conference | FDimWzmcWn | AgentRefine: Enhancing Agent Generalization through Refinement Tuning | Large Language Model (LLM) based agents have proved their ability to perform complex tasks like humans. However, there is still a large gap between open-sourced LLMs and commercial models like the GPT series. In this paper, we focus on improving the agent generalization capabilities of LLMs via instruction tuning. We f... | ICLR 2025 Poster | /pdf/ed23bb725e8e83d171ff039e600322fa09ee6de9.pdf |
ICLR.cc/2024/Conference | CtiFwPRMZX | A simple connection from loss flatness to compressed representations in neural networks | Deep neural networks' generalization capacity has been studied in a variety of ways, including at least two distinct categories of approach: one based on the shape of the loss landscape in parameter space, and the other based on the structure of the representation manifold in feature space (that is, in the space of uni... | Rejected_Submission | /pdf/7a1c28dd36050217163a2a09e2cb0537a11efd8b.pdf |
ICLR.cc/2024/Conference | 1NHgmKqOzZ | Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality | Dataset distillation aims to minimize the time and memory needed for training deep networks on large datasets, by creating a small set of synthetic images that has a similar generalization performance to that of the full dataset. However, current dataset distillation techniques fall short, showing a notable performance... | ICLR 2024 poster | /pdf/9e441d8d82995f6fd1859104998e6b597e5f6bbb.pdf |
ICLR.cc/2025/Conference | irCuIdCdAl | Improving Transformer Interpretability with Activation Contrast-Based Attribution | Transformers have revolutionized AI research, particularly in natural language processing (NLP). However, understanding the decisions made by transformer-based models remains challenging, which impedes trust and safe deployment in real-world applications. While activation-based attribution methods have proven effective... | Rejected_Submission | /pdf/2e54bc7a4d5a95f64c87a3b8290c25a2cbdaf729.pdf |
ICLR.cc/2025/Conference | yRd4loGAhJ | SEAL: Scaling to Emphasize Attention for Long-Context Retrieval | In this work, we introduce a novel approach called Scaling to Emphasize Attention for Long-context retrieval (SEAL), which enhances the retrieval performance of large language models (LLMs) over extended contexts. Previous studies have shown that each attention head in LLMs has a unique functionality and collectively c... | Rejected_Submission | /pdf/f535de612e0dbd9a5e4002252bd4486520eb13e1.pdf |
ICLR.cc/2025/Conference | Sd4wYYOhmY | TabM: Advancing tabular deep learning with parameter-efficient ensembling | Deep learning architectures for supervised learning on tabular data range from simple multilayer perceptrons (MLP) to sophisticated Transformers and retrieval-augmented methods.
This study highlights a major, yet so far overlooked opportunity for substantially improving tabular MLPs; namely, parameter-efficient ensembl... | ICLR 2025 Poster | /pdf/bebc365e6ad7a313ae48f559c61d88dcf595c492.pdf |
ICLR.cc/2024/Conference | r9FsiXZxZt | Object centric architectures enable efficient causal representation learning | Causal representation learning has showed a variety of settings in which we can disentangle latent variables with identifiability guarantees (up to some reasonable equivalence class). Common to all of these approaches is the assumption that (1) the latent variables are represented as $d$-dimensional vectors, and (2) th... | ICLR 2024 poster | /pdf/3d57a7aa7c838ea0afeff38494e03b1cf226aee4.pdf |
ICLR.cc/2024/Conference | tI3eqOV6Yt | Adaptivity and Modularity for Efficient Generalization Over Task Complexity | Can transformers generalize efficiently on problems that require dealing with examples with different levels of difficulty?
We introduce a new task tailored to assess generalization over different complexities and present results that indicate that standard transformers face challenges in solving these tasks. These ta... | Rejected_Submission | /pdf/c72d05b27626a1ebd90ea49da6c18a5ba2ed893d.pdf |
ICLR.cc/2025/Conference | y15LAM4u0A | EmbodiedCity: A Benchmark Platform for Embodied Agent in Real-world City Environment | Embodied artificial intelligence (EmbodiedAI) emphasizes the role of an agent's body in generating human-like behaviors. The recent efforts on EmbodiedAI pay a lot of attention to building up machine learning models to possess perceiving, planning, and acting abilities, thereby enabling real-time interaction with the ... | Rejected_Submission | /pdf/6c3df24b6df2c7608015f96a86714e223b60acd1.pdf |
ICLR.cc/2025/Conference | tZk3LnvVtK | Measuring Language Model Uncertainty With Internal Concepts | We study the problem of evaluating the predictive uncertainty of large language models (LLMs).
We assign an uncertainty measure to the correctness of outputs from an LLM conditioned on a query using a form of entropy that applies to semantic objects (concepts).
Unlike prior works, the notion of meaning used to define ... | Rejected_Submission | /pdf/5b561c1ed4b804bbdb1ce0f925b394ba16c00a61.pdf |
ICLR.cc/2025/Conference | 75PhjtbBdr | Multi-Label Test-Time Adaptation with Bound Entropy Minimization | Mainstream test-time adaptation (TTA) techniques endeavor to mitigate distribution shifts via entropy minimization for multi-class classification, inherently increasing the probability of the most confident class. However, when encountering multi-label instances, the primary challenge stems from the varying number of l... | ICLR 2025 Poster | /pdf/2bff6d5745da5c1b694abcf3275a5de715dcd964.pdf |
ICLR.cc/2024/Conference | 760br3YEtY | ($\texttt{PEEP}$) $\textbf{P}$redicting $\textbf{E}$nzym$\textbf{e}$ $\textbf{P}$romiscuity with its Molecule Mate – an Attentive Metric Learning Solution | Annotating the functions of proteins (e.g., enzymes) is a fundamental challenge, due to their diverse functionalities and rapidly increased number of protein sequences in databases. Traditional approaches have limited capability and suffer from false positive predictions. Recent machine learning (ML) methods reach sati... | Rejected_Submission | /pdf/229b5541e156c007d936644963f6c16308376bcd.pdf |
ICLR.cc/2025/Conference | wLR9d5ZFpY | No Training Data, No Cry: Model Editing without Training Data or Fine-tuning | Model Editing(ME)--such as classwise unlearning and structured pruning--is a nascent field that deals with identifying editable components that, when modified, significantly change the model's behaviour, typically requiring fine-tuning to regain performance.
The challenge of model editing increases when dealing with mu... | Rejected_Submission | /pdf/d4c79036cbf8db6627816db879083eef4a8639a7.pdf |
ICLR.cc/2025/Conference | kn3GT7LbxT | Value Residual Learning For Alleviating Attention Concentration In Transformers | Transformers can capture long-range dependencies using self-attention, allowing tokens to attend to all others directly. However, stacking multiple attention layers leads to attention concentration. One natural way to address this issue is to use cross-layer attention, allowing information from earlier layers to be dir... | Rejected_Submission | /pdf/256b822b4875504ba69ec60740df37a0227970a8.pdf |
ICLR.cc/2025/Conference | uClUUJk05H | Compositional simulation-based inference for time series | Amortized simulation-based inference (SBI) methods train neural networks on simulated data to perform Bayesian inference. While this strategy avoids the need for tractable likelihoods, it often requires a large number of simulations and has been challenging to scale to time series data. Scientific simulators frequently... | ICLR 2025 Poster | /pdf/1dbae3c70d8c2fc5739a83e2aa1534ecb821b10c.pdf |
ICLR.cc/2025/Conference | XLMAMmowdY | ToolGen: Unified Tool Retrieval and Calling via Generation | As large language models (LLMs) advance, their inability to autonomously execute tasks by directly interacting with external tools remains a critical limitation. Traditional methods rely on inputting tool descriptions as context, which is constrained by context length and requires separate, often inefficient, retrieval... | ICLR 2025 Poster | /pdf/b5d464a0c1f8e39ed945666ae1468185132c7754.pdf |
ICLR.cc/2024/Conference | J1djqLAa6N | Efficient Score Matching with Deep Equilibrium Layers | Score matching methods -- estimate probability densities without computing the normalization constant -- are particularly useful in deep learning. However, computational and memory costs of score matching methods can be prohibitive for high-dimensional data or complex models, particularly due to the derivatives or Hess... | ICLR 2024 poster | /pdf/5a1c744cb67c5bf5bf079113f4ba7d6825f45bd0.pdf |
ICLR.cc/2024/Conference | ONhLaNbxVV | Improving Prototypical Part Networks with Reward Reweighing, Reselection, and Retraining | In recent years, work has gone into developing deep interpretable methods for image classification that clearly attributes a model's output to specific features of the data. One such of these methods is the \textit{prototypical part network} (ProtoPNet), which attempts to classify images based on meaningful parts of th... | Rejected_Submission | /pdf/681a22f29efb50baa2e70db4b29fef081d9da21a.pdf |
ICLR.cc/2024/Conference | jYsowwcXV1 | A Data Perspective on Enhanced Identity Preservation for Diffusion Personalization | Large text-to-image models have revolutionized the ability to generate imagery using natural language. However, particularly unique or personal visual concepts, such as your pet, an object in your house, etc., will not be captured by the original model. This has led to interest in how to inject new visual concepts, bou... | Rejected_Submission | /pdf/b5eac88faa234cc74366667dddc412c9638404fa.pdf |
ICLR.cc/2025/Conference | yRKelogz5i | Causally Motivated Sycophancy Mitigation for Large Language Models | Incorporating user preferences into large language models (LLMs) can enhance the personalization and reliability of model outputs and facilitate the application of LLMs to real-world scenarios. However, leveraging user preferences can be a double-edged sword. Recent studies have found that improper utilization can incu... | ICLR 2025 Poster | /pdf/a24fb8781480a42b0b7877a0529b415440c7a3a7.pdf |
ICLR.cc/2025/Conference | GsCMKwyfWm | LVLM-COUNT: Enhancing the Counting Ability of Large Vision-Language Models | Counting is a fundamental skill for various visual tasks in real-life applications, requiring both object recognition and robust counting capabilities. Despite their advanced visual perception, large vision-language models (LVLMs) struggle with counting tasks, especially when the number of objects exceeds those commonl... | Rejected_Submission | /pdf/80f890cf28f9838fc40b2d3b66370776945328ea.pdf |
ICLR.cc/2024/Conference | zMvMwNvs4R | Error Norm Truncation: Robust Training in the Presence of Data Noise for Text Generation Models | Text generation models are notoriously vulnerable to errors in the training data. With the wide-spread availability of massive amounts of web-crawled data becoming more commonplace, how can we enhance the robustness of models trained on a massive amount of noisy web-crawled text? In our work, we propose Error Norm Trun... | ICLR 2024 spotlight | /pdf/1f26c396130ee811d490f099f908c0b8c99a3382.pdf |
ICLR.cc/2024/Conference | 97Dl82avFs | Alt-Text with Context: Improving Accessibility for Images on Twitter | In this work we present an approach for generating alternative text (or alt-text) descriptions for images shared on social media, specifically Twitter. More than just a special case of image captioning, alt-text is both more literally descriptive and context-specific. Also critically, images posted to Twitter are often... | ICLR 2024 poster | /pdf/c1026cd07ff18150f89e055f2d06edfc22ba23f7.pdf |
ICLR.cc/2025/Conference | 9e5syenoVE | Multiple-play Stochastic Bandits with Prioritized Resource Sharing | This paper proposes a variant of multiple-play stochastic bandits tailored to resource allocation problems arising from LLM applications,
edge intelligence applications, etc. The proposed model is composed of $M$ arms and $K$ plays. Each arm has a stochastic number of capacities, and each unit of capacity is associ... | Rejected_Submission | /pdf/c086e80e9482dd84fe06b3c9cc5874b286c9c26d.pdf |
ICLR.cc/2025/Conference | ayT4e9C3Gd | ROSARL: Reward-Only Safe Reinforcement Learning | An important problem in reinforcement learning is designing agents that learn to solve tasks safely in an environment. A common solution is to define either a penalty in the reward function or a cost to be minimised when reaching unsafe states. However, designing reward or cost functions is non-trivial and can increase... | Rejected_Submission | /pdf/eee5786275d7d8be6da118f068d7e09fe9a76892.pdf |
ICLR.cc/2025/Conference | NNBAzdF7Cg | Binary Spiking Neural Networks as causal models | In this paper, we provide a causal analysis of binary spiking neural networks (BSNNs)
aimed at explaining their behaviors.
We formally define a BSNN
and represent its spiking activity
as a binary causal model.
Thanks to this causal representation,
we are able to explain the output of the network
by leveraging... | Rejected_Submission | /pdf/1ee54d4fb16d3625681306398b2e06ee104c6e2a.pdf |
ICLR.cc/2024/Conference | X2gjYmy77l | Taming AI Bots: Controllability of Neural States in Large Language Models | We tackle the question of whether an agent can, by suitable choice of prompts, control an AI bot to any state. We view large language models (LLMs) and their corresponding conversational interfaces (AI bots) as discrete-time dynamical systems evolving in the embedding space of (sub-)word tokens, where they are triviall... | Rejected_Submission | /pdf/bd769a02a136801e4f105cfb484afb8bb88d571a.pdf |
ICLR.cc/2024/Conference | 1YPfmglNRU | Defining Expertise: Applications to Treatment Effect Estimation | Decision-makers are often experts of their domain and take actions based on their domain knowledge. Doctors, for instance, may prescribe treatments by predicting the likely outcome of each available treatment. Actions of an expert thus naturally encode part of their domain knowledge, and can help make inferences within... | ICLR 2024 poster | /pdf/35dbc01121c44c86a33cf8d22866d22f5320a557.pdf |
ICLR.cc/2024/Conference | BxPqibGUPR | VibeSpace: Automatic vector embedding creation for arbitrary domains and mapping between them using large language models | We present VibeSpace; a method for the fully unsupervised construction of interpretable embedding spaces applicable to arbitrary domain areas. By leveraging knowledge contained within large language models, our method automates otherwise costly data acquisition processes and assesses the similarity of entities, allowin... | Rejected_Submission | /pdf/4aed14fefa5225bf4c4b2b7d3f94add60bab4ae1.pdf |
ICLR.cc/2025/Conference | itwyfJilM5 | Graph Scattering Networks with Adaptive Diffusion Kernels | Scattering networks are deep convolutional architectures that use predefined wavelets for feature extraction and representation. They have proven effective for classification tasks, especially when training data is scarce, where traditional deep learning methods struggle. In this work, we introduce and develop a mathem... | Rejected_Submission | /pdf/95a5a271a9b6219098bceb6eb90f5adada5276e9.pdf |
ICLR.cc/2025/Conference | KA2Rit4ky1 | PDETime: Rethinking Long-term Multivariate Time Series Forecasting from the Perspective of Partial Differential Equations | Recent advancements in deep learning have led to the development of various approaches for long-term multivariate time-series forecasting (LMTF). Most of these approaches can be categorized as either historical-value-based methods, which rely on discretely sampled past observations, or time-index-based methods that mod... | Rejected_Submission | /pdf/f23dacc00c9d15ce85d5364100cf419e5b992754.pdf |
ICLR.cc/2024/Conference | uBpSkFGVQU | Depth-Guided Self-Supervised Learning: Seeing the World in 3D | Self-Supervised Learning (SSL) methods operate on unlabeled data to learn robust representations useful for downstream tasks. Most SSL methods rely on augmentations obtained by transforming the 2D image pixel map. These augmentations ignore the fact that biological vision takes place in an immersive three-dimensional, ... | Rejected_Submission | /pdf/cb049d9dc19d329220ff7f043e1310f54b85609a.pdf |
ICLR.cc/2025/Conference | v2nEL42Pvb | SSGNN: Simple Yet Effective Spectral Graph Neural Network | Spectral GNNs leverage graph spectral properties to model graph representations but have been less explored due to their computational challenges, especially compared to the more flexible and scalable spatial GNNs, which have seen broader adoption. However, spatial methods cannot fully exploit the rich information in g... | Rejected_Submission | /pdf/fc199855f2476f218add439aa10285d0f0630683.pdf |
ICLR.cc/2025/Conference | vxvgZ0kTFv | Gradient Descent Converges Linearly to Flatter Minima than Gradient Flow in Shallow Linear Networks | We study the gradient descent (GD) dynamics of a depth-2 linear neural network with a single input and output. We show that GD converges at an explicit linear rate to a global minimum of the training loss, even with a large stepsize--about $2/\textrm{sharpness}$. It still converges for even larger stepsizes, but may do... | Rejected_Submission | /pdf/1c2204258657182578f3289ebb7e8ecc29524363.pdf |
ICLR.cc/2025/Conference | vjbIer5R2H | Improved Risk Bounds with Unbounded Losses for Transductive Learning | In the transductive learning setting, we are provided with a labeled training set and an unlabeled test set, with the objective of predicting the labels of the test points. This framework differs from the standard problem of fitting an unknown distribution with a training set drawn independently from this distribution.... | Rejected_Submission | /pdf/d7d9cb8bbd1567b0bc079468d50f710ec7576806.pdf |
ICLR.cc/2025/Conference | RlpJmARXqj | Adaptive Self-Supervised Learning Strategies for Dynamic On-Device LLM Personalization | Large language models (LLMs) have revolutionized how we interact with technology, but their personalization to individual user preferences remains a significant challenge, particularly in on-device applications. Traditional methods often depend heavily on labeled datasets and can be resource-intensive. To address these... | Rejected_Submission | /pdf/e857d244c08eca580a44cc8bca343c464c0c5869.pdf |
ICLR.cc/2018/Conference | H1NV4agCb | Tracking Loss: Converting Object Detector to Robust Visual Tracker | In this paper, we find that by designing a novel loss function entitled, ''tracking loss'', Convolutional Neural Network (CNN) based object detectors can be successfully converted to well-performed visual trackers without any extra computational cost. This property is preferable to visual tracking where annotated video... | Reject | /pdf/06ea09534e938f0792175ba1cb770b994107116e.pdf |
ICLR.cc/2025/Conference | iIrvKrtwnZ | HuRi : Humanoid Robots Adaptive Risk-ware Distributional Reinforcement Learning for Robust Control | Due to the high complexity of bipedal locomotion, the locomotion control of humanoid robots requires precise adjustment of the balance system to adapt to the varying environment conditions. In the past, few studies have explicitly incorporated risk factors into robot policy training, and lacked the ability to adaptivel... | Rejected_Submission | /pdf/54446585530bf09a43381a35f76a0b22e3c5d3e7.pdf |
ICLR.cc/2025/Conference | Uc3kog3O45 | Global Context-aware Representation Learning for Spatially Resolved Transcriptomics | Spatially Resolved Transcriptomics (SRT) is a cutting-edge technique that captures the spatial context of cells within tissues, enabling the study of complex biological networks. Recently, graph-based deep learning has been utilized in identifying meaningful spatial domains by leveraging both gene expression and spatia... | Rejected_Submission | /pdf/3015f7cd278881bf5d00a076ec3dc7b9018515f7.pdf |
ICLR.cc/2025/Conference | 04RGjODVj3 | From Rest to Action: Adaptive Weight Generation for Motor Imagery Classification from Resting-State EEG Using Hypernetworks | Existing EEG-based brain-computer interface (BCI) systems require long calibration sessions from the intended users to train the models, limiting their use in real-world applications. Additionally, despite containing user-specific information and features correlating with BCI performance of a user, resting-state EEG da... | Rejected_Submission | /pdf/fe67b84fb9f3855a93c69b0b64c4bac3103faf9d.pdf |
ICLR.cc/2025/Conference | xPTzjpIQNp | Optimal Transport for Time Series Imputation | Missing data imputation through distribution alignment has demonstrated advantages for non-temporal datasets but exhibits suboptimal performance in time-series applications. The primary obstacle is crafting a discrepancy measure that simultaneously (1) captures temporal patterns—accounting for periodicity and temporal ... | ICLR 2025 Poster | /pdf/b5211a0cc33b9c5316eb4545340830695d7cc21f.pdf |
ICLR.cc/2024/Conference | ZSD3MloKe6 | Alleviating Exposure Bias in Diffusion Models through Sampling with Shifted Time Steps | Diffusion Probabilistic Models (DPM) have shown remarkable efficacy in the synthesis of high-quality images. However, their inference process characteristically requires numerous, potentially hundreds, of iterative steps, which could exaggerate the problem of exposure bias due to the training and inference discrepancy.... | ICLR 2024 poster | /pdf/f903a547671f42a166043602f68eab7350998e6a.pdf |
ICLR.cc/2025/Conference | j0sq9r3HFv | Automated Parameter Extraction for Biologically Realistic Neural Networks: An Initial Exploration with Large Language Models | In computational neuroscience, extracting parameters for constructing biologically realistic neural models is a resource-intensive task that requires continuous updates as new research emerges. This paper explores utilizing large language models (LLMs) in automating parameter extraction from scientific literature for b... | Rejected_Submission | /pdf/3f353293b66d9dbebc9521f94fa1ef1a410a9fe4.pdf |
ICLR.cc/2025/Conference | Y9yQ9qmVrc | scKGOT: Intercellular Signaling Inference with Knowledge Graph Optimal Transport for Single-cell Transcriptomics | Single-cell transcriptomics provides detailed genetic insights into cellular heterogeneity within intact organs and the intercellular signaling that underpins tissue homeostasis, development, and disease. To improve the inference of intercellular signaling and pathway activity, we introduce scKGOT, a novel method that ... | Rejected_Submission | /pdf/8cb7ca811f567389c2e9def62968c7a5b9c6029b.pdf |
ICLR.cc/2025/Conference | Mi45HjlVRj | Injecting Learnable Table Features into LLMs | To migrate the remarkable successes of Large Language Models (LLMs), the community has made numerous efforts to extend them to the table reasoning tasks for the widely deployed tabular data. Despite that, in this work, by showing a probing experiment on our proposed StructQA benchmark, we postulate that even the most a... | Rejected_Submission | /pdf/633a60f2e6719bcd3116834a63673b6f0bf3c852.pdf |
ICLR.cc/2025/Conference | 3bcN6xlO6f | Video Action Differencing | How do two individuals differ when performing the same action? In this work, we introduce Video Action Differencing (VidDiff), the novel task of identifying subtle differences between videos of the same action, which has numerous applications, such as coaching and skill learning. To enable development on this new task,... | ICLR 2025 Poster | /pdf/102482b5babaacddfd916de17bda7c15b2020db5.pdf |
ICLR.cc/2024/Conference | gzT61ziSCu | Automatic Functional Differentiation in JAX | We extend JAX with the capability to automatically differentiate higher-order functions (functionals and operators). By representing functions as infinite dimensional generalization of arrays, we seamlessly use JAX's existing primitive system to implement higher-order functions. We present a set of primitive operators ... | ICLR 2024 poster | /pdf/f304e5e89c5a8226b8d319b116acda0074847079.pdf |
ICLR.cc/2024/Conference | mGHJAyR8w0 | Rethinking the Benefits of Steerable Features in 3D Equivariant Graph Neural Networks | Theoretical and empirical comparisons have been made to assess the expressive power and performance of invariant and equivariant GNNs. However, there is currently no theoretical result comparing the expressive power of $k$-hop invariant GNNs and equivariant GNNs. Additionally, little is understood about whether the per... | ICLR 2024 poster | /pdf/74b784ba5dc8dddcc830faa9c5c2cc8f035fd123.pdf |
ICLR.cc/2024/Conference | dALYqPm9gW | Recurrent Linear Transformers | The self-attention mechanism in the transformer architecture is capable of capturing long-range dependencies and it is the main reason behind its effectiveness in processing sequential data. Nevertheless, despite their success, transformers have two significant drawbacks that still limit their broader applicability: (... | Rejected_Submission | /pdf/fc6538dd41043dd25078ede2d13f6dd47031fda6.pdf |
ICLR.cc/2025/Conference | GdXI5zCoAt | RaSA: Rank-Sharing Low-Rank Adaptation | Low-rank adaptation (LoRA) has been prominently employed for parameter-efficient fine-tuning of large language models (LLMs). However, the limited expressive capacity of LoRA, stemming from the low-rank constraint, has been recognized as a bottleneck, particularly in rigorous tasks like code generation and mathematical... | ICLR 2025 Poster | /pdf/26347c818217d5c434c3eab6d9f6aea95413ae38.pdf |
ICLR.cc/2025/Conference | DbZDbg2z9q | Ontology-Retrieval Augmented Generation for Scientific Discovery | Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, sparkling an increasing interest for their application in science. However, in scientific domains, their utility is often limited by hallucinations that violate established relationships between concepts or ignore their... | Rejected_Submission | /pdf/c53d89b79e99da1907ed9be63d407d19f199d67a.pdf |
ICLR.cc/2024/Conference | SZErAetdMu | Time Series Modeling at Scale: A Universal Representation Across Tasks and Domains | Time series are ubiquitous, capturing real-world phenomena ranging from human
neuronal firing and tectonic activity to atmospheric conditions. However, they are
challenging to analyze due to domain-specific timescales (e.g., sub-second for brain
activity and years for weather phenomena), complex multivariate relations,... | Rejected_Submission | /pdf/54b3bc1b407713a227c0e6e333f054c42e5967fd.pdf |
ICLR.cc/2024/Conference | 5eLgTLusaR | Loco3D: Indoor Multiuser Locomotion 3D Dataset | In the context of human-AI interaction, modeling human actions is a critical and challenging endeavor, with locomotion being a particularly fundamental behavior for AI agents to understand. Modeling human trajectories in complex indoor scenes, such as the home environment, requires an understanding of how humans intera... | Rejected_Submission | /pdf/6d496a76c7f759d37bf5c1e771161d44bd6f95ce.pdf |
ICLR.cc/2024/Conference | z3L59iGALM | Massively Scalable Inverse Reinforcement Learning in Google Maps | Inverse reinforcement learning (IRL) offers a powerful and general framework for learning humans' latent preferences in route recommendation, yet no approach has successfully addressed planetary-scale problems with hundreds of millions of states and demonstration trajectories. In this paper, we introduce scaling techni... | ICLR 2024 spotlight | /pdf/4d44d3413d097944d3e6328c02e01436c56a3fac.pdf |
ICLR.cc/2025/Conference | NtwFghsJne | From Search to Sampling: Generative Models for Robust Algorithmic Recourse | Algorithmic Recourse provides recommendations to individuals who are adversely impacted by automated model decisions, on how to alter their profiles to achieve a favorable outcome. Effective recourse methods must balance three conflicting goals: proximity to the original profile to minimize cost, plausibility for reali... | ICLR 2025 Poster | /pdf/fcce4ead07ac9d558bf045dbc62907d1308dd399.pdf |
ICLR.cc/2025/Conference | 6w9qffvXkq | Improving CNN training by Riemannian optimization on the generalized Stiefel manifold combined with a gradient-based manifold search | Enforcing orthonormality constraints in deep learning has been shown to provide significant benefits. Although hard restrictions can be applied by constraining parameter matrices to the Stiefel manifold, this approach limits the solution space to that specific manifold. We show that a generalized Stiefel constraint $X^... | Rejected_Submission | /pdf/ed5e0074aff4a10cd703abbeb1d46eb2e190ed81.pdf |
ICLR.cc/2024/Conference | mliQ2huFrZ | Class Probability Matching with Calibrated Networks for Label Shift Adaption | We consider the domain adaptation problem in the context of label shift, where the label distributions between source and target domain differ, but the conditional distributions of features given the label are the same. To solve the label shift adaption problem, we develop a novel matching framework named \textit{clas... | ICLR 2024 poster | /pdf/f71e2de1b4ae7cf346b9a0e07e7fc9b9901ad681.pdf |
ICLR.cc/2025/Conference | KRqMfdwQaP | SEAL-Pose: Enhancing Pose Estimation through Trainable Loss Function | Accurately predicting 3D human pose is a challenging task in computer vision due to the need to capture complex spatial structures and anatomical constraints. We propose SEAL-Pose, an adaptation of the Structured Energy As Loss (SEAL) framework for deterministic models, specifically designed to enhance 3D human pose es... | Rejected_Submission | /pdf/dedeff3138009af7e70da257eb70762931ac49e8.pdf |
ICLR.cc/2025/Conference | kbeX97jExm | Neural Wave Equation for Irregularly Sampled Sequence Data | Sequence labeling problems arise in several real-world applications such as healthcare and robotics. In many such applications, sequence data are irregularly sampled and are of varying complexities. Recently, efforts have been made to develop neural ODE-based architectures to model the evolution of hidden states contin... | ICLR 2025 Poster | /pdf/8ed140ad99d8ff52315acf85732ba20a69b155a7.pdf |
ICLR.cc/2025/Conference | bzB7OIbITu | Prompt-Independent Safe Decoding to Restrain Unsafe Image Generation for Text-to-Image Models against White-Box Adversary | Text-to-image (T2I) models, developed through extensive training, are capable of generating realistic images from textual inputs, profoundly influencing various facets of our lives. Nevertheless, they can be exploited by adversaries who input malicious prompts, leading to the creation of unsafe content and posing serio... | Rejected_Submission | /pdf/268eb0338d6ee1bd99b11b61383e589ff2036ac5.pdf |
ICLR.cc/2025/Conference | XwibrZ9MHG | PokeFlex: A Real-World Dataset of Deformable Objects for Robotics | Data-driven methods have shown great potential in solving challenging manipulation tasks, however, their application in the domain of deformable objects has been constrained, in part, by the lack of data.
To address this, we propose PokeFlex, a dataset featuring real-world paired and annotated multimodal data that inc... | Rejected_Submission | /pdf/d0fe62f6491c4d9c249830cc1e2365af0dde847b.pdf |
ICLR.cc/2024/Conference | eJHnSg783t | DIFFTACTILE: A Physics-based Differentiable Tactile Simulator for Contact-rich Robotic Manipulation | We introduce DIFFTACTILE, a physics-based differentiable tactile simulation system designed to enhance robotic manipulation with dense and physically accurate tactile feedback. In contrast to prior tactile simulators which primarily focus on manipulating rigid bodies and often rely on simplified approximations to model... | ICLR 2024 poster | /pdf/ca114f69e2dc9d526e44fe9161eacd32eca35c8b.pdf |
ICLR.cc/2024/Conference | VLFhbOCz5D | Tangent Transformers for Composition,Privacy and Removal | We introduce Tangent Attention Fine-Tuning (TAFT), a method for fine-tuning linearized transformers obtained by computing a First-order Taylor Expansion around a pre-trained initialization. We show that the Jacobian-Vector Product resulting from linearization can be computed efficiently in a single forward pass, reduci... | ICLR 2024 poster | /pdf/c77be697e6bc470f2fc4b8ddfbd306f34883f76e.pdf |
ICLR.cc/2025/Conference | TdIx7u2ECv | Imagine to Ensure Safety in Hierarchical Reinforcement Learning | This work investigates the safe exploration problem, where an agent must maximize performance while satisfying safety constraints. To address this problem, we propose a method that includes a learnable world model and two policies, a high-level policy and a low-level policy, that ensure safety at both levels. The high-... | Rejected_Submission | /pdf/cb94dcb73c9654cc67316139a86a0ea261042e37.pdf |
ICLR.cc/2025/Conference | Bdhro9gxuF | The Advancement in Stochastic Zeroth-Order Optimization: Mechanism of Accelerated Convergence of Gaussian Direction on Objectives with Skewed Hessian Eigenvalues | This paper primarily investigates large-scale finite-sum optimization problems, which are particularly prevalent in the big data era.
In the field of zeroth-order optimization, stochastic optimization methods have become essential tools.
Natural zeroth-order stochastic optimization methods are primarily based on stoc... | Rejected_Submission | /pdf/e3549608ee59c7517d4103c34edd0e127e38ddad.pdf |
ICLR.cc/2025/Conference | 7jDv1RrNQX | Path Selection Makes BERT-family Good Generators | The Mask-Predict decoding algorithm has been widely used to enhance the generation capacity of traditional non-autoregressive (NAR) models and provide a good recipe for adapting the pre-trained BERT-like masked language models (MLMs) to NAR generation scenarios.
However, these models, which we denote as NAR-MLMs, are s... | Rejected_Submission | /pdf/e81ff5b225f81bafc96cc4ff81a53fc702c463e7.pdf |
ICLR.cc/2024/Conference | yV6wwEbtkR | Bayes Conditional Distribution Estimation for Knowledge Distillation Based on Conditional Mutual Information | It is believed that in knowledge distillation (KD), the role of the teacher is to provide an estimate for the unknown Bayes conditional probability distribution (BCPD) to be used in the student training process. Conventionally, this estimate is obtained by training the teacher using maximum log-likelihood (MLL) method.... | ICLR 2024 poster | /pdf/ef0ffe301e1cc1839e1ba8713066bf60c490d401.pdf |
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