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**A**: Therefore, it is desirable to develop more scalable and non-iterative estimation methods and aid psychometric researchers and practitioners to perform GoM analysis of modern item response data.
**B**: However, due to its iterative manner, JML’s efficiency is still unsatisfactory when applied to very large-scale ... | BCA | ABC | ABC | CBA | Selection 4 |
**A**: An algorithm has been proposed that generates a Bernoulli random variable of arbitrary parameter τ𝜏\tauitalic_τ, using a sequence of independent Bernoulli variables of parameter 1/2121/21 / 2 as input**B**: The algorithm requires a positive series representation of τ𝜏\tauitalic_τ, and a bound for the truncatio... | BAC | ACB | ACB | ABC | Selection 4 |
**A**: In other words, it is not entirely clear what the SCMS is actually estimating**B**: Even though it appears that this does not have a serious impact in many practical examples, this theoretical gap provides a motivation for developing alternative ridge finding algorithms that (i) come with theoretical guarantees ... | BAC | BCA | BAC | BAC | Selection 2 |
**A**: However, the DA-PM-MH scheme seems to provide slightly better approximations.
**B**: In Table 5, we show the MMSE estimations of 𝜽𝜽{\bm{\theta}}bold_italic_θ provided by the different algorithms**C**: We can observe that the compared techniques are able to approximate the groundtruth marginal histograms | ACB | ACB | BAC | CAB | Selection 4 |
**A**: This result appears to be the first rate in supsupremum\suproman_sup norm for a nonlinear, long term dose response**B**:
Under these conditions, we arrive at our main result: uniform consistency of long term dose response curves**C**: It accommodates general types of short term rewards, actions, and contexts. | CAB | BAC | CBA | ABC | Selection 2 |
**A**: In this setting, we argue that the performance of SAA is the worse when observing two samples with different values and show that selecting two different point mass distributions is the unique worst-case sequence of historical distributions.
**B**: Proposition 1 formalizes that the performance of SAA, a central ... | BAC | CAB | BAC | BCA | Selection 2 |
**A**: In Section 4.3, we present a model selection strategy and show an application on the 30303030 publicly available datasets listed in Appendix G. The model selection strategy is applied to historically fully developed market data in Section 4.4. In Appendix F shows how to use the clmplus package to replicate some ... | CAB | ABC | ACB | BCA | Selection 1 |
**A**: we show the distributions of covariates in two populations are different and some covariates may modify effects. In Section 8.2, we apply the proposed method to two datasets and estimate the ATE of different dietary components in the target population (the whole U.S. pregnant female population). Finally sensitiv... | ABC | ABC | CBA | ACB | Selection 3 |
**A**: 12 bins are more accurate than 5, but 120 bins are even less accurate than 5 due to sparsity. With splines we achieve best accuracy. **B**:
Figure 4: Results of segmentized approximations of four FFM models trained on synthetic data**C**: In each plot, a family of segmentized functions on the interval [0,40]040... | CAB | BCA | CBA | ACB | Selection 1 |
**A**: In this section we consider a simulation of the T-maze experimental setting (CFFG of Fig. 7) and two extensions thereupon**B**: The initial T-maze simulation considers perception and learning from repeated trials, where we compare behaviour between a GFE and Bethe Free Energy (BFE) based agent**C**: A first exte... | ACB | BAC | ABC | BCA | Selection 3 |
**A**: For further information about Glance, we refer the reader to their website: https://glance.com/us. platform – a smart lock-screen that aims to personalize user experience through recommending dynamic lock screens (also called glances). We find that MNN can improve the mean-squared error by 28x compared to a stan... | BAC | ABC | BAC | CBA | Selection 4 |
**A**: The exponential function is directly related to trigonometric functions as well as to characteristic functions of distributions**B**: We combine the methodology proposed in (Jasour et al., 2021) with Prob-Solvable loops to obtain exact moments of trigonometric and exponential functions of random variables at lo... | BCA | ABC | BAC | ABC | Selection 1 |
**A**: The loss function is motivated by the intuition that nodes densely interconnected with edges in a given network are likely to exhibit similar labels**B**:
In this study, we propose a novel probability-based objective (loss) function for the semi-supervised node classification (community detection) task using hi... | BCA | BAC | ABC | CBA | Selection 2 |
**A**: The pattern of results, however, changes under DGP3 (nonlinear and discontinuous) where the learners now outperform OLS in terms of RMSE and bias**B**: The tree-based learners perform well in terms of bias as the sample size increases, but under-estimate the standard errors with CART performing particular poorly... | CBA | ACB | CBA | BAC | Selection 2 |
**A**: The intervention ratio denotes the proportion of provided correct concepts**B**: We use CEM with RandInt. CelebA and AWA2 do not have grouped concepts; thus we adopt individual intervention.
**C**: Performance with different ratios of intervened concepts on three datasets (with error bars) | CAB | BAC | BCA | CAB | Selection 3 |
**A**: For a comprehensive review on the subject, we refer to (Williams and Rasmussen,, 2006; Gramacy,, 2020).**B**:
In this work, we consider Gaussian Process (GP) based surrogate models because GPs induce closed-form posterior distributions**C**: We briefly recall the basic concepts of GP surrogates | ACB | CBA | CBA | CAB | Selection 4 |
**A**: The time dependence between the observations poses technical challenges to ensure the asymptotic guarantees**B**: In this work, we extend the recent development on ancestor regression by Schultheiss and Bühlmann, (2023) to the case of multivariate time series with linear causal relations, both instantaneous and ... | BAC | CAB | ACB | ABC | Selection 1 |
**A**: 2021), MixUp (Zhang et al. 2017), and MentorMix (Jiang et al. 2020), known for their inherent tolerance to mislabeled data.**B**: 2023), Sharpness Aware Minimization (SAM) (Foret et al**C**: We compare our method to several label noise robust learning algorithms:
Logit Clip (Wei et al | ACB | CBA | BAC | CAB | Selection 2 |
**A**: The above result motivates the following paradigm: we can construct the forward process (1.1) by time-discretizing the diffusion process (1.3), and construct the reverse process (1.2) by discretizing the reverse-time SDE (1.4) and learning the score functions from the data**B**: Although the idea of the DDPM sam... | ABC | CAB | ABC | ACB | Selection 4 |
**A**: Common approaches include local search algorithms like random-walk Mcmc (Ronquist et al., 2012) and sequential search algorithms like Combinatorial Sequential Monte Carlo (Csmc) (Bouchard-Côté et al., 2012; Wang et al., 2015). Mcmc methods also handle model learning. Dinh et al**B**:
A recent body of research ... | ACB | CBA | ABC | BAC | Selection 4 |
**A**: Similarly, Ebrahimi et al**B**: [31] explored zero-inflated and hurdle models to better capture the inherent sparsity in social and biological networks.
Furthermore, Dong et al. [15] and Motalebi et al. [32] specifically focused on adapting stochastic block models to account for excess zeroes, underscoring the i... | ACB | CAB | BCA | BCA | Selection 1 |
**A**: The large deviation principle for the number of edges in the graph in Theorem 2.3 suggests that also a central limit theorem should also hold**B**: Several natural and interesting extensions are possible**C**: This is further exemplified by the nice limiting generating function for the number of edges in Lemma 2... | ABC | BAC | CAB | CAB | Selection 2 |
**A**: These are closely related to the probability flow ODE (pfODE) view of DBMs, and, in fact, have been shown to be equivalent to such models for specific choices of “interpolant” functions and conditional distributions. Despite their exceptional generative performance and deterministic nature, existing flow matchin... | BCA | CBA | BCA | BAC | Selection 2 |
**A**:
Following the methodology outlined in (Sesia and Candès 2020), we rescale the response Y𝑌Yitalic_Y by the mean absolute value**B**: We randomly allocate 20%percent2020\%20 % of the samples for testing, and from the remaining data, we utilize 70%percent7070\%70 % for training the quantile regression model and 3... | CBA | ABC | ACB | CBA | Selection 2 |
**A**: We relax this condition to allow for small amounts of overlap between groups. Second, given prior knowledge of the number of unknown labels being summed, we can stratify the prediction into different levels. Finally, we can incorporate other conformal prediction methods, such as conformalized quantile regression... | BCA | BCA | ACB | CAB | Selection 4 |
**A**: 13 and quantitatively in Table 3. Also, in comparing performance for in-distribution and out-of-distribution predictions (for same number of training data points N=50𝑁50N=50italic_N = 50) in Table 3, one can see that, although the mean prediction is less accurate for out-of-distribution data, which can be expec... | CAB | ABC | CBA | BCA | Selection 1 |
**A**: Our linear-regression-based estimator integrates seamlessly into algorithms for preference-based bandits with linear human utility functions [3, 31], enabling interactive learning systems to leverage response times for faster learning**B**: We specifically integrated our estimator into the Generalized Successive... | ACB | CBA | ABC | CBA | Selection 3 |
**A**: Fundamentally, when using a true random number generator (classical or quantum) the simulation results are not
bit-wise reproducible, i.e., two consecutive executions will yield slightly different outcomes**B**: An outcome of a stochastic simulation is deemed reproducible if - all parameters being equal - the re... | ABC | CAB | CAB | BAC | Selection 1 |
**A**: (2023); Freedman et al. (2023) formalized the teacher selection problem in RLHF, highlighting the need to query the most appropriate teacher for effective reward learning.**B**: Daniels-Koch and Freedman (2022); Barnett et al**C**: Teacher Selection.
RLHF typically aggregates preferences from multiple teachers (... | BAC | CAB | BAC | CBA | Selection 4 |
**A**: Under this setting, we achieve nearly optimal learning rates, introducing an additional factor that depends on the chain’s mixing time. Furthermore, we demonstrate that the i.i.d. case is a special instance of our more general results.**B**: Instead, we consider samples along a Markov chain trajectory with stati... | BAC | CBA | ACB | ABC | Selection 2 |
**A**: The change in what the expectation is with respect to in the equality in (1) is in part due to equivalence of expectations under a change of variables, as discussed and proven in [8]**B**: We remark this result does not rely on the use of the Jacobian. We enforce the prior distribution p𝑝pitalic_p upon the para... | BAC | CBA | CAB | BCA | Selection 4 |
**A**: In these cases, agents may only have access to the sampled points by few other agents, and thus datasets available to distinct agents may differ. We propose a distributed Thompson sampling algorithm for this constrained communication case, and provide theoretical guarantees for the algorithm.
**B**: Batch Thomps... | CAB | CBA | ABC | BCA | Selection 1 |
**A**: Our experimental results demonstrated that TNDP significantly outperforms traditional BED methods across a variety of tasks. By integrating decision-making considerations directly into the experimental design process, TNDP not only accelerates the design of experiments but also improves the quality of the decisi... | BAC | ACB | ACB | CAB | Selection 4 |
**A**: Fairness in GNNs has gained substantial attention, particularly in efforts to identify and mitigate biases associated with specific sensitive features (Zhang et al**B**: 2023; Luo et al. 2024d).**C**: 2024c).
Various fairness-aware GNN studies aim to preserve the independence of sensitive features through pre-pr... | CAB | ABC | ABC | ACB | Selection 4 |
**A**: (2018); Dai and Genton (2018); Masak and Panaretos (2023) and references therein), expanding RDPCA to these areas seems worthwhile. Calculating the RDMD involved regularizing by a suitable operator that smooths out unwanted noise components while keeping the relevant signal within the (uncontaminated) data unaff... | CAB | CBA | ACB | BCA | Selection 2 |
**A**: The Brakerski-Gentry-Vaikuntanathan (BGV) [19] and Brakerski/Fan-Vercauteren (BFV) [30, 20] schemes are based on the hardness of the Ring Learning with Errors (Ring-LWE) problem, which is a variant of the Learning with Errors problem tailored for rings. The BGV scheme was introduced to address some of the comput... | BCA | CBA | ABC | ACB | Selection 3 |
**A**: FPET2 errors (y-axis) plotted again FPET1 errors (x-axis)**B**: An error refers to the difference between a left-out DHS observation and the estimate for the indicator based on the validation data. A positive (negative) error indicates that the prediction based on data up to 2018 underpredicted (overpredicted) t... | ACB | BCA | ACB | ABC | Selection 2 |
**A**:
We assessed the approximation error associated with the s-MTJ devices and our theoretical framework. Findings show that the physical approximation error is negligible when sampling uniform random numbers**B**: It also allows the development of specialized solutions designed for specific algorithms and tasks in ... | ACB | CAB | CBA | ABC | Selection 1 |
**A**: [18], [17], [9], [10], [2], [3]).
In this regard it is more convenient to use the measure called Normalized Strength (borrowed from complex networks terminology, see [6], [1]), and that we define here by**B**: [19]**C**: A high measure of CB means that the competition is highly interesting since it is very diffi... | CAB | BAC | CBA | CBA | Selection 1 |
**A**: The analysis produced approximate samples from the posterior distribution of the parameter vector θ∗superscript𝜃∗\theta^{\ast}italic_θ start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT (see Figure 4 below).
**B**: The MCMC analysis was based on two chains of 3,000 iterations, with a burn-in period of 1,000 iterations... | BCA | BAC | ABC | CAB | Selection 4 |
**A**: This continuity property is explicitly incorporated into the definition of ridges for adapted harmonic signals [23] and plays a critical role in the development of ridge extraction algorithms [4, 5, 6, 19, 21, 22, 28, 30, 38, 41].**B**:
Traditionally, a ridge in a TFR is often understood in an “ad hoc” manner a... | BAC | BCA | CBA | CAB | Selection 4 |
**A**:
We also performed experiments on a 7×7777\times 77 × 7 gridworld with each state’s reward drawn from 𝒩(0,3)𝒩03\mathcal{N}(0,3)caligraphic_N ( 0 , 3 )**B**: States with reward above the 0.90.90.90.9 quantile of rewards are also terminal.**C**: Each state furthermore has a 10% probability of being terminal | ACB | BCA | CAB | BAC | Selection 1 |
**A**: NTD relies on the minimization of the KL divergence between the observed data and the low-rank approximation — a procedure which could perhaps allow it to better capture fine-grained patterns in the data, particularly when dealing with a larger number of topics**B**: However, this comes at a significant computat... | ABC | ACB | BCA | ABC | Selection 3 |
**A**: Continuous compositional distributions such as the Dirichlet distribution are often used to model compositional covariates**B**: This is formalized in the following corollary:**C**: These arise commonly when a continuous whole is partitioned, such as component fractions of a chemical solution or amount of time s... | ACB | ABC | BCA | CAB | Selection 1 |
**A**: The loss function is the cross entropy.
**B**: For this task, we used a simple version of bidirectional LSTM model using our token embedding to perform the classification**C**: The model architecture starts with an embedding layer using the pre-trained token embedding from either SA-Tweedie or GloVe, followed by... | BCA | ACB | BAC | CAB | Selection 4 |
**A**: (2021) survey existing NMF methods and their variants, analyzing their properties and applications. Saberi-Movahed et al**B**: Nonnegative matrix factorization (NMF) is particularly useful when dealing with non-negative data, such as in image processing and text mining. Gan
et al**C**: (2024) present a comprehen... | ACB | ACB | CAB | BAC | Selection 4 |
**A**: The STh has the smallest average number of selected genes in this case. Figure S.5(b) in the Supplementary Material presents the analysis for the Lung2 dataset with best performance achieved by OTh followed by the STh**B**: The last two data sets are for Lung cancer. The analysis of Lung1 dataset in Figure S.5(a... | BAC | CAB | ACB | CBA | Selection 1 |
**A**:
To regularize the estimation process and address potential issues of singularity in the second-moment matrix of individual-specific regressors, we apply individual-specific ridge regression**B**: This helps mitigate problems of nonidentification or near singularity in the data**C**: For each individual, we perf... | CAB | ABC | ACB | BCA | Selection 2 |
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