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With recent advancement in data collection capabilities and development on machine learning techniques, many traditional linear models have been extended to non-linear structures through various non-parametric approaches. Similar to [14], [20] considered the case-control data under a semi-supervised framework. They ext... | **A**: In the first step, an estimating equation is developed to estimate the marginal case proportional by utilizing the external summary information.
**B**: The proposed two-step estimation procedure is described in details.
**C**: The non-asymptotic error bound for the estimation error is derived.
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Among the other players displayed, Chris Smalling, primarily a defensive player for Roma, but he also demonstrates notable scoring ability, having netted several goals in the season under review. This dual aspect of his play is captured by our model, which assigns him a modest membership to the pure strikers profile, a... | **A**: Paulo Dybala, one of Serie A’s most technically skilled and elegant second forwards, often takes on ball-running duties in the forward area.
**B**: Edin Dzeko’s well-rounded abilities as a complete centre forward, blending technique with game vision, are reflected in his affiliation to the midfielders’ profile.... | BAC | BAC | BCA | BAC | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> Notably, “agecat” is consistently the first variable. <|MaskedSetence|> Besides, we observe that the tree structure of ZICPG3-BCART is quite similar to ZIP2-BCART. However, ZICPG3-BCART identifies another important variable “area”, which was recognized as important for average sev... | **A**: All models use the same splitting variables (“agecat”, “veh_value”, “veh_body”, and “area”), but the order of use and the tree structures vary.
**B**:
We again examine the splitting rules used in the selected trees.
**C**: Among them, ZICPG3-BCART demonstrates the ability to identify a riskier group (i.e.,... | BAC | BAC | BAC | ACB | Selection 3 |
<|MaskedSetence|> The expected free energy functional can be understood through its decomposition into a cross-entropy term between states and observations given action ("ambiguity"), and a Kullback-Leibler divergence between the posterior predictive and a goal prior distribution ("risk") [7, 18, 4]. We show that Gaus... | **A**:
Active inference agents are based on free energy functionals that rank policies on explorative and goal-directed behaviour [5, 7, 6, 16].
**B**: Under this model, the agent will avoid states where the non-linear measurement function curves strongly.
Our contributions are:
.
**C**: However, utilizing a second... | ACB | ACB | ACB | ACB | Selection 2 |
We applied the proposed eGene discovery method to adipose eQTL data from the METSIM study (Laakso et al., 2017; Raulerson et al., 2019). This dataset consisted of RNA-seq data generated on subcutaneous adipose tissue biopsies from 426 males from Kuopio, Finland. <|MaskedSetence|> (2019) primarily investigated adipose ... | **A**: While Raulerson et al.
**B**: The complete list of selected genes, ranked by their BJ statistics, is provided in the Appendix..
**C**: The RNA-seq data quality control, normalization, and covariates were described in Raulerson et al.
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<|MaskedSetence|> Although emulation techniques have previously been applied to predict the results of dark matter simulations [52, 19, 2, 27, 26, 16, 10, 57], there may be concerns that the emulated solutions might not match the truth if the initial conditions or cosmological parameters are “unusual,” i.e., unlike th... | **A**: [28] in the context of well-understood simple matter distributions that had not been seen during training.
**B**: One of the key motivations behind the COCA framework is the concept of ML-safety.
**C**: [13] found that their emulator performed well with initial conditions containing significantly less power th... | BAC | BAC | BAC | CBA | Selection 2 |
Section 3 examines the cure mixture formulation within a two-sample framework. <|MaskedSetence|> <|MaskedSetence|> [22]. This plot offers a clear interpretation of the relative treatment effect on the susceptible groups over time, which is preferred to a single number summary such as an average hazard rate or restr... | **A**: Beyond assessing long-term survivor outcomes by comparing differences in cure rates between treatment groups, we introduce a graphical estimand that captures the temporal impact of treatments on susceptible groups.
**B**: The proposed method is a modification of the approach originally developed by Tai et al.
... | ABC | ABC | CBA | ABC | Selection 1 |
Several methods exist to solve a Generalized Eigenvalue Problem. <|MaskedSetence|> <|MaskedSetence|> Indeed, the presence of this null space makes procedures like the well-known QZ-algorithm very unstable. So far, the available algorithms are not satisfactory as they might lead to complex and negative eigenvalues. F... | **A**: Among others, it exists the well-known QZ-algorithm introduced by [26] or the procedure described by [35].
**B**: However, in practice, solving a GEP of two scatter matrices with a common null space from a numerical point of view is particularly challenging.
**C**: In this section, we investigate three theoret... | ABC | ABC | ACB | ABC | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> (2021), we used the reference set DILIrank of known pharmacovigilance signals related to drug-induced liver injuries Chen et al. <|MaskedSetence|> It includes 203 negative controls (drugs known not to be associated with DILI) and 133 positives (drugs known to be associated with DI... | **A**: To assess the performance of our method, as in Courtois et al.
**B**: (2016).
**C**: When a variable is selected with a positive coefficient estimate, we considered it to be a pharmacovigilance signal.
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Another area of relevant research is to alter the use pseudolikelihoods used commonly in Markov random fields as opposed to traditional likelihoods [28]. These methods replace the joint likelihood of the observed covariates with conditional likelihoods of each of the covariates conditioned on the remaining values, whic... | **A**: First, gPCR includes a joint likelihood of the remaining covariates making guarantees for likelihood-based inference still applicable.
**B**: This can be done by using a neural network “encoder” to approximate the generative posterior then using sampled values from the encoder to evaluate the generative model. ... | ACB | ACB | ACB | ACB | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> Standard tools such as elbow method and Silhouette score do not yield a clear answer for the optimal cluster choice. <|MaskedSetence|> One of our ongoing work, is to make this choice in a more data-driven way. We also find that the mean of HTT values is robust with respect to the ... | **A**:
Figure 7: Amount of variance explained as a function of principal components.
**B**: In the current work, we choose 20 clusters as we think its best suited for our data.
**C**:
Choosing the number of clusters is less straightforward.
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<|MaskedSetence|> We then discuss multivariate polynomial approximation (§4), this being one of the main motivating examples for this work. The next two sections contain the core developments of this article. <|MaskedSetence|> We then show that sampling from a measure proportional to this function leads to provably n... | **A**: We describe the theory of least-squares approximation with random sampling and introduce the so-called Christoffel function, which plays a key role in its analysis (§5).
**B**: Once more, we see the Christoffel function plays a key role in analyzing the sample complexity.
**C**:
After a short literature revie... | CAB | CAB | CAB | CBA | Selection 2 |
We apply the generalized additive model (GAM) (Hastie and Tibshirani,, 1987; Hastie,, 2017) to study non-linear relationships between response, cause-specific mortality rate, and continuous covariates, including lockdown intensity level and age, in terms of smooth functions. GAMs have demonstrated considerable potent... | **A**: Alternatively, Clark and Wells, (2023) introduce dynamic GAMs where extra dynamic spatial random effects are incorporated into the mean of response variable, offering a solution to forecasting discrete time series while estimating relevant nonlinear predictor associations that conventional generalized linear mod... | CAB | CAB | BCA | CAB | Selection 2 |
Preference learning improves pass@n only when n is relatively small.
We plot the pass@n accuracy in terms of the number of candidate trajectories n in Figure 4. <|MaskedSetence|> We observe that the preference learning only improves the pass@n when n is relatively small. In particular, when n>16𝑛16n>16italic_n > 16,... | **A**: (2024), which studies the DRL-based GRPO method for the CoT mathematical reasoning task.
**B**: We expect that the final model performance can be further improved with more high-quality SFT data.
.
**C**: To evaluate the pass@n, for each question, we independently sample n trajectories, and the question is co... | CAB | BAC | CAB | CAB | Selection 1 |
The rest of the paper is organized as follows.
In Section 2, we introduce the basic settings of both discrete and continuous time cases. <|MaskedSetence|> The existence and the zero-variance property of the optimal twisting function / optimal control are also discussed in Section 2. <|MaskedSetence|> <|MaskedSetenc... | **A**: In Section 3, under suitable assumptions, we prove the convergence from the discrete-time model to the continuous-time model.
**B**: We also discuss the importance sampling for both models via the twisting function or control variate, respectively.
**C**: Based on the connection built in Section 3, and motivat... | BAC | BAC | BAC | CBA | Selection 2 |
The structure of this short note is as follows. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> In Section 4 we discuss risk premia and show that characteristics are covariances under certain conditions. In Section 5 we address Problem (ii). Using the counterexample, we also show some pitfalls with false impli... | **A**: In Section 2 we introduce the formal setup for conditional linear factor models and give an overview of our main results.
**B**: In Section 3 we address Problem (i) and derive related results based on the covariances.
**C**: We also give an example that serves as counterexample in some of our proofs.
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Table 1: Quantitative evaluation of temporal degradation tasks on the DAVIS dataset. Bold indicates the best results. <|MaskedSetence|> <|MaskedSetence|> All videos were normalized to the range [0, 1] and split into 16-frame samples of size 256×\times×256. <|MaskedSetence|> More preprocessing details are described i... | **A**: FVD is displayed scaled by 10−3superscript10310^{-3}10 start_POSTSUPERSCRIPT - 3 end_POSTSUPERSCRIPT for easy comparison.
We conduct our experiments on the DAVIS dataset (Perazzi et al., 2016; Pont-Tuset et al., 2017), which includes a wide variety of videos covering multiple scenarios.
**B**: The pre-trained... | ABC | ABC | ABC | CBA | Selection 2 |
With the recent advancement of Large Language Models (LLMs) (Brown et al., 2020; OpenAI, 2023), there has been a growing interest in using them for scientific discovery (AI4Science and Quantum, 2023). <|MaskedSetence|> Their potential is now studied in domains such as natural sciences (AI4Science and Quantum, 2023). D... | **A**: We leverage LLMs as proxy domain experts to propose new hypotheses in causal DAGs.
.
**B**: Previous work also proposed using LLMs as creative solution proposers with task-specific means of verifying said solutions (Romera-Paredes et al., 2023; Wang et al., 2023b; Qiu et al., 2024).
Figure 1: Scientific disco... | CBA | CBA | CAB | CBA | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> In Figure 3(a), the effect is most pronounced for the in-distribution MNIST test data, but the collapse is also clearly visible for out-of-distribution data (Fashion-MNIST & SVHN test sets). The decrease in mutual information indicates a reduction in the model’s epistemic uncertain... | **A**: However, the AUROC for OoD detection using different uncertainty metrics slightly improves as the model width increases, which is in line with the results by Fellaji & Pennerath (2024), who report a significant deterioration of OoD performance for such MLP ensembles trained on CIFAR-10 but not MNIST.
.
**B**: ... | BAC | BCA | BCA | BCA | Selection 2 |
<|MaskedSetence|> Our proposed approach, termed Meta Subspace Pursuit (Meta-SP), demonstrates efficacy in resolving such problems. <|MaskedSetence|> Through experimental validation, Meta-SP showcased superior accuracy and efficiency compared to existing methodologies, particularly in scenarios with exceptionally scar... | **A**: In this study, we introduced the concept of utilizing matrix rank minimization techniques to address challenges posed by multi-task linear regression problems with limited data availability.
**B**: This intriguing observation warrants further exploration and validation in future research endeavors.
.
**C**: Mo... | ACB | ACB | ABC | ACB | Selection 2 |
In this paper, we tested the hypothesis that low-dimensional structure can enable scalable estimation of MI from high-dimensional data. We introduced LMI approximation, which applies a nonparametric MI estimator to low-dimensional representations learned by neural networks. We quantified the effectiveness of LMI appro... | **A**: Our results suggest that nontrivial protein-protein interaction information is learned by ProtTrans5, motivating the development of interaction prediction tools based on pLMs.
**B**: LMI may similarly help identify dependence in cellular dynamics in other systems [39, 41, 42].
**C**: As the number of large pLM... | ACB | BAC | BAC | BAC | Selection 3 |
We apply the proposed regularized multi-output Gaussian convolution process model, referred as MGCP-R, to two simulation cases and one real case. In Section 4.1, we introduce the general settings and benchmark methods for our numerical studies. <|MaskedSetence|> Section 4.3 presents the effectiveness of our framework ... | **A**: Finally in Section 4.5, we apply the proposed modeling framework to the density prediction of ceramic product.
4.1 General settings.
**B**: And in 4.4, we test and verify the performance with a moderate number of sources and input dimensions.
**C**: Section 4.2 demonstrates the advantages of our method in r... | CBA | CBA | CAB | CBA | Selection 1 |
<|MaskedSetence|> Our preliminary experiments found that in some cases of adversarial attacks, Randomized Smoothing did not effectively provide defense as expected (See Case Study in Section 6.3). So, how can Randomized Smoothing performance be improved? Currently, there are two main schools of thought on improving Ra... | **A**:
However, the aforementioned works (yoon2022robust, ; liu2023robust, ; belkhouja2022adversarial, ) did not thoroughly evaluate the specific performance of Randomized Smoothing on various TSC architectures.
**B**: While our intuition is more straightforward, improving the performance of the base classifier by v... | ACB | ACB | ACB | ACB | Selection 2 |
This issue of misleading CIs is not isolated to our domain. <|MaskedSetence|> <|MaskedSetence|> Moreover, the propensity to overlook or underreport negative or null (non-conclusive) results further exacerbates the problem of biased interpretations. Borji (2017) argues for the importance of acknowledging and analyzing... | **A**: In their work, “Unbiased Look at Dataset Biases”, they identify and measure several biases such as selection, capture and negative set.
**B**: Belia et al.
**C**: (2005) have similarly criticized the common misinterpretations surrounding confidence intervals in broader scientific research.
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The rest of the paper is organised as follows. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Section 4 gives detailed proofs of all the results. Finally, in Appendix A, we collect some useful results from matrix analysis and concentration of measure which are used throughout the paper.
. | **A**: In Section 3 we state our main results and work out some examples.
**B**: We also provide brief proof sketches of our main results in this section.
**C**: In Section 2, we describe the model under consideration and recall preliminaries of random matrices.
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5 Empirical Example
To demonstrate the utility of the subgrouping multi-VAR framework, we present an empirical example using data from Fisher et al. <|MaskedSetence|> Data consisted of 40 individuals with a primary diagnosis of either major depressive disorder (MDD) or generalized anxiety disorder (GAD) who were ass... | **A**: For the purposes of the current application, we restricted our analyses to the 10 variables related to MDD (e.g., down and depressed) and GAD (e.g., worried) symptomatology, thereby ensuring that the number of variables examined mirrored those assessed in the simulation study.
**B**: (Fisher2017).
**C**: For m... | ACB | BAC | BAC | BAC | Selection 2 |
This approach favours hand crafting the causality graph but does not preclude using concepts and tools developed for automatic causality discovery. The use of FDR control during model discovery has been proposed by several researchers. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> These algorithms aim to le... | **A**: [2015]] apply techniques for controlling the multiplicity of hypotheses.
**B**: [2014]] & [Gasse et al.
**C**: For example, algorithms such as FDR-IAMB [Peña [2008]] and FDR-IAPC [Gasse et al.
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Recent advances in unsupervised representation learning, particularly through nonlinear Independent Component Analysis (ICA), have shown promising results in identifying latent variables by incorporating side information such as class labels and domain indices [10, 11, 12, 13, 14, 15, 16, 17, 18, 19]. <|MaskedSetence... | **A**: (More related work can be found in Appendix S5.) However, these methods face significant limitations; some are inadequate for modeling time-delayed causal relationships in latent spaces, and they rely on the Markov property, which cannot adequately capture the arbitrary nonstationary variations in domain variabl... | CBA | BCA | CBA | CBA | Selection 4 |
Once you understand the behavior of the data, you can then look at the classifiers. Table 1 presents the results of classic ML metrics for the 10 fine-tuned models with their respective ranking positions. <|MaskedSetence|> If the objective of this experiment was to choose the best model, with a brief analysis it is ... | **A**: Simply put, the recall metric represents how well a model was able to correctly classify True Positives instances, and in the case of a context-sensitive dataset that aims to correctly identify patients who have heart disease, the recall metric has great relevance.
**B**: If only recall is considered, the model... | CAB | CAB | CAB | BCA | Selection 1 |
In recent years, functional data have garnered increasing attention due to the continuous or intermittent recording of data at discrete time points. These data often exhibit non-linearity, and a common assumption is that they lie on a nonlinear manifold. <|MaskedSetence|> However, analyzing functional data poses chall... | **A**: These kernels, widely used in physical sciences, capture both smoothness and spatial correlations.
**B**: For instance, image data, which can be influenced by random domain shifts, are known to reside in such manifolds .
**C**: This novel framework enables modeling of vector fields on Riemannian manifolds.
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Beyond the superior performance of Mamba on this plurality of benchmarks, we also demonstrated its effectiveness on an important real-world application.
One of the most significant challenges in the field of Quantitative Systems Pharmacology is the prediction of parameters for known compartmental and non-compartmental ... | **A**: Such integration would facilitate the assessment of disparate treatment regimen outcomes for each patient based on a limited set of patient-specific features.
It is important to note that in the present study, a single PK model was used for all patients under the assumption that a specific drug was used.
**B**:... | BAC | BAC | BCA | BAC | Selection 2 |
In computer science and logic design, Boolean functions are basic, representing functions with binary outputs. <|MaskedSetence|> <|MaskedSetence|> While it is primarily used for assessing classifier complexity, directly computing the VC dimension can be challenging, especially in high-dimensional and complex models.... | **A**: The VC dimension, first introduced by Vapnik and Chervonenkis ([33]), measures the maximum complexity a model’s hypothesis space can handle, especially in classification tasks.
**B**: It serves as a theoretical guide for understanding a model’s learning and generalization abilities.
[8] introduced a novel gen... | CAB | CAB | CAB | CAB | Selection 3 |
6.2 Matrix regression in power systems
The admittance matrix containing all the electrical parameters of a distribution grid is often not known by the operators. <|MaskedSetence|> <|MaskedSetence|> Brouillon et al. <|MaskedSetence|> In mathematical terms, this means that one must maximize the likelihood. | **A**: (2022a) shows that the Bayesian EIV regression of the current on the voltage can produce sufficiently precise Maximum A Posteriori (MAP) estimates.
**B**: The resulting estimates must however follow some characteristics common to all distribution grids such as sparsity (Ardakanian et al., 2019).
**C**: Identif... | CBA | CAB | CBA | CBA | Selection 1 |
This section derives the asymptotic distribution of the difference between an estimator of impact that naively aggregates over assessment items and one that is highly robust to DIF. The former corresponds to the usual practice of estimating treatment effects using the unweighted mean over items. <|MaskedSetence|> The... | **A**: This is a practical advantage, since Hausman’s lemma is an asymptotic result that does not always obtain in finite samples (e.g., Wooldridge,, 2005, §10.7.3).
**B**: In the present context, the alternative hypothesis is less clear cut: the robust estimator will remain consistent up to a certain point, but both ... | CAB | CBA | CAB | CAB | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> Instead, their role is to reduce the bias sufficiently so that the bias correction by the first algorithm becomes effective. However, the use of the first algorithm within the second algorithm requires justification of the first algorithm in cases where quasi-prior is non-negligibl... | **A**:
In addition to utilizing the recently emerging Bayesian infinitesimal jackknife approximation (Giordano and Broderick, (2023)), the second algorithm is characterized by a hybrid approach that combines bias estimation and correction.
**B**: Preliminary analysis suggests that the success of this approach may dep... | ACB | BCA | ACB | ACB | Selection 3 |
2.1 Spatially-Variable Gene Detection
The field of spatial omics data analysis has grown rapidly in the last 10 years fueled by advancements in sequencing technologies and data availability [26]. The term spatial omics denotes spatially-resolved molecular measurements in general, including features extracted from the... | **A**: These features are greatly relevant as they give insights into fundamental biological processes.
**B**: continuous coordinates on a tissue sample or spots on a pre-defined grid..
**C**: Some of these transcripts later code for proteins, which are of great practical importance as they perform the actual biologi... | BAC | ACB | ACB | ACB | Selection 2 |
Current approaches for robust learning across various machine learning tasks often use gradient descent over a robust objective (see e.g. <|MaskedSetence|> These robust objectives tend to not be convex and therefore are difficult to obtain strong approximation bounds for general classes of models. <|MaskedSetence|> S... | **A**: Tilted Empirical Risk Minimization (TERM) (Li et al., 2021)).
**B**: Another popular approach is filtering, where at each iteration of training, points deemed to be as outliers are removed from training (see e.g.
**C**: Part of its appeal lies in its simplicity, as at each iteration of training we simply ignor... | ABC | ABC | ABC | BAC | Selection 2 |
The approach is extensible to cost functions with other functional forms suited to particular tasks.
Second, performing inference about the parameters of Bayesian actor models given behavioral data is computationally very expensive because each evaluation of the likelihood requires solving the decision-making problem. ... | **A**: Thus, we model the researcher’s uncertainty about the subject’s decision-making parameters explicitly..
**B**: We provide a statistical method for analyzing continuous responses.
Finally, by inferring what a subject’s decisions were optimal for instead of postulating optimality, we conceptually reconcile normat... | CBA | CBA | CAB | CBA | Selection 1 |
<|MaskedSetence|> If the low error region dominates, then the method is easily tuned to the domain, and adding new data with a slight distribution shift is not likely to make tuning substantially harder. <|MaskedSetence|> It is important to assess how the prediction error distribution (irreducible uncertainty in erro... | **A**: Visualization-aided dialogue with stakeholders makes sure that this distribution is compatible with the application at deployment time.
For the marginal error distribution: Distribution plot (histogram and KDE) of train/validation/test loss.
**B**: If high error regions dominate, then the method is potentially ... | ACB | CBA | CBA | CBA | Selection 4 |
The necessity of Latent EBM prior
To demonstrate the significance of the EBM prior model in posterior inference, we conducted research using the Spiral2D experiment from Chen et al. (2018). <|MaskedSetence|> <|MaskedSetence|> We train three models: Latent ODE, MCMC-based posterior inference without an EBM prior, and... | **A**: We generate 1000 samples each for training and validation, and 200 samples for testing.
**B**: The substantial improvement achieved by using the EBM prior underscores its importance.
.
**C**: The sequence length for training and validation is 100, while the test sequence length is 500.
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<|MaskedSetence|> [6], Rakhlin and Zhai [49], Haas et al. <|MaskedSetence|> <|MaskedSetence|> In Rakhlin and Zhai [49], the authors show that for input distributions on the unit ball, the Laplace kernel is inconsistent, even with varying bandwidth. In Haas et al. [25], the authors show that under different assumptio... | **A**: [26].
In Beaglehole et al.
**B**: [6], the authors show that there exists a specific data distribution for which the minimum norm interpolanting solution for a particular set of translation invariant kernels is not consistent.
**C**: The case of varying bandwidth has been considered in Beaglehole et al.
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<|MaskedSetence|> Computing at this many points using a high-fidelity model imposes a severe computational burden. <|MaskedSetence|> This implies that we can potentially construct reachable and observable subspaces with only a few interpolation points, which then raises the question of how to choose those few interpo... | **A**: To address this, we aim to develop an active sampling strategy in Section 3 that allows us to achieve our goal, hence reducing the computational burden drastically.
**B**: However, we assume that there exists a low-dimensional model that can describe the dynamics of the high-fidelity system well.
**C**:
Note ... | CBA | CBA | CBA | CBA | Selection 1 |
Comparing the results in Tab. 2 across different feature extractors, we make two remarks. First, the larger models generally outperform the base size models. This is an unsurprising result; overwhelmingly it has been found that larger transformer models result in more informative features and better performance in down... | **A**: This result is slightly more surprising considering that Oquab et al.
**B**: 2023; Roberts et al.
**C**: However, it is consistent with recent literature on using finetuned models for heterogeneous transfer learning (Dimitrovski et al.
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<|MaskedSetence|> For example, a team that employs the shift more often is not necessarily doing so because they are better (or worse) at fielding in a shifted alignment than in a standard alignment. <|MaskedSetence|> For example, teams with high shift rates in 2022 may have been more (or less) effective when deployi... | **A**: We calculated the two-stage least squares estimate for each year and then obtained the estimate for the ETT by taking the weighted average, with each year’s average weighted by its proportion of treated observations (Wang & Tchetgen Tchetgen, 2018).
.
**B**: However, this assumption may not be plausible witho... | BCA | CBA | CBA | CBA | Selection 3 |
We applied the method to a toy regression problem shown in Figure 1. The problem is a standard non-linear 1d regression task which requires both interpolation and extrapolation. The top-left figure was obtained by computing the kernel of the NTK-GP using formula (3) and computing the posterior mean and covariance usi... | **A**: The bottom-left figure was obtained by taking the first 5 eigenvectors of the kernel.
**B**: Details of network architecture are deferred to Appendix C.
.
**C**: The top-right figure was obtained by analytically computing the upper bound defined in appendix D.
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In MCTS, particularly within our NEMoTS framework, the simulation phase is key for assessing the potential rewards of newly expanded nodes. This phase typically follows the expansion phase and starts from the most recently added node in the expression tree. It involves a rapid simulation method, often random, and conti... | **A**: Instead, we utilize the policy-value network’s reward estimator for immediate reward estimations.
**B**: The focus here is on quick evaluation rather than deep exploration.
NEMoTS diverges from traditional random simulations, which are time-intensive.
**C**: This approach effectively evaluates the potential ... | BAC | BAC | BAC | BCA | Selection 3 |
Figure 3: The skeletal structure of a 2D GC. <|MaskedSetence|> The CMS is the blue curve connecting the two vertices (blue dots). <|MaskedSetence|> <|MaskedSetence|> (d) A valid model with high symmetricity. The model is close to violating the RCC.
. | **A**: (b) A model based on a smooth curve very close to the CMS is invalid as it violates the RCC.
**B**: (a) The approximation of the medial axis based on the Voronoi diagram.
**C**: (c) A valid model based on a slightly relaxed CMS, which is tidy but not highly symmetric.
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Our theoretical analysis of the inference for the regression-adjusted treatment effect estimators is related to recent studies, which have shown that under covariate-adaptive randomization, regression-adjusted treatment effect estimators are valid for inference. <|MaskedSetence|> [2018] proposed robust treatment effe... | **A**: With the inclusion of additional baseline covariates, stratum-common and stratum-specific treatment effect estimators have been proposed to improve the efficiency of the estimators [Ye et al., 2023, 2022, Ma et al., 2022].
**B**: [2020] for comprehensive surveys on transfer learning.
**C**: Bugni et al.
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<|MaskedSetence|> At the current stage of research, the Half-VAE still cannot directly solve ICA problems under underdetermined conditions. <|MaskedSetence|> <|MaskedSetence|> Thus, future work will also investigate the design of effective initialization strategies for both Half-VAE and VAE in solving ICA problems. ... | **A**:
However, several issues require further investigation.
**B**: Although the Half-VAE avoids explicit inverse mapping, solving underdetermined problems still requires more assumptions and constraints depending on the various conditions (Comon, (1994); Hyvarinen et al., (2001)).
**C**: Additionally, we found th... | ABC | ABC | ABC | BCA | Selection 2 |
IC1 has small scaled MASHAP values for all variables, which indicates that IC1 is more of a residual component. It still has some seasonal variability based on the temporal behaviour.
IC2 explains only the wind. It has some seasonal variability and high peaks in time irregularly and does not show any clear spatial beha... | **A**: The wind is not highly present in any other components meaning that wind might not share any common latent components with the other variables.
IC3 and IC5 together explain the most of the precipitation and humidity.
**B**: Evapotranspiration, maximum temperature and minimum temperature have high scaled MASHAP ... | ACB | ABC | ACB | ACB | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> To construct a bipartite graph in this setting we set the diversion units as buyers who receive the treatment, and sellers as outcome units.
Unlike in the literature published to date, one cannot know in advance which sellers the randomized buyers will engage with. Therefore to e... | **A**: Product engineering teams at Vinted routinely conduct experiments on buy or sell side of the marketplace, and would benefit from bipartite experiment design.
**B**: For example, buyers can view sellers’ listings, favorite them, send messages or offers, purchase items, or visit seller profiles.
**C**: For the r... | ACB | ACB | ACB | ACB | Selection 2 |
<|MaskedSetence|> In this work, we use a similar sampling rule to derive (with a simpler analysis) our upper bounds. <|MaskedSetence|> Proposition 5.5) or stabilizer states (c.f. Proposition 5.6) as well as all pure observables in the low accuracy regime (c.f. <|MaskedSetence|> | **A**: Remark 5.4).
.
**B**:
In [GPR23], an upper bound on the sample complexity in terms of the L1superscript𝐿1L^{1}italic_L start_POSTSUPERSCRIPT 1 end_POSTSUPERSCRIPT-norm is given for the problem of estimating the expectation values of observables in the Pauli model.
**C**: Moreover, we provide nearly matchin... | BCA | BAC | BCA | BCA | Selection 3 |
<|MaskedSetence|> Network calibration can be performed in conjunction with training (see e.g. [16, 17, 33]). Post-hoc scaling methods for calibration, such as Platt scaling [24], isotonic regression [31], and temperature scaling [9], are commonly employed. <|MaskedSetence|> <|MaskedSetence|> This is known as the dom... | **A**: Various methods have been introduced to address the issue of over-confidence.
**B**: These techniques apply calibration as post-processing, using a hold-out validation set to learn a calibration map that adjusts the model’s confidence in its predictions to become better calibrated.
The implementation of deep ... | ABC | ABC | ABC | CBA | Selection 3 |
<|MaskedSetence|> We use the term “intruder” for such a person, though no malicious intent is implied. <|MaskedSetence|> <|MaskedSetence|> scraping information from the web. The intruder first attempts to see if the individual with these quasi-identifiers is present (identity disclosure), and then to determine the v... | **A**: The identification of quasi-identifiers is an important aspect of DR assessment and a data holder may have to update decisions about this if new data sources are accessed that allow people to discover information about known individuals, e.g.
**B**: The intruder is assumed to have information for one or more in... | CBA | CBA | CBA | CBA | Selection 3 |
5 Pfaffian piece-wise structure for data-driven distributional learning
In this section, we propose the Pfaffian piece-wise structure for function classes, a refinement of the piece-wise decomposable structure introduced by Balcan et al. <|MaskedSetence|> <|MaskedSetence|> We argue that the additional information ca... | **A**: Additionally, we propose a further refined argument for the case where all dual utility functions share the same boundary structures, which leads to further improved learning guarantee.
.
**B**: [BDD+21].
**C**: Compared to their piece-wise decomposable structure, our proposed Pfaffian piece-wise structure in... | BCA | ACB | BCA | BCA | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> We extend Q-learning algorithm for index learning. It is two-timescale algorithm with constant stepsizes— faster timescale Q-learning is performed and slower scale index is updated. <|MaskedSetence|> The analysis is discussed with two timescale constant stepsizes approximations.. | **A**: Stepsizes are constant in both algorithms.
**B**: We discuss the Q-learning algorithm for MDP with action selection policies.
**C**: I-B Our contributions
Our contributions are as follows.
We consider Q-learning algorithm for a single-armed restless bandit (SARB).
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<|MaskedSetence|> While research on uncertainty quantification remains limited, some recent studies have addressed this area. Zhang et al. Zhang et al. (2022) estimated uncertainties in ECG classification using Monte Carlo dropout simulations. They also categorized predictions with uncertainty under a given threshold ... | **A**: (2020) used a variational encoder network to study uncertainty in classifying atrial fibrillation - a common type of arrhythmia, by conducting multiple passes of input through the network to build a distribution.
**B**: (2020); Muyskens et al.
**C**: These approaches and prior studies in ECG classification hav... | CAB | ACB | CAB | CAB | Selection 3 |
1. Manufacturing Process Control:- The CUSUM chart can very much be used for exercising control in production processes to detect small shifts or drifts in product quality characteristics such as weight, thickness, or other various dimensions. <|MaskedSetence|> <|MaskedSetence|> Chemical and Pharmaceutical Industry:-... | **A**: The multi-objective economic statistical design of the CUSUM chart helps in maintaining product consistency by detecting small shifts in the chemical mixture or reaction parameters and also helps in reducing the risk of costly recalls.
3.
**B**: The multi-objective economic statistical design of the CUSUM chart... | ABC | CBA | CBA | CBA | Selection 2 |
The forester is perfect for beginners, as they can easily analyze some tasks with just a few lines of code. This characteristic is extremely fruitful in the case of scientists from other fields. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> It is also visible in the Issues history, with almost 100 entries, ... | **A**: It is reflected by over 100 stars on the website, which represents the community gathered around the package.
**B**: Furthermore, they can also use the data check, or custom preprocessing modules without the need to explicitly use the tool for model training.
Additionally, the forester’s GitHub repository alr... | CBA | CBA | BAC | CBA | Selection 4 |
This paper investigates the impact of calibration methods on the performance of the double/debiased machine learning estimator in finite samples through a simulation study. We find that calibration methods can significantly improve the performance of the DML estimator when the propensity scores are difficult to estima... | **A**: This results are confirmed in an empirical application, where we find that calibration methods can be particularly relevant in small samples and do not hurt in larger samples.
Future research could investigate the use of calibration methods on the estimation of other treatment effects, such as heterogeneous tr... | CBA | CBA | CBA | CAB | Selection 1 |
There has been much work on generalizations of stable matchings, including weighted stable matchings and stable matchings in non-bipartite settings (see e.g., [78, 36, 72]). <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> In contrast, the utilities of a candidate for different institutions remain the same in ... | **A**: In this model, the correlation between the two utility values assigned to a candidate by the institutions depends on the group to which the candidate belongs.
**B**: In the context of bias,
[13] considered a stable assignment setting with two groups of candidates and two institutions where both sides have prefe... | BAC | BAC | ABC | BAC | Selection 4 |
In this work, we introduce a spectral estimator with local averaging and analyze its performance with an improved error upper bound. In addition, we sharpen the existing minimax lower bound. Together, our results settle the optimal statistical rate in well-conditioned cases. <|MaskedSetence|> <|MaskedSetence|> <|Mas... | **A**: Furthermore, the optimal rate shows that collaboration among clients always reduces overall sample complexity compared to independent local learning and further quantifies the benefits of transfer learning and private fine-tuning for new clients or tasks.
A key open problem is whether we can eliminate the depe... | CAB | CAB | ABC | CAB | Selection 2 |
While it is common in the literature to emphasise comparisons with the control and test hypotheses against it, this approach does not always align with the objectives of some multi-armed trials, such as selecting the best treatment arm. Rather than comparing each arm against the control, we aim at identifying the trea... | **A**: Under this assumption, competing treatment arms can be ranked from the best to the worst according to the proximity of their true mean responses to the target.
**B**: Indeed, the use of suitable parametric weight functions allows to comply with ethical constraints by informing entropy measures about which outco... | ACB | BCA | ACB | ACB | Selection 4 |
The learned representation of VGG19 is the output of VGG Block 4 defined in Table XVIII, and for ResNet-18 is the output of Residual Block 3 defined in Table XVI. <|MaskedSetence|> 14, which will be deliberated later. <|MaskedSetence|> Using the 2DSig-Norm, we compute the scores of these representations to identify... | **A**: Extracting the representations is crucial because it amplifies the backdoor attack signals, making them easier to detect.
**B**: This choice may help us generate high-level features [25] and is supported by evidence in Fig.
**C**: The detailed algorithm is shown in Algorithm 5.
.
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<|MaskedSetence|> Afterwards, we provide regularized linear models, logistic regression models and Bayesian linear models to overcome certain difficulties in e-sports. <|MaskedSetence|> We trained our models using the data of big event matches from 2018 to 2022. <|MaskedSetence|> This indicates that the rating mecha... | **A**: The ratings of players from Bayesian model and elastic net logistic model are not only correlated to the Plus/Minus value from 2018 to 2022 but also correlated to the Plus/Minus value in 2023.
**B**: These difficulties include variable selection and multicollinearity in player appearance.
**C**: Our study aims... | BAC | CBA | CBA | CBA | Selection 3 |
In this work, we study a PE problem that broadly involves clustering of arms and/or finding the arms whose distributions are matched. <|MaskedSetence|> We consider the fixed-confidence setting from a sequential multi-hypothesis testing perspective. <|MaskedSetence|> <|MaskedSetence|> Moreover, the unconstrained grou... | **A**: We assume that each cluster contains at least two arms, and the distributions of the arms in a cluster are identical.
The arms in the unconstrained group may or may not share the same distribution as one another.
**B**: We assume that arms follow unknown distributions that are supported on a common finite alpha... | BCA | ACB | BCA | BCA | Selection 1 |
To illustrate this, consider two models, A and B, both with an empirical mean Coverage of 95%. <|MaskedSetence|> In contrast, model B might produce intervals that are closer to the nominal level in most simulations but occasionally fail to cover the true treatment effect, resulting in a mean Coverage that masks these ... | **A**: However, model A might achieve this mean by consistently producing intervals that are too wide, leading to a higher-than-expected Coverage in some simulations and under-coverage in others.
**B**: Without additional metrics to capture these nuances, the empirical mean of Coverage can be misleading, suggesting th... | ABC | ABC | CBA | ABC | Selection 1 |
3 Results and Discussion
In Table 7 and Figure 5, we report and visualize summary statistics and the distributions of influence scores of trades with different attributes. The average trade has an influence score of 52.90, with a standard deviation of 70.70. The maximum influence score observed is 1601.27, and minim... | **A**: The maximum influence score observed is marginally higher for stocks at 1601.27, compared to 1550.34 for ETFs.
**B**: The average influence score for trades in windows where future negative action is predicted is 52.81, with a sample standard deviation of 65.69.
**C**: For trades in windows where future positi... | BCA | BAC | BCA | BCA | Selection 1 |
<|MaskedSetence|> This is reinforced by the fact that SEF has the highest PICP value in 7 out of 10 folds. <|MaskedSetence|> This is reinforced by the fact that SEF has the highest PICP value in 7 out of 10 folds and that in 3 folds PICP was equal to 1. <|MaskedSetence|> 0.422 of PIVEN) but with a significantly high... | **A**:
Here, too, the SEF method shows the best average PICP, 0.954, compared to 0.92 for PI3NN and 0.912 for PIVEN.
**B**: Again, the SEF method shows a better average PICP, 0.954 compared to 0.92 for PI3NN and 0.912 for PIVEN.
**C**: The SEF method shows the second smallest average NMPIW value (0.465 vs.
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Given a prior density, ensembling with anchoring provides a computationally affordable means to perform approximate posterior inference. A major challenge remains in designing a meaningful prior density in the high-dimensional, non-physical space of NN weights and biases. The choice of prior is particularly critical i... | **A**: [30] presents a VI-based scheme to learn priors that embed general domain knowledge and transfer this learned knowledge across NN architectures.
**B**: data.
**C**: The important task of designing priors for BNNs is currently receiving attention from both the engineering and machine learning research communiti... | CAB | CAB | ABC | CAB | Selection 2 |
In this paper, we have considered the Hopscotch and the Warships challenge in the Squid Games and its spin-off show. <|MaskedSetence|> <|MaskedSetence|> Although, we have stopped short of producing an optimal algorithm based on reinforcement learning, we have managed to get quite far within a simpler set-up. <|Mask... | **A**: We have explained how the probability of survival and of being the first over the bridge for the Hopscotch challenge can be easily evaluated using the binomial distribution.
**B**: Since the binomial distribution is often taught in first year introductory classes, this may be a nice example to engage students.
... | BCA | ABC | ABC | ABC | Selection 3 |
4 Efficient Structure Learning of MRFs from Dynamics
In this section, we provide our main result, Algorithm 1, and prove the correctness and the runtime guarantees of Theorem 1.2. In Section 4.1, we formally define the stopping times and establish a number of elementary probabilistic bounds on the occurrence of suit... | **A**: In Section 4.4, we explain why the guarantees extend to the setting where some subset of variables are unobserved.
**B**: Finally, in Section 4.5, we show that Algorithm 1 is essentially the simplest possible approach to recovering G𝐺Gitalic_G from dynamical samples in a slightly idealized observation model.. ... | BCA | CAB | CAB | CAB | Selection 4 |
<|MaskedSetence|> One is the non-real-time EA study design, where perceivers provide their response to stimuli after the stimuli have been conducted. <|MaskedSetence|> The other EA study design is the real-time assessment of perceivers’ empathy on an audio or video stimuli (i.e., the recorded affective states of targ... | **A**: The outcome of their overall empathy can be categories of emotion (e.g., happiness, anger, sadness, etc.) or extent of emotion on a Likert-type scale (Ekman,, 1992; Schweinle et al.,, 2002).
**B**:
There are two types of studies commonly used to examine EA.
**C**: Illustrated in Figure 1, social targets vary... | BAC | BAC | BCA | BAC | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> We analysed the arguments from [20] in some detail, as we find it important to explain how the authors of that paper arrived at their conclusion, and why this argument is incorrect.
We would like to add that the phrase ‘full-features’ is in a sense deceiving, because we do not al... | **A**: This directly contradicts the statement made in [20] that within the Bayesian paradigm, “one cannot use score based likelihood ratios”.
**B**: The more recent PPF6C kit contains 13 additional loci, so that calculating a likelihood ratio based on SGMPlus has become a score based likelihood ratio, as it comprises... | CAB | CAB | CAB | CAB | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> The simulated inventory policy follows a Newsvendor system. The safety stock is designed based on a certain target CSL, where such a CSL is estimated as the corresponding percentile of the forecasting demand distribution [21]. <|MaskedSetence|> The censoring level determines the l... | **A**: Then, the quantile produced by the point forecast plus the safety stock is the censoring level for the lead time (one day) and is continuously changing over time.
**B**:
3.3 Case 3: A Newsvendor inventory simulation
In this case, more complex examples originated from supply chain management are simulated, whe... | BCA | BAC | BCA | BCA | Selection 4 |
After reorganizing and reviewing external data, we referenced Civil Aviation Leisure Station 111Please refer to http://xmyzl.com/ for some insights. Among these 11 mainstream aircraft models in China, more than half are equipped with the same engine type. <|MaskedSetence|> <|MaskedSetence|> Different versions are po... | **A**: The PW4000 sub-type we encountered was the PW4170, which differs in thrust configuration from the Rolls-Royce Trent 772B-60.
**B**: It’s confirmed that the A330-300 is a twin-engine aircraft.
**C**: The Airbus A330-300, however, is notable for being equipped with multiple engine variants.
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<|MaskedSetence|> [36] proposed a supercomputer-accelerated approach to enhance the performance of LiNGAM causal discovery. The method leverages SIMD (Single Instruction, Multiple Data) instructions and MPI (Message Passing Interface) parallelization to overcome the computational complexity bottleneck of LiNGAM, which... | **A**: Subsequently, MPI parallelization is employed to distribute the computation across multiple nodes of the Fugaku supercomputer.
**B**: Evaluations on 96 nodes showed a 17,531-fold speed-up over the original Python implementation, completing 20,000-variable computations in 17.7 hours.
**C**:
MATSUDA et al.
| CAB | CAB | CBA | CAB | Selection 1 |
<|MaskedSetence|> Surprisingly, bootstrap consistency has never been proven, not even for the classical block-maxima method based on disjoint blocks. <|MaskedSetence|> A new approach, called the circular block-maxima method, has been proposed to allow for valid and computationally efficient bootstrap inference regard... | **A**: 8 Conclusion
Both the block-maxima method and the bootstrap are time-honored statistical methods that have seen wide use in applied statistics for extremes.
**B**: In this paper, respective consistency statements were established under high-level conditions on the data-generating process.
**C**: Respective me... | ABC | ABC | CBA | ABC | Selection 1 |
In the present paper, we propose a novel method that avoids the two above-mentioned issues. Given a structural model, it extracts the set of all constraints of conditional independence induced by the absence of links between pairs of regions in the model and tests for their validity in a Bayesian framework, either ind... | **A**: In other words, we use the relevance of the constraints associated with a structural model as a proxy for the relevance of the structural model itself.
**B**: Also, it deals with cyclic graphs, which are common in neuroscience/neuroimaging while being quite challenging from a graph-theoretic perspective.
**C**... | CAB | ACB | ACB | ACB | Selection 3 |
Confidence intervals based on the t-test and normal approximation for both trimmed and smoothly trimmed means can be imprecise when the data distribution is skewed and the sample size is small or medium. <|MaskedSetence|> In 2002, Qin and Tsao introduced the empirical likelihood method for the trimmed mean (Qin and T... | **A**: Combining these two ideas, we establish the empirical likelihood method for the smoothly trimmed mean estimator.
We provide simulations for several contaminated distributions.
**B**: In 2007, Glenn and Zhao (2007) introduced the weighted empirical likelihood method, which is applied to data that are independe... | BCA | CBA | CBA | CBA | Selection 3 |
Finally, \citeauthor10.1145/3437963.3441722 (\citeyear10.1145/3437963.3441722) addresses a critical issue in CATE estimation: the robustness to distributional shifts between training and testing data. <|MaskedSetence|> <|MaskedSetence|> \citeauthor10.1145/3437963.3441722 (\citeyear10.1145/3437963.3441722) provide the... | **A**: To tackle this, they propose the Causal Transfer Random Forest (CTRF), which combines existing training data with a small amount of data from a randomized experiment to train a model resilient to feature shifts and capable of transferring to new targeting distributions.
**B**: From a causal perspective, the cha... | BAC | CBA | BAC | BAC | Selection 3 |
This asymptotic variance can be decomposed into two additive terms, where the first term coincides with per-step variance, while the other term involves correlations between states across time periods.
In a setting where the state sequence is independent, the second term is zero, while it is not in any non-trivial MDP.... | **A**: This problem of estimating the asymptotic variance is a vital sub-problem in mean-variance policy optimization, for instance, as a critic in an actor-critic framework, cf.
**B**: For the discounted RL setting, a TD type algorithm for estimating variance has been proposed/analyzed in Tamar et al.
**C**: (2013),... | ACB | ABC | ABC | ABC | Selection 2 |
Prompt: "Q: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now?"
Chain of Thought: "Roger starts with 5 tennis balls. He buys 2 cans of tennis balls. Each can contains 3 tennis balls. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetenc... | **A**: Now, we add these to his original tennis balls: 5 + 6 = 11.
**B**: So, from the cans, he gets: 2 * 3 = 6 tennis balls.
**C**: Therefore, Roger now has 11 tennis balls.".
| BAC | BAC | BAC | BAC | Selection 4 |
<|MaskedSetence|> The empirical likelihood ratio test has been particularly valued for constructing confidence regions and hypothesis tests. <|MaskedSetence|> To overcome these limitations, Jing et al., (2009) proposed the JEL ratio test as a more robust alternative.
The jackknife method, known for reducing bias and ... | **A**: In this paper, we propose both a JEL ratio test and an AJEL ratio test for Cauchy distribution..
**B**: Recently, based on various characterizations, several authors, including Mahdizadeh and Zamanzade, (2017), Mahdizadeh and Zamanzade, (2019), Villaseñor and González-Estrada, (2021), and Pekgör, (2023), have p... | BAC | BCA | BCA | BCA | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> We will consider modular networks in which model parameters are divided into separate modules, each of which processes a projection of the input; monolithic (or nonmodular) networks in this context will correspond to networks without an explicit separation of par... | **A**: In our model, the number of training samples required to generalize on a task with m𝑚mitalic_m dimensional input scales exponentially with m𝑚mitalic_m.
**B**: Now, we demonstrate that modular NNs (in contrast to the monolithic NNs studied so far) can avoid this exponential dependence on m𝑚mitalic_m for tasks... | CAB | CAB | CAB | ACB | Selection 3 |
Double descent is an empirically observed phenomenon in which generalization error of machine learning models with respect to training data volume, model size, and training time exhibits an initial decrease, followed by a brief, sharp increase followed by a final decrease (Belkin et al., 2019; Nakkiran et al., 2021). ... | **A**: Typically, this work uses tools from random matrix theory to explain double descent for random feature models, in which linear regression maps a random, fixed feature pool to the desired output (Simon et al., 2024; Atanasov et al., 2024; Adlam et al., 2022; Bordelon et al., 2024; Maloney et al., 2022; Mei & Mont... | ACB | CAB | ACB | ACB | Selection 1 |
We validate our approach over a large collection of pretrained causal LLMs on the Hugging Face Open LLM Leaderboard (Beeching et al., 2023) and find that perplexity correlations are often predictive of an LLM’s benchmark performance. More importantly, we find that these relationships are robust enough to enable reliab... | **A**: In controlled pretraining experiments at the 160M parameter scale on eight benchmarks, our approach strongly outperforms DSIR (Xie et al., 2023b) (a commonly used training-free data selection approach based on n-gram statistics) while generally matching the performance of the best overall method validated at sca... | ACB | BCA | ACB | ACB | Selection 4 |
2.1. Mars Perseverance PIXL Data
The PIXL instrument aims to measure the mineral structure of small rock samples (called targets) on the surface of Mars contributing toward the larger inquiry towards any potential evidence of a history of life on Mars.
For each individual target on the martian surface multiple scans a... | **A**: This produces a single color image for each target with 4 primary channels, as opposed to the standard 3 channel RGB, and is often analysed using the 16 distinct ratios between them.
**B**: Each scan point is measured with a beam diameter of 50-200 microns111This beam diameter is energy dependent and since ther... | CAB | CBA | CAB | CAB | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> Recently, Few-Shot Learning (FSL) for time series prediction has gained attention both from theoretical [Iwata and Kumagai, 2020] and applied perspectives [Xu et al., 2024], to help mitigate the costs of training. This work is the start of a series of analyses on French regional sh... | **A**: For time series forecasting, GPs are well-adapted as they natively quantify uncertainty.
**B**: We take a FSL approach to training GPs, using a set of GPs trained on synthetic data in a first instance, with the natural next step being with actual electricity consumption data, available at https://www.rte-france... | ACB | BCA | ACB | ACB | Selection 4 |
To address these limitations, researchers extended the MAB framework by incorporating additional structures and complexities in order to be able to handle realistic scenarios. Examples of that are linear (Abbasi-Yadkori et al., 2011), continuous-action spaces (Kleinberg et al., 2008), and kernelized bandits (Chowdhury ... | **A**: In particular, we define a novel space of MABs called Graph-Triggered Bandits (GTBs).
**B**: In particular, restless bandits correspond to the case of a fully-connected graph, while rested ones correspond to the graph with the self-loops only..
**C**: Figure 1 shows an example of this scenario, where the nodes... | ACB | CAB | ACB | ACB | Selection 1 |
We also construct a probabilistic GC catalog using the J24 point source list. <|MaskedSetence|> Thus, we use a different method to obtain the probabilistic catalog from the one used for our data. In short, we consider a two-component parametric mixture model to cluster the color–magnitude data of sources that pass th... | **A**: As in the left panel of Figure 1, the color and size of points in the right panel represent the estimated probability and uncertainty.
The difference between the probabilities shown in Figure 1 for the two datasets is mainly due to identification of point sources in each approach.
**B**: Thus, at faint levels... | CAB | CAB | CAB | CAB | Selection 2 |
After making measurement decisions concerning proximity, researchers then proceed to specify linear or generalized linear regression models to explain a certain phenomena of interest. Sometimes regression models include dummy variables used to code observations as near or not-near landmarks or events (with dummy indic... | **A**: Examples of the second approach are Reny and Newman (2018) who include measures of cities’ spatial proximity to the nearest ‘Black growth city;’ and Newman and Hartman (2019) who include survey respondents’ spatial proximity to the nearest mass shootings over a period of time.
**B**: Unless interaction terms ar... | ABC | ABC | ABC | ABC | Selection 2 |
We organize this manuscript as follows. We present preliminary work in Section 2, putting the focus on Ville’s inequality and 2-smooth separable Banach spaces. <|MaskedSetence|> <|MaskedSetence|> We instantiate such a result to obtain empirical Bernstein confidence intervals for the batch setting, and confidence seq... | **A**: The key ideas that underpin the scalar Bernstein, scalar empirical Bernstein and multivariate Bernstein inequalities are then exhibited in Section 3.
**B**: Section 4 is dedicated to the statement and implications of the main theorem of the paper, a (empirical Bernstein-type) supermartingale construction for 2-... | ABC | ABC | ABC | ABC | Selection 1 |
<|MaskedSetence|> For a representative evaluation, we focus on multi-label prediction on graphs, and language model fine-tuning. In the first setting, each labeling task corresponds to a subgraph within a graph. Given a seed set of each labeling as the training set, the goal is to identify the remaining nodes of the s... | **A**: Each instruction corresponds to a prompt.
**B**: This can be cast as multitask learning, by viewing each labeling as a binary classification task.
**C**: We note that our algorithm applies to a wide range of multitask learning scenarios.
| ACB | CBA | CBA | CBA | Selection 4 |
<|MaskedSetence|> To quantify the accuracy of the fitted regression model through QQ-plot, we consider the measure of the average perpendicular distance of the plotted data, i.e. (quantile of the observed data, quantile of the predicted data), to the line with radiant 1111 and passing through the origin. <|MaskedSete... | **A**: In the QQ plots, the quantiles of the observed data are along the horizontal axis, while the quantiles of the predicted values are along the vertical axis.
**B**: Clearly, the proposed model gives a better fit to the data.
The proposed regression model achieves an AIC of 113.275113.275113.275113.275 and a BIC o... | ACB | ACB | BCA | ACB | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> For a given year, the predicted probabilities are then the fractions of ensemble members falling into the respective tercile. <|MaskedSetence|> In fact, this way of relating terciles of the ensemble forecast to climatology terciles constitutes a special case of statistical post-pr... | **A**: Tercile forecasts can easily be derived from ensemble forecasts, by comparing the ensemble forecast for a given year with the forecast climatology.
**B**: To be specific, for a given grid point and a given season, tercile boundaries are calculated from all ensemble members for this grid point and period, consid... | ABC | ACB | ABC | ABC | Selection 1 |
Similar qualitative behaviors and likelihoods have been obtained with other indices, such as the number of days in summer with temperatures exceeding 24°C, 26°C or 30°C (indices T24, T26 and T30), not allowing selection of one of these indices (index T26 even leads to better likelihood for the fitted period 2008-2019).... | **A**: the amount of infected rachises in the litter, show a quick and sharp transition from low to high levels within 1-2 years.
**B**: First, the dynamics of the inoculum, i.e.
**C**: Nevertheless, these temperature indices lead to the same estimation range for parameters (excluding the parameters of the temperatur... | CBA | CBA | ACB | CBA | Selection 2 |
We also apply our procedures to the two genomics datasets mentioned above. For the Mouse Aging Project, our test strongly rejects the hypothesis of proportionality of the 16 covariance matrices (the normalized test statistic is 13.999 to be compared with the standard normal under the null). The same dataset has also be... | **A**: A closer look reveals that their procedure in fact tests an independence hypothesis among the columns of the data matrix.
**B**: So there is no contradiction; rather, the Kronecker product covariance structure assumed in [37] is unlikely satisfied by this dataset.
The other application to the 1000 Genomes Pro... | CBA | ACB | ACB | ACB | Selection 4 |
<|MaskedSetence|> Recalling from Theorem 15 that the spaces of measure networks and measure hypernetworks isometrically embed into the space of partitioned measure networks, we recover from (26) the Fréchet functional on measure networks [18, 43] and measure hypernetworks [20] as special cases. <|MaskedSetence|> <|M... | **A**: [65]).
In practice, a stationary point of the functional (26) can be found via gradient descent on the space of partitioned measure networks using the “blow-up” scheme of [18] which progressively carries out alignment of network representatives as per Proposition 53.
**B**:
In Section 4.3, we introduced the ... | BCA | BCA | CAB | BCA | Selection 2 |
In this paper, we propose an innovative method for automatic road pavement classification from road satellite images. By means of new methodologies and using open-source software and data, roads of unknown surface are labeled as paved or otherwise unpaved. The analysis is conducted in view of low costs and high scalabi... | **A**: Li et al., 2019; Paulo et al., 2010).
**B**: In Riid et al.
**C**: Ragnoli et al., 2018).
| CBA | CAB | CAB | CAB | Selection 2 |
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