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A: ΣX⁢YsubscriptΣ𝑋𝑌\Sigma_{XY}roman_Σ start_POSTSUBSCRIPT italic_X italic_Y end_POSTSUBSCRIPT appearing in EqB: We choose the components of ΣX⁢YsubscriptΣ𝑋𝑌\Sigma_{XY}roman_Σ start_POSTSUBSCRIPT italic_X italic_Y end_POSTSUBSCRIPT with some degree of symmetry:C: (5) is 4×4444\times 44 × 4	ACB	CBA	ABC	BAC	Selection 1
A: However, they did not account for potential model errors in the closure equation which may arise due to the reasons discussed in the next paragraph.B: More recently, researchers performed model-consistent or CFD-driven training by involving the solver in the training processC: [38, 39] used gradient-free...	CAB	CBA	ACB	CBA	Selection 1
A: The approach is to tightly bound the maximum of the local test statistics (2) under the null of no change points, and use this bound to select an appropriate threshold λ𝜆\lambdaitalic_λ for (4)B: We impose the following assumptions on the minimum grid scale and on the noise components. C: As a starting ...	CAB	BCA	CBA	ACB	Selection 2
A: By optimizing this problem with the bottleneck objective formulation and the generalized Bellman updates, we demonstrate that the proposed approach is competitive and highly effective in the setting of multi-agent reinforcement learning. B: For the physical layer routing and spectrum access problem, we assig...	ACB	CBA	ACB	CAB	Selection 4
A: Figure 4: CelebA accessories datasetB: Images were centered on the face and then resized to 64×64646464\times 6464 × 64, pixels were normalized between 00 and 1111.C: We used a train set of 20000200002000020000 images (10000100001000010000 no accessories, 5000500050005000 glasses, 5000500050005000 hats)...	CAB	ACB	BAC	BCA	Selection 2
A: Our main results in Theorems 7, 8, 11 and 12 indicate that both CV and ML estimators can adapt to the unknown smoothness of f𝑓fitalic_f, but the range of smoothness for which this adaptation happens is broader for the CV estimator. Accordingly, the CV estimator can make GP uncertainty estimates asymptotically w...	ABC	CAB	ACB	BAC	Selection 4
A: The Chernoff bound is an influential statistical concept used to derive a stringent upper limit on the probability that the aggregate of random variables strays from its anticipated value. It holds a notable place in probability theory and statistics, and is especially pertinent when handling large sums of indep...	BCA	CAB	CBA	CBA	Selection 2
A: Due to page limits, the applications of our proposed algorithm are deferred to Appendix A. All the proofs can be found in the appendices. B: Organization. The rest of the paper is structured as follows. Section 2 provides preliminariesC: Section 3 proposes our multi-layer online ensemble approach for u...	ABC	CAB	BAC	BCA	Selection 2
A: All extracted features are outlined in the Supplementary InformationB: After extracting the features outlined in Sec.2.2 and Supplementary Information, we perform feature selection on the training dataset following the method described in Sec. 3.6C: To avoid poor performance on the test datasets due to o...	ACB	ABC	ACB	BAC	Selection 4
A: Kernel testing techniques are less sensitive to assumptions on data distribution than traditional methods, and can handle complex dependencies within and across cells. This capability is particularly relevant in the context of single-cell data, where inherent noise, sparsity, and heterogeneity pose unique challe...	ABC	ABC	CBA	CAB	Selection 3
A: We show that these changes bring improvements to state-of-the-art models on the majority of algorithms from the commonly used CLRS-30 benchmark [velickovic_clrs_2022, ibarz_generalist_2022]. B: The second weakness is that the model tends to struggle when encountering out-of-distribution values during algorit...	CBA	BCA	CAB	CBA	Selection 3
A: As an example of a recent paper on this subject, [6] introduced a new anomaly detection algorithm that searches for significant changes in the occupancy’s road traffic time seriesB: They showed that their proposed technique could effectively capture time series features and discover spatial and temporal patt...	ABC	ABC	CBA	ACB	Selection 4
A: Thus far we have only considered estimators of a single scalar quantityB: For example, consider the following generalisation of the partially linear model (6): C: In other situations, one may be interested in estimating several parameters simultaneously, and in such cases there are several modifications ...	ABC	BAC	ACB	CBA	Selection 3
A: In this work, we have introduced the SDSM-EC, an extension of SDSM that allows the user to specify edge constraints in the form of prohibited edgesB: We have demonstrated in both a toy example and an empirical example that the SDSM-EC performs as expected, correctly omitting weaker edges in the backbone tha...	ABC	CBA	CAB	ACB	Selection 4
A: While the method is straightforward, a critical point in the SVD-based approach is often overlooked: acquiring a basis for projection via SVD is only valid if the data matrix is centered. This detail is often bypassed by employing a folklore strategy to compute an extra base. B: A common choice of implementa...	CAB	CBA	BAC	BCA	Selection 2
A: Instability in the global FI values can lead to instability in their ranking [23] (an example is provided in Figure 1). A simple ranking of the features based on the FI values cannot reflect this uncertaintyB: Existing uncertainty measures are insufficient, because stakeholders often rely on the rank of the ...	CAB	ACB	ABC	BAC	Selection 4
A: The Omicron variant (B.1.1.529) was first identified in South Africa, and it sparked a travel ban to Canada in November 2021 (CDC COVID-19 Response Team, 2021). As seen in Figure 9, the first case of the COVID-19 Omicron variant was confirmed on Nov 30, 2021 in British Columbia. It caused an explosion of active ...	ABC	CBA	CAB	ABC	Selection 3
A: This article proposes a new statistical methodology: the spatial autoregressive graphical model. The methodological novelty arises from the method’s capacity to learn multivariate asymmetric between-location effects, combined with the capacity of illustrating complex within-location effects through a conditiona...	BAC	BAC	ABC	CBA	Selection 3
A: Moreover, the estimator Ψ^^Ψ\hat{\Psi}over^ start_ARG roman_Ψ end_ARG has been studied before in the context of partially linear models (Robinson,, 1988) and nonparametric estimation of GLM coefficients (Vansteelandt and Dukes,, 2022). Its properties are an active research topic with regards to the smoothness an...	ACB	BCA	BCA	CBA	Selection 4
A: Shrinkage causes systematic underestimation of large true effects and overestimation of small true effectsB: In contrast to the RMSE, higher σ𝜎\sigmaitalic_σ values result in better ISELC: While this generally improves RMSE, which does not differentiate between overestimation and underestimation, exces...	BAC	ACB	ACB	CAB	Selection 1
A: An essential advantage of DeepONet, shared by many ML-based surrogate models, is its remarkably fast execution speed compared to conventional simulationsB: This remarkable speed makes DeepONet a potent modeling method for digital twin systems, enabling real-time predictions based on data from sensors install...	BAC	ABC	CBA	ACB	Selection 4
A: We begin in Section 2 by describing how collections of models may be endowed with the structure of a partially ordered set (poset)B: Posets are relations that satisfy reflexivity, transitivity, and antisymmetry, and they facilitate a hierarchical organization of a set of models that leads to a natural defin...	CBA	CAB	BAC	ABC	Selection 4
A: Hence, the continuation method then becomes a technique to follow along the Pareto set in two steps; a) a predictor step along the tangent space of the Pareto set which forms in the smooth setting a manifold of dimension m - 1 (where m𝑚mitalic_m is the number of objective functions) [13] and b) a corrector step...	BCA	BCA	CAB	BAC	Selection 3
A: The Bayesian approach seems to be more subjective in the formulation than frequentistic. However, the way the Bayesian framework (BF) is defined allows a user to control the subjectivity of assumptions. From that perspective, Bayesian statistics can be used to study the impact of the model assumptions on the ana...	BAC	ABC	CBA	BAC	Selection 3
A: This adds, at worst, O⁢(M2)𝑂superscript𝑀2O(M^{2})italic_O ( italic_M start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT ) computation to each step.B: We have so far assumed that the spectral density is available in closed formC: However, we only need regularly spaced point evaluations of the spectral density...	CAB	BAC	CBA	ABC	Selection 1
A: To prove regret lower bounds in bandits, we leverage the generic proof ideas in [2]. The main technical challenge in these proofs is to quantify the extra cost of “indistinguishability” due to DP. This cost is expressed in terms of an upper bound on KL-divergence of observations induced by two ‘confusing’ bandi...	BCA	CBA	CBA	ACB	Selection 4
A: (2014); Lattimore and Szepesvári (2019a, b)B: Our results on minimax regret rates are developed based on the theory for general finite partial monitoring games developed by Bartók et alC: Before presenting our results, it is necessary to reproduce the relevant definitions and notations as in Bartók et al...	ACB	BAC	CBA	CBA	Selection 2
A: (b) Embedding pattern. (c) OM indicator(red) compared with the Std(green)B: Figure 8: The out-of-sample points validate the effectiveness of our framework. (a) Validation datasetC: (d) SE indicator(red) compared with the Std(green). (e) The transition probability.	ACB	ACB	BCA	BAC	Selection 4
A: The definition is inspired by \citetmoitra2010settlingB: We set the distance between two mixtures with different number of components to be ∞\infty∞. Otherwise, the distance between two mixtures is the distance between their farthest components.C: Below, we define the component-wise distance between two...	BCA	BAC	ACB	ABC	Selection 1
A: In this section, we first showcase the capability of the CDRL in estimating the density of a 2D checkerboard distribution. Experimental results are presented in Figure 3, where we illustrate observed samples, the fitted density, and the generated samples at each noise level, respectively. These results confirm C...	CBA	ACB	BCA	ACB	Selection 1
A: Generalisations and extensions to metric learning algorithms have also been well-studied. We refer the reader to the surveys in [12, 13] for a more detailed review on metric learning algorithmsB: Mahalanobis metric learning was introduced in [2] and has attracted a significant amount of research since. Short...	BCA	CBA	BAC	ABC	Selection 3
A: Figure 1: Bounds using linear quantile regressionB: Our estimates are generally close to median unbiased. Once c≥0.07𝑐0.07c\geq 0.07italic_c ≥ 0.07,C: Average bound estimates (solid line), median bound estimates (dotted line), and true bounds (dashed line) in our 1,000 simulations	BCA	CAB	ACB	BCA	Selection 3
A: It is known that the successful training of Transformer requires a large-scale dataset with labeled data, e.g., ImageNet-21k (Deng et al., 2009), due to the lack of inductive bias (Dosovitskiy et al., 2020)B: However, creating labeled ECG datasets at scale is challenging since accurate annotation requires me...	BCA	ACB	BAC	BAC	Selection 1
A: Its proof is given in Section 4B: In this generality, Theorem 2.1 has not yet appeared in the literature, at least to our knowledgeC: For the parameter range 0<s≤10𝑠10<s\leq 10 < italic_s ≤ 1, the exponent −s/(2+s)𝑠2𝑠-s/(2+s)- italic_s / ( 2 + italic_s ) in the right-hand side of (17) is best possibl...	CAB	BAC	ACB	ACB	Selection 2
A: In this paper, we introduced Heckman-FA, a novel data-driven approach that obtains an assignment of prediction features for the Heckman selection model to robustly handle MNAR sample selection biasB: Heckman-FA finds prediction features for the Heckman model by drawing a number of Gumbel-Softmax samples usin...	BCA	ABC	ACB	ABC	Selection 3
A: SSVS is a Gibbs sampler that tries to flip each binary inclusion variable one at a time from a Bernoulli full conditional distribution; at the end of each iteration, it samples the regression coefficients given the vector of binary inclusion variables. As an alternative to using the marginal posterior, the Ortho...	BCA	ACB	BAC	CAB	Selection 3
A: We then in Section 4 develop the MMA’s AOP theory in the nested setup. The non-nested setup is addressed in Section 5. Section 6 shows the minimax adaptivity of MMAB: Section 7 presents the results of simulation experiments. Concluding remarks are given in Section 8. The discussions on the other related work...	BAC	CBA	BCA	ACB	Selection 3
A: Such experiments are time consuming and cost tens of thousands of dollars per donor, so narrowing possible future directions to a small set of genes is of the utmost importanceB: Critically, RID also found very low importance for the majority of variables, allowing researchers to dramatically reduce the num...	BAC	ACB	ACB	BCA	Selection 1
A: However, in such scenarios, the optimal MA risk within the standard constraint does not represent the optimal performance. The relative risks of SMA1 and SMA2 fall below one across various cases when n=1000𝑛1000n=1000italic_n = 1000, owing to their targets on the optimal risks within the relaxed weight sets. As...	CAB	ACB	CBA	CBA	Selection 1
A: Software is written using MATLAB object-oriented programming. Full specifications of classes, properties and methods are therefore provided, and available for user inspection.B: The covXtreme is available for download from GitHub at Towe et alC: (2023b)	BCA	BAC	CAB	ABC	Selection 3
A: Datasets and Models. We perform experiments on synthetic toy data as found in Grathwohl et alB: For deep generative models, we conduct experiments with continuous normalizing flows (Chen et al., 2018) constructed using a conditional flow-matching loss (CFM (Lipman et al., 2022; Tong et al., 2023)) and two po...	ABC	ABC	ACB	BCA	Selection 3
A: Second, the topology selection embeds the discrete open/close decision of the switches using the physics-informed rounding. Third, we use the power flow equations to predict a subset of variables (denoted as the independent variables), and compute the remaining variables in a recovery step. The GraPhyR framework...	CAB	CBA	BCA	ACB	Selection 2
A: It is a fully nonparametric approach, applicable to all continuous loss distributions without any moment restrictionsB: This article proposes a graphical method for assessing the validity of a Pareto model, especially with respect to the tails of the distributionC: This is in sharp contrast to the mean ...	BAC	ACB	BCA	CBA	Selection 1
A: CTM, a novel generative model, addresses issues in established modelsB: With a unique training approach accessing intermediate PF ODE solutions, it enables unrestricted time traversal and seamless integration with prior models’ training advantagesC: A universal framework for Consistency and Diffusion Mod...	BCA	ABC	BCA	CBA	Selection 2
A: We find a consistent absolute increase in test accuracy over the performance of the smaller anchor network, thus implying successful knowledge transfer (Tab. 4)B: These results showcase that our method is an effective and efficient alternative to knowledge distillation. C: Our methodology, as opposed to ...	CAB	ABC	BCA	CBA	Selection 3
A: Without convexity and dissipativity assumptions. Note also that often, when dealing with Langevin MC, the authors consider the convex/monotone setup (Dalalyan, 2017; Erdogdu et al., 2018; Durmus & Moulines, 2019; Li et al., 2019b; Chatterji et al., 2020; Xie et al., 2021), which is possible and relevant, but at...	BAC	CBA	BCA	ACB	Selection 4
A: Previous explanations of grokking rely on weight norm decreases at late time (Liu et al., 2022a; Varma et al., 2023)B: Weight norm of parameters increases during training (Figure 2(a)), so grokking cannot in general be explained by theories of weight decay. C: In Figure 2, we show a simple example of a m...	ACB	CBA	CBA	CBA	Selection 1
A: On the contrary, the last four classes represent only 39 samples or correspondingly 3.4% of the full dataset. Furthermore, it is important to mention that there are no male samples available for the age groups 75-79 years and 85-92 years. As past studies did not indicate a strong interaction effect between gende...	ABC	CAB	ABC	CBA	Selection 4
A: This guarantees that the algorithm does not blow up or remain static at any instance. The second part of Assumption 2 implies that the change in the preconditioning matrix is upper boundedB: The intuition for this is that The exclusion of the two bounds means the condition number of the preconditioning matri...	BCA	CAB	ACB	BAC	Selection 1
A: Alternative choices would include basing step sizes on conditional distributions, or tuning them for agreement with f𝑓fitalic_f. Our goal has been to examine a straightforward control variate method with minimal costs both computationally and in human effort, so we have not pursued these variations here. We spe...	ABC	BAC	CAB	ABC	Selection 3
A: These techniques include compressing models and gradients [7, 8, 9, 10, 11, 12, 13], using accelerated scheme [14, 15, 16, 17, 18, 19], and implementing local updates [20, 21, 22, 23, 24]B: By applying these strategies, it is possible to reduce the amount of information exchanged between different nodes duri...	BCA	CAB	CBA	CAB	Selection 1
A: Replicability analysis shares the composite null structure with mediation analysis but has a distinctive feature of information borrowing across different studiesB: Fithian (2020) and Bogomolov and Heller (2013) examine replicability in a two-stage analysis with a primary study and a follow-up studyC: Re...	CBA	CBA	BCA	BAC	Selection 4
A: Indeed, analytic maximization of our function l𝑙litalic_l over a well-chosen class of priors constitutes an easier path to express reference priors that might differ from JeffreysB: Additionally, we draw attention to the open-ended nature of the result we express in Theorem 1C: The following section su...	ACB	BCA	ABC	BAC	Selection 4
A: The parameters are then updated in the optimal direction using the ADAM[37] optimization algorithmB: The training strategy proposed in this work allows the MZ-AE linear operator to learn the dynamics to its maximum potential while constraining the memory model to the residual dynamicsC: Further, minimiz...	CBA	ABC	ACB	BCA	Selection 2
A: Our first bound applies to VaR and CVaR of the infinite-horizon discounted cost, while the second applies even to its mean and variance. B: In either case, our bound is order optimal and the first of its kind for risk estimationC: Lower bounds: We derive a minimax sample complexity lower bound of Ω⁢(1/ϵ2...	ABC	CBA	ACB	CAB	Selection 2
A: We will first recall one of the main results of Li et alB: (2022), which is stated informally111The statement is “informal” in the sense that we have stated what the final limit is, but not the precise sense of the convergence. See Appendix A for a rigorous treatment of the convergence resultC: below.	BAC	ABC	CBA	BCA	Selection 2
A: According to Panel A, models 3 and 4 are better when sample size is less than 250250250250B: The actual estimation errors (Panel A) and model selection frequencies (Panels BD̃) for this example are presented in Figure 2C: As more samples are being processed, models 1, 3, and 6 perform better.	BCA	CBA	ABC	BAC	Selection 4
A: The first two models we consider are epidemic models, namely the SIR and SEIAR model, the former being a simple but ubiquitous model in epidemiology (Allen, 2017), and the latter being a more complex model which admits symptomatic phases (Black, 2019)B: The third example is the predator-prey model described ...	BCA	CAB	CAB	ABC	Selection 1
A: The learned hypothesis h∈ℋℎℋh\in{\mathscr{H}}italic_h ∈ script_H corresponding to those two stages can be expressed as h=(h𝒴,hn+1)ℎsubscriptℎ𝒴subscriptℎ𝑛1h=(h_{{\mathscr{Y}}},h_{n+1})italic_h = ( italic_h start_POSTSUBSCRIPT script_Y end_POSTSUBSCRIPT , italic_h start_POSTSUBSCRIPT italic_n + 1 end_POSTSUBSCR...	CAB	CAB	BCA	BAC	Selection 3
A: By Theorem 4.12, underB: whether abstention occurs or notC: By applying the Lebesgue dominated convergence theorem, we show that ℰℓΦ,h^⁢(r^)=0subscriptℰsubscriptℓΦ^ℎ^𝑟0{\mathscr{E}}_{\ell_{\Phi,\hat{h}}}(\hat{r})=0script_E start_POSTSUBSCRIPT roman_ℓ start_POSTSUBSCRIPT roman_Φ , over^ start_ARG italic_...	CAB	CBA	ABC	BAC	Selection 1
A: Appendix C.1)B: Consequently, we have ℳℓ2⁢(ℋ)=𝒜ℓ2⁢(ℋ)subscriptℳsubscriptℓ2ℋsubscript𝒜subscriptℓ2ℋ{\mathscr{M}}_{\ell_{2}}({\mathscr{H}})={\mathscr{A}}_{\ell_{2}}({\mathscr{H}})script_M start_POSTSUBSCRIPT roman_ℓ start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT end_POSTSUBSCRIPT ( script_H ) = script_A start_POSTSU...	CAB	ABC	CAB	BAC	Selection 2
A: Furthermore, it is worth to highlight that this is the first study to achieve fast rates in imbalanced classification, marking a significant advancement in the field. Our findings confirm that both risk-balancing approaches and cost-sensitive learning are consistent across nearly all imbalanced classification sc...	ACB	CAB	BAC	ABC	Selection 2
A: Representation-based theories such as Davies et al. (2023), Barak et al. (2022), Nanda et al. (2023) and Varma et al. (2023) claim that grokking occurs as a result of feature learning (or circuit formation) and associated training dynamicsB: Finally, there are a set of theories which use the neural tangent k...	ABC	ACB	ABC	BCA	Selection 4
A: For future research, we have identified several intriguing directions:B: In this paper, we have introduced a weighted training approach designed to address the interference problem caused by data training loopsC: Our approach has demonstrated the capability to achieve low bias and reasonable variance	BCA	BCA	CAB	ABC	Selection 3
A: Additionally, proper surface preparation prior to spraying is necessary to ensure maximum adhesion strength and achieve the desired coating performance characteristicsB: In order to obtain best wear resistance, corrosion resistance, or thermal insulation, a specific combination of coating properties is essen...	BAC	CBA	BCA	ABC	Selection 1
A: (2017); Pawelczyk et alB: Wachter et alC: (2021, 2022) explore this concept. Their work particularly emphasizes the importance of generating not only accurate but also plausible counterfactuals, which builds trust in the model’s decision-making process.	BCA	CAB	ACB	BAC	Selection 4
A: It can be seen how the best results for this case are always considering all the data, except for the EPEX-FR market, where they are practically the same. However, the results with small calibration windows are not positive, being very distant from the rest of the configurations. However, this is inline with the...	BCA	CBA	CBA	BAC	Selection 1
A: Multivariate multiscale-based representations were accordingly devised for the estimation of the resulting vector of selfsimilarity parametersB: ItsC: Nonetheless, these constructs were essentially based on component-wise univariate power law behavior [21, 19]. More recently, a richer model for multivari...	CBA	CBA	ACB	ABC	Selection 3
A: Our methods relied on a novel boundary for centered partial sums that is uniformly valid in a class of distributions, in time, and in a family of boundariesB: We gave a definition of “distribution-uniform anytime-valid inference” as a time-uniform analogue of distribution-uniform fixed-n𝑛nitalic_n inferenc...	ABC	BAC	ABC	ACB	Selection 2
A: For example, item popularity can generate popularity bias and is considered a confounding factor. As causal inference has become a popular approach to eliminate bias in recommender systems and examine the relationships between variables [13], researchers have focused more on the challenge of confounding bias. Co...	CBA	CAB	ACB	ACB	Selection 1
A: Sherwood and Wang, (2016) analyzed the same data using a semiparametric quantile regression model at τ=0.1𝜏0.1\tau=0.1italic_τ = 0.1, 0.30.30.30.3 and 0.50.50.50.5 and reported covariate selection resultsB: To be more specific, for each quantile, Sherwood and Wang, (2016) first applied QaSIS of He et al., (...	BAC	ABC	ACB	CAB	Selection 2
A: The real Wishart case from Theorem 3.2 was already studied by Pielaszkiewicz, Von Rosen and Singull in [WishartRecursion], though they made a minor error in their formula. To the best of our knowledge the results of Theorems 3.1 and 4.1 for the complex Wishart ensemble and the GOE respectively are entirely new.*...	BAC	CBA	BAC	ACB	Selection 2
A: Thus, the description of the dynamical equation given by Eq. (6) on the energy surface given by Eq. (5) is straightforward: one starts with the random (with no prior information) coordinates, which corresponds to either the basin of the planted pattern or spurious state, and then follows the gradient descent unt...	ABC	BAC	ACB	CBA	Selection 1
A: In the geochemical application, we calculated the average scaled MASHAP values of the latent components of spatial iVAE’s mixing function estimate, and discovered that fewer components might be sufficient to model the datasetB: Hence, in future work, procedures for estimating the dimension of the latent repr...	CAB	BCA	ACB	CBA	Selection 3
A: Osband’s principle can be used to create new MK divergencesB: Indeed any strictly monotonic transformation of an elicitable risk functional leads to a new MK divergence, where the optimal coupling follows from Proposition 3.17C: Here, we give an example of the reciprocate of risk functionals.	CAB	BCA	ABC	CAB	Selection 3
A: In Section 2 we show that the relative error used to learn the kernel in the original version of Kernel Flows can be viewed as a log-likelihood ratioB: In Section 4 we establish the link between MDL and KFs, and Section 5 concludes.C: In Section 3, we give a brief introduction to AIT and introduce Kolmog...	CAB	ACB	CBA	BAC	Selection 2
A: Both the KL and PL settings have been the subject of numerous works [36, 25, 44]B: Furthermore, an increasingly rich literature has shown that overparameterized models such as neural networks satisfy the KL condition in a number of scenarios [6, 18, 41, 44]. C: The KL condition, in addition to being weak...	CAB	ABC	BAC	ACB	Selection 4
A: But in the air pollution context we do not expect the same coordinates in different cities to be the most polluted — e.gB: Therefore, we expect prior transfer to work well in situations unsuited to direct transfer, such as air pollution monitoring. C: three kilometres north of the city centre	ACB	CBA	ABC	BAC	Selection 1
A: We exploit the arbitrariness of choice in the Riemannian metric relative to which the gradient flow can be defined, both on the side of the parameter space, and in the output layer of the DL networkB: Choosing the gradient flow with respect to the Euclidean metric in the output layer of the DL network (inste...	ABC	BCA	BAC	CBA	Selection 1
A: (2023b) provided bounds for different treatment types (i.e., binary, continuous) and causal queries (e.g., CATE, distributional effects but not multiple outcomes). Yet, the results are limited to MSM-type sensitivity models. B: Extensions under the MSM have been proposed for continuous treatments (Jesson et...	CBA	BCA	CAB	ABC	Selection 1
A: This is achieved through a Bayesian nonparametric framework where a multiplicative truncated gamma process (MTGP) prior is assumed on the variance of the latent positionsB: This approach eliminates the requirement to fit multiple LPMs and then choose between them using model selection criteria to identify th...	CBA	ACB	CAB	BAC	Selection 2
A: Zhou et alB: (2016) apply to the multinomial belief network, we start by summarising and reviewing their method in some detail. We stay close to their notation, but have made some modifications where this simplifies the future connection to the multinomial belief network.C: Since many of the techniques ...	CAB	ACB	ABC	BCA	Selection 4
A: There have been few provable bounds on approximating average-case distortion style objectives for dimensionality-reduction problemsB: Bartal, Fandina and Neiman [BFN19] give provable approximation guarantees for a several variants of multi-dimensional scaling, the closest of which to our setting, i.eC: t...	ABC	CBA	BAC	CAB	Selection 1
A: It should be noted that this network is not necessarily derived from physical reality, since it only represents correlations between brain regionsB: This is why the term “functional” is coined. In this study, the connectivity matrices are defined on a parcellation of the brain into n=25𝑛25n=25italic_n = 25 ...	CBA	CBA	CAB	BCA	Selection 4
A: For all levels of censoring, correlation and historical covariate use, excluding the highest correlation level with historical covariate use, Model A (the ad hoc nonlinear modified XMT model) has the best C-index with varying degrees of superiority to Model B (the main effects modified XMT model) and RFRE.PO me...	ABC	BCA	BCA	CBA	Selection 1
A: (2023) that continuous-time diffusion loss is invariant under choice of schedule may not hold in practice. Based on this result, we introduce a novel regularization term that proves to be critical for the performance of the method in our general formulation.B: We prove that the rate of convergence of the di...	BCA	ACB	BCA	CAB	Selection 4
A: We leave this topic for future research. B: This issue can be mitigated by adopting survey methods to promote accuracy and honesty [24] and can also be examined by a sensitivity analysisC: While this may not be an issue for mandatory testing such as case contact tracing, bias may occur with self-reportin...	ABC	ACB	CBA	ACB	Selection 3
A: This experiment is used to evaluate the model’s ability to be used for different downstream tasks and evaluate whether the trained encoder can correctly identify the cluster of origin for each data point within the synthetic dataset’s defined 10 clusters (as shown in Fig. 4.1). The cluster prediction accuracy is...	CBA	CBA	ABC	CAB	Selection 4
A: The exact null distributions for those tests are not based on pivotal quantities, and it is not possible to generate sufficient statistics in nonparametric settingsB: Finally, we could explore how this framework might be applied to quickly and reliably recommend sample sizes for nonparametric bioequivalence...	CBA	BAC	BCA	CBA	Selection 2
A: Do indeterministic laws feature in these approaches? There are two main types of models considered in this context. The first are the Bayesian networks; these consist of probabilistic distributions paired with graphs, and are intrinsically indeterministic models; but laws or mechanisms are not specified in any w...	ACB	CBA	BAC	CAB	Selection 2
A: Normal preference learning assigns higher utility to the jailbroken response. While DPL also assigns a higher mean utility to the unsafe response, it also assigns it higher variance, indicating there is disagreement resulting from the helpfulness and harmlessness objectives diverging. Thus, if we evaluate the re...	CBA	CAB	CAB	BAC	Selection 1
A: SQIRL’s sample complexity has higher Spearman correlation with PPO and DQN than they do with each otherB: Table 3: A comparison of the empirical sample complexities of SQIRL, PPO, and DQN in the sticky-action Bridge environmentsC: Furthermore, SQIRL tends to have just slightly worse sample complexity the...	ACB	BAC	BCA	CBA	Selection 2
A: Other examples of intervention invariant auxiliary tasks for our augmentations are dense scene flow or sparse landmarks detectionB: The key challenge is to design different interventions and intervention-invariant auxiliary tasks. C: Furthermore, our idea paves the foundation and generalizes easily to ot...	CAB	ABC	ACB	ABC	Selection 3
A: Figure 2: Illustration of a combinatorial complexB: Left: Schematic drawing of a combinatorial complexC: Black dots indicate rank-0 cells (vertices), cyan shapes indicate rank-1 cells, and light green indicates rank-2 cells. Note that vertex 2 is directly attached to the left rank-2 cell, without being ...	BCA	ABC	BCA	CAB	Selection 2
A: Binary segmentation is a very well known algorithm for the multiple change detection scenarioB: This algorithm scans through the whole data set and looks for a first change pointC: When such a change point is detected, the data is divided into two samples before and after this initial change point and th...	ABC	CBA	ACB	CAB	Selection 1
A: At the beginning of a period, we have to place a replenishment order. This becomes available after l𝑙litalic_l periods. After placing an order, we observe and satisfy demand. This means that there are l+1𝑙1l+1italic_l + 1 periods of uncertain demand to cover before the replenishment order becomes available*B...	BCA	BAC	ACB	ACB	Selection 2
A: A simple case is when labels have a hierarchical structureB: For example, if two binary labels show mammal and dog, respectively, (0, 1) meaning ”not mammal but dog” is not possibleC: Transformations that mix axes cannot be performed because they break the sparse structure, and constant shifts and scale...	ABC	CBA	CAB	CBA	Selection 1
A: However, the accuracy is just one facet to consider. Recall that the choice of the AES is always a trade-off between experimentation cost and accuracyB: Therefore, it is hard to define a single optimal solution.C: In the previous section, we evaluated the estimation accuracy of different methods	ABC	CBA	BCA	ABC	Selection 3
A: FWHM estimates even at f≈2.5𝑓2.5f\approx 2.5italic_f ≈ 2.5 where state-of-the-art methods are biasedB: (1996) and smoothing white noise on a grid with a Gaussian kernel is the typical way that LKC estimators and the FWER have been validated in the past (Nichols and Hayasaka, 2003; Hayasaka and Nichols, 2003...	BCA	BCA	ACB	CAB	Selection 3
A: To demonstrate the generalization of MixEHR-SurG and the ability for inferring multi-modal EHR topics, we made use of the Medical Information Mart for Intensive Care III (MIMIC-III) dataset [29]B: The dataset was downloaded from the PhysioNet database (mimic.physionet.org) under its user agreement. We carri...	ACB	ABC	BCA	ABC	Selection 1

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