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**A**: In this paper, we have adopted a spectral approach to GoM analysis of multivariate binary responses**B**: Under the notion of expectation identifiability, we have proposed sufficient conditions that are close to being necessary for GoM models to be identifiable**C**: For estimation, we have proposed an efficient... | CBA | ABC | BCA | ACB | Selection 2 |
**A**: Therefore the average number of inputs consumed by Algorithm 1 is close to the optimum. Moreover, even if the number of required inputs in a given realization of the algorithm can potentially be much larger than its average value, the probability that this happens is very small thanks to the exponential-bound pr... | CAB | ABC | CBA | BAC | Selection 3 |
**A**: We note that this challenge is faced by all ridge estimation algorithms, due to the fact that ridges are local features which may arise in any low-estimated-density regions as long as the density is positive, even when true ridges do not exist in these regions. A second challenge is possible local (but non-globa... | CBA | ABC | BCA | CAB | Selection 3 |
**A**: Moreover, a k-nearest neighbor (kNN) regression is applied in order to construct the surrogate function**B**: In this section, we compare different algorithms discussed in Section 4.
It is important to remark that all the techniques are always compared with the same number of evaluations (denoted as E𝐸Eitalic_E... | CBA | ABC | BCA | BAC | Selection 4 |
**A**: its variance and skewness. Appendix A extends our results accordingly. This work appears to provide the first nonlinear, nonparametric estimators for long term dose response curves and counterfactual distributions.**B**:
Definition 2.1 generalizes from long term dose response curves to long term counterfactual ... | CBA | ACB | BCA | CAB | Selection 4 |
**A**: Furthermore, quite notably the performance of SAA can be significantly improved upon for the ski-rental problem, even under the Kolmogorov distance**B**: We discuss in details the derivation of alternative policies for ski-rental in Appendix E.**C**:
We note that related arguments allow us to design alternative... | ABC | BCA | CBA | BAC | Selection 2 |
**A**: Especially adding a cohort effect seemed to often be beneficial.
Interestingly, adding a period effect when modeling the claim amount did often not lead to any improvement. This is possibly due to the fact that inflation is often adjusted for before being aggregated into run-off triangles.**B**: In the data sets... | BCA | BAC | ABC | CBA | Selection 4 |
**A**: The source population is different from the target population and source samples may not be representative of the target population**B**: The goal is to transport the causal effects from the source population to the target population. This setup arises widely in public policy research, where randomized trial or ... | BAC | CBA | CAB | BCA | Selection 4 |
**A**: Indeed, we present an elementary Lemma showing that the resulting model learns a segmentized output function spanned by the chosen basis, meaning that spanning coefficients depend on the values of the remaining fields**B**: This is, of course, an essential property for recommendation systems, since indeed users ... | CAB | CBA | CBA | ABC | Selection 4 |
**A**: Estimation of the observation matrix through importance sampling thus renders GFE optimisation a stochastic procedure. As a result, the GFE may fluctuate over iterations. For policy selection, we therefore average the GFE over iterations, after a short burn-in period (ten iterations in this case).**B**: Therefor... | ACB | CAB | CBA | ACB | Selection 3 |
**A**: We also observe from Table 1 that the R2superscript𝑅2R^{2}italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT, MSE, MAE, and max error of MNN are far better than that of modified USVT. Specifically the MSE of MNN is > 28x better compared to modified USVT. That is, MNN works significantly better on MNAR data.
*... | CAB | BCA | ACB | BAC | Selection 1 |
**A**: Moreover, we extended (Jasour et al., 2021) to include exponential updates. We present the new methodological material in Sec. 5.**B**:
(Jasour et al., 2021) developed a method that obtains the exact time evolution of the moments of random states for a class of dynamical systems that depend on trigonometric upd... | ABC | ACB | CAB | BAC | Selection 3 |
**A**: The result is evaluated in Section V. Finally, in Section VI, the conclusion and future work are presented.
**B**: The objective function is discussed in Section III. The experimental setup is described in Section IV**C**: This paper is structured as follows. Some preliminary information is provided in Section I... | CBA | ABC | ACB | BCA | Selection 1 |
**A**: (2018) wherein ML is used to learn nuisance functions with ex ante unknown functional forms, and the predicted values of these functions used to construct (orthogonalized) scores for the interest parameters from which consistent and asymptotically normal estimators can be obtained. DML is a very general estimati... | CAB | BAC | BCA | BCA | Selection 1 |
**A**: Interestingly, ECBM significantly outperforms other methods in terms of overall concept accuracy, especially in CUB (71.3%percent71.371.3\%71.3 % for ECBM versus 39.6%percent39.639.6\%39.6 % for the best baseline CEM); this shows that ECBM successfully captures the interaction (and correlation) among the concept... | ACB | ABC | BAC | CBA | Selection 4 |
**A**:
This work presents two new sequential design strategies to build efficient Gaussian process surrogate models in Bayesian inverse problems. These strategies are especially important for cases where the posterior distribution in the inverse problem has thin support or is high-dimensional, in which case space-fill... | BAC | CBA | ABC | ACB | Selection 3 |
**A**: In networks, additional effects can be inferred by the logic that an ancestor of an ancestor must be an ancestor**B**: In our simulation, we see that this can help to find almost all ancestors without errors even when some connections are individually hard to find at a given sample size. When incorporating unobs... | BCA | BAC | CBA | CAB | Selection 1 |
**A**: A vast majority of unlearning algorithms (Triantafillou et al**B**: This presents a major challenge to deploy machine unlearning algorithms for tackling label noise.**C**: 2024) require knowledge of which samples are mislabeled to partition the data into the retain set and the forget set.
It is challenging to di... | CBA | ACB | CBA | BCA | Selection 2 |
**A**: However, their error bound has linear dependence on the ambient dimension d𝑑ditalic_d and exponential dependence on the diameter of the low-dimensional manifold. Another line of works (Chen et al., 2023b, ; Tang and Yang,, 2024; Oko et al.,, 2023) focused mainly on score estimation with properly chosen neural n... | CAB | ABC | BCA | BCA | Selection 1 |
**A**:
Expanding on the foundation laid by Csmc, Moretti et al**B**: Vcsmc employs Csmc as a means to create an unbiased estimator for the marginal likelihood:**C**: (2021) introduces Variational Combinatorial Sequential Monte Carlo (Vcsmc) as an approach to learn distributions over phylogenetic trees | ACB | CAB | ABC | CAB | Selection 1 |
**A**: [31] explored zero-inflated and hurdle models to better capture the inherent sparsity in social and biological networks.
Furthermore, Dong et al. [15] and Motalebi et al. [32] specifically focused on adapting stochastic block models to account for excess zeroes, underscoring the importance of accurately modellin... | ACB | CBA | CBA | CAB | Selection 4 |
**A**:
This paper is organised as follows. In Section 2, we estimate the second-order of the large-deviation probabilities of the rare event that a sparse Erdős–Rényi random graph has a linear number of vertices in triangles, study the structure of the graph conditionally on this rare event, and provide proofs for our... | BCA | ACB | CBA | BAC | Selection 2 |
**A**: Finally, emerging work on flow matching models [36, 37, 38, 39, 40, 41, 42] has achieved impressive generative performance on several benchmark image datasets**B**: These are closely related to the probability flow ODE (pfODE) view of DBMs, and, in fact, have been shown to be equivalent to such models for specif... | ABC | CAB | ACB | CAB | Selection 3 |
**A**: To understand this better, let’s first take a look at the Neyman-Pearson Lemma:
**B**: In the context of the entire population, CTI shares a very similar formulation with the Least Ambiguous Set method used for classification, as described in (Sadinle, Lei, and Wasserman 2019)**C**: If we assume that our quantil... | BAC | BAC | CBA | CAB | Selection 4 |
**A**:
This gap is particularly relevant in applications such as transductive conformal prediction on traffic networks. For example, existing Graph Neural Network (GNN) methods can predict the label of each road, where the label can be considered as the cost of traversing that road. This problem has been studied in (H... | CAB | CAB | CAB | ABC | Selection 4 |
**A**: Embedding these data-driven linkages with quantification of epistemic uncertainty is critical to assess confidence in predictions and guide future data collection**B**: A cornerstone task in materials modeling and discovery consists in building efficient structure–property linkages from experimental or simulatio... | CAB | ABC | BCA | BAC | Selection 4 |
**A**:
In this paper, we propose leveraging implicit human feedback, specifically response times, to provide additional insights into preference strength. Unlike explicit feedback, response time is unobtrusive and effortless to measure [17], offering valuable information that complements binary choices [16, 2]**B**: T... | CAB | BCA | ACB | ABC | Selection 3 |
**A**:
Moving beyond the investigated use cases with known solutions, we envisage, and encourage, further testing and applications of QRNGs in stochastic modelling, ranging from Bayesian inference, stochastic differential equations, optimisation, and Monte Carlo simulations to leverage the revealed advantages in all a... | ABC | CBA | BAC | BAC | Selection 1 |
**A**: Teacher Selection.
RLHF typically aggregates preferences from multiple teachers (Hao et al., 2023; Zhong et al., 2024; Chakraborty et al., 2024)**B**: (2023); Freedman et al. (2023) formalized the teacher selection problem in RLHF, highlighting the need to query the most appropriate teacher for effective reward ... | ABC | CAB | ABC | ACB | Selection 4 |
**A**: However, this is not true for infinite state spaces. Hence we need some conditions that guarantee geometric ergodicity for which we refer the reader to [13, Theorem 9].
Under geometric ergodicity, the mixing time is upper-bounded by**B**: The key difference is that in geometric ergodicity, the constant N𝑁Nitali... | BCA | CAB | ACB | ACB | Selection 2 |
**A**:
however, we remark this choice of the sphere can be granted flexibility for other geometries, although diagonal matrices work well. This steady state term is primarily a regularizer and helps robustness**B**: The steady state altogether produces a more robust representation that leads to lower out-of-distributi... | BCA | CAB | CBA | ACB | Selection 4 |
**A**: Batch Thompson sampling is centralized, with all agents having access to the same information**B**: However, this may not be realistic in real-world situations, where communication between agents may be constrained due to bandwidth limitations, computational constrictions, or privacy concerns**C**: In these case... | CAB | CBA | ABC | CAB | Selection 3 |
**A**:
Our proposed architecture is based on pre-trained Transformer models**B**: Transformer-based neural processes (Müller et al.,, 2021; Nguyen and Grover,, 2022; Chang et al.,, 2024) serve as the foundational structure for our approach, but they have not considered experimental design**C**: Decision Transformers (... | ACB | BAC | ACB | ABC | Selection 4 |
**A**: Fairness in GNNs has gained substantial attention, particularly in efforts to identify and mitigate biases associated with specific sensitive features (Zhang et al**B**: 2024c).
Various fairness-aware GNN studies aim to preserve the independence of sensitive features through pre-processing and in-processing tech... | BAC | BAC | ABC | BCA | Selection 3 |
**A**: One of our initial assumptions was that the data had been observed at a dense grid. In the case where the data is only available at a sparse grid, further extensions would require smoothing by an appropriate basis, e.g., CB-splines (Machalová et al., 2021) specifically developed for Bayes spaces. Naturally, the ... | ABC | CBA | CAB | CAB | Selection 1 |
**A**: We assume no coalition of institutions large enough to meet or exceed the threshold T𝑇Titalic_T colludes to combine their secret key shares and decrypt data without authorization**B**: If fewer than T𝑇Titalic_T participants collude, they cannot decrypt the aggregated ciphertexts**C**: Those holding enough shar... | BCA | ABC | BCA | BCA | Selection 2 |
**A**: We find that FPET is reasonably well calibrated for predicting mCPR, see Table 1. For example, when predicting mCPR 3 years ahead, i.e. when producing forecasts for 2020 using the training set, the median error in mCPR among all women is -0.7%, and the median absolute error is 1.4%**B**: Examples are given for p... | ABC | BAC | BAC | ACB | Selection 4 |
**A**: The number of control bits in an s-MTJ device impacts both energy consumption and the precision of setting the energy bias, which in turn affects the available probabilities of obtaining bit samples**B**: Figure 2 illustrates this relationship**C**: This section evaluates the approximation error caused by imprec... | ABC | CBA | CBA | BCA | Selection 1 |
**A**: [18], [17], [9], [10], [2], [3]).
In this regard it is more convenient to use the measure called Normalized Strength (borrowed from complex networks terminology, see [6], [1]), and that we define here by**B**: [19]**C**: A high measure of CB means that the competition is highly interesting since it is very diffi... | CAB | BCA | BAC | ABC | Selection 1 |
**A**: Despite the complexity of these interactions, the equations remain mathematically tractable, often enabling precise predictions of disease trends (see, for instance, the discussions of related DSA-based approaches given in [8, 24]).
**B**: By transforming the SIR model using dynamical survival analysis within th... | BCA | CAB | CBA | CAB | Selection 3 |
**A**: However, due to differences in dataset characteristics and data analysis mission, this usage is not equivalent to ridges in the TFR**B**: In the following, we focus exclusively on ridges within TFRs.
**C**: Before proceeding, it is worth noting that the term “ridge” has a long history of usage in statistics [16]... | BCA | ABC | ACB | CAB | Selection 1 |
**A**: NMC stands for naive nested Monte Carlo estimation, while BO stands for Bayesian optimization**B**:
Figure 2: Results on two gridworld environments comparing EIG-based methods with baselines**C**: "Single st. EIG (x / 8.8)" denotes single-state EIG with the x axis scaled by 8.8 - the mean length of trajectories... | CAB | ABC | ABC | BAC | Selection 4 |
**A**: Moreover, the core tensors derived from our method (TTM-HOSVD) in Figure 1 and Tensor-LDA in Figure 3 reveal clear interactions between clusters along all modes. In particular, these methods show the first cluster of indices along the first mode switches from topic 1 to topic 2 for as the documents to which they... | ACB | CBA | CBA | CAB | Selection 4 |
**A**: Together, this definition informally says that the Markov boundary is the minimal set of variables that, once known, allows us to drop all other variables without losing information about Y𝑌Yitalic_Y, and removing any variable from this set would lead to a strict loss of information about Y𝑌Yitalic_Y. When the... | BCA | CAB | BAC | ACB | Selection 2 |
**A**: This approach benefits from a quadratic rate of convergence when the true parameters and their estimates lie within the interior of the parameter space, conditional on the values of row vector representations during the estimation of column vector representations (and vice versa).**B**: Instead, our alternating ... | CAB | ABC | CBA | ABC | Selection 3 |
**A**: The key difference between SA-ZIG and factor analysis is that SA-ZIG uses a large coefficient matrix, whereas factor analysis uses a single vector. Additionally, SA-ZIG assumes a two-stage Bernoulli-Gamma model, while factor analysis assumes a normal distribution.**B**:
The SA-ZIG model is inherently similar to... | ABC | ACB | ABC | CAB | Selection 4 |
**A**: (3) About gold standard.
The gold standard we used is the minimum of test errors from all algorithms in a comparison. This is because it is desired to have smaller test errors and less number of informative genes used in a classification algorithm. The algorithms with the smaller SRD values are closer to the ide... | CBA | ABC | ACB | CBA | Selection 3 |
**A**: Stern (1986) provides an expression for the direct utility function (it is a non-closed form function). Later the functional form (11) was used by Gruber and Saez (2002) and Blomquist and Selin (2010) to estimate taxable income functions.**B**: Sufficient conditions for the Slutsky condition to be satisfied are ... | CBA | BAC | BCA | BAC | Selection 1 |
**A**: However, more observation offers more redundancy, thus better robustness for the same algorithm.**B**: Recoverability.
To evaluate LRMC’s robustness against outliers, we generated 20202020 problem instances with varying outlier density levels and compared its recoverability to ScaledGD**C**: Table II shows that ... | BCA | ABC | ABC | CAB | Selection 4 |
**A**: These features, determined by the Köppen function, provide universal topological information about the input space, effectively implementing a k-nearest neighbors structure that is inherent to the representation**B**: The most striking aspect of KST is that it leads to a Generalized Additive Model (GAM) with fix... | ACB | ACB | BAC | ABC | Selection 3 |
**A**: Consequently, based on Eq. (10), the necessary ϵitalic-ϵ\epsilonitalic_ϵ and δ𝛿\deltaitalic_δ for a given ℒℒ\mathscr{L}script_L to qualify as a PAC learner can be analyzed using the accessible physical quantity, i.e., the learning probability (as shown later)**B**:
Here, the crucial point is that the theoretic... | ABC | CBA | BAC | BCA | Selection 3 |
**A**: The paper makes a number of significant contributions to both the fiducial and causal inference literature**B**: First, we propose fiducial based acceptance sampling algorithm to quantify uncertainty of bounds for a variety of causal estimands under various assumptions by leveraging a binary IV**C**: Second, we ... | BAC | CBA | ACB | ABC | Selection 4 |
**A**: (2024) propose more relaxed assumptions regarding the variance of contexts.
However, these approaches result in a regret that grows exponentially with the number of arms K𝐾Kitalic_K (as shown in Table 1).**B**: For example, Wang et al**C**: (2023a; b); Yang et al | BAC | CAB | BAC | ACB | Selection 2 |
**A**: One of the core assumptions of the H0subscript𝐻0H_{0}italic_H start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT algorithm is that the performances of all drafted players count for the team that drafted them (Rosenof, 2024b). This assumption can be problematic for several reasons, one of which is especially problematic fo... | CAB | ACB | CAB | CAB | Selection 2 |
**A**:
Beyond the differences in reachability distance calculations, the EILOF algorithm only computes the LOF score for the new data point, avoiding recalculation of LOF scores for existing points**B**: This design strikes a balance between computational efficiency and accuracy in LOF calculations**C**: However, it i... | BCA | ABC | CAB | BAC | Selection 2 |
**A**: Figure 6 presents the actual coverage and widths of the confidence intervals under two different correlation structures**B**: We observe that, in general, the coverage for dependent studies is nearly as good as in the independent case. However, when the estimators are equally correlated with ρ𝜌\rhoitalic_ρ arou... | CBA | CBA | CBA | BCA | Selection 4 |
**A**: Subsequently, Section 3 presents the results, along with a comprehensive discussion revolving around the forecast accuracy of each transformation**B**: Lastly, Section 4 concludes the paper by summarizing key findings and suggesting possible extensions for future research.
**C**: Section 2 provides a detailed de... | BCA | CBA | CBA | CAB | Selection 1 |
**A**: We have also tested several possible scenarios and variants, such as different noise perturbations in the observation model and the use of mini-batches**B**: The dimension of the space is:
**C**: We test the proposed scheme in numerous different numerical examples, comparing it with different benchmark schemes | ABC | BAC | ACB | BCA | Selection 4 |
**A**: The block structure of the model is designed to capture seasonality in precipitation data 19. Parameter estimation is carried out using a Bayesian approach, as described by 19.
**B**: This work aims to develop a spatio-temporal regression model with a block structure, incorporating fixed and random functional va... | ABC | BCA | CAB | ABC | Selection 3 |
**A**: At the high temperature of 1.5, the reviews become notably more expressive, with shifts in tone**B**: Phrases such as ‘exceeded my expectations’ and ‘a great choice for a quick getaway’ make the text feel more enthusiastic compared to the original.
This example suggests that a temperature setting that is too hig... | CAB | ACB | BAC | ABC | Selection 4 |
**A**:
Tables S.9-S.10 reports the accuracy of selecting 𝒦𝒦\mathcal{K}caligraphic_K under Bernoulli and Poisson-based DDEs, which correspond to Figure S.3 and Figure S.4**B**: Similarly, Tables S.11-S.12 display the results for selecting K(2)superscript𝐾2K^{(2)}italic_K start_POSTSUPERSCRIPT ( 2 ) end_POSTSUPERSCRI... | ACB | CAB | CAB | ABC | Selection 1 |
**A**: Based on this finding, we propose Generative Gradual Domain Adaptation with Optimal Transport (GOAT). At a high-level, GOAT contains the following steps:**B**:
The above insight is particularly helpful under the situation where intermediate domains are missing or scarce, which is often the case in real-world ap... | ABC | ACB | CAB | ACB | Selection 3 |
**A**: The global reach-level estimation supplied in a previous study [47] provides climatological discharge data in our study. We note that sensitivity experiments (driving the model with 10 discharge values ranging from the average discharge and bankfull discharge) demonstrate that both the astronomical tide and stor... | CAB | BAC | BAC | ACB | Selection 1 |
**A**: We run our MCMC algorithm for 10,000 iterations, discarding the first 4,000 as a burn-in, and thinning every twelfth sample. A detailed discussion on the convergence of MCMC sampling can be found in Appendix D.1**B**: We run our proposed approach as described in Section 3 targeting both marginal and heterogeneou... | BCA | BCA | BAC | CAB | Selection 3 |
**A**: The average MASE and average rank of Online, Online(E), and Offline(1) are better than that of Original**B**: Overall, only Online, Online(E), and Offline(1) show an effectiveness above 60%.
**C**: We illustrate the effectiveness of each data augmentation method in Figure 6, which shows the ratio of times each a... | CBA | ACB | BAC | BCA | Selection 2 |
**A**: This study examined the impact of three classroom types on elementary school students’ academic achievement: small classes (13-17 students per teacher), regular classes (22-25 students per teacher), and regular classes with a full-time aide.
**B**: We revisit Tennessee’s Student/Teacher Achievement Ratio (STAR) ... | ABC | BCA | CAB | BCA | Selection 3 |
**A**: In addition, machine learning methods may perform unsatisfactorily with a relatively small or moderate sample size**B**: Weighting does not directly ensure covariate balance across treatment groups and is less intuitively appealing to practitioners as matching.
Therefore, it is imperative to develop a sensible a... | BAC | BCA | CAB | CAB | Selection 2 |
**A**: Section 3 is devoted to Bank of America portfolios for investment-grade and high-yield corporate bonds: their rates (yields), spreads, and total returns. We again show that dividing autoregression innovations by VIX improves them, and again fit the model (2). Finally, we motivate and fit several alternative mode... | CAB | BCA | ACB | ABC | Selection 1 |
**A**: Second, we show that there are settings with recoverable structure in which any intervention rule that
robustly increases total surplus is equivalent, in terms of how it allocates surplus, to the interventions identified by our main result. These tightness results show that robust interventions must, in general,... | BCA | ACB | CAB | BAC | Selection 3 |
**A**: By focusing on the “mfeat-large” dataset in the main paper, we aim to illustrate the benefits of our proposed algorithms in a complex, multi-view context**B**: To save space, only the concatenated and multi-view subplots are included for the multi-class plot; complete results can be found in the Appendix.
**C**:... | BAC | ABC | ACB | CBA | Selection 3 |
**A**: In setting 1, we run 100 epochs, leverage the gradient descent method with Adam optimizer, and set the learning rate to 0.05**B**: In setting 2, we run 100 epochs, leverage gradient descent method with Adam optimizer, and set the learning rate to 0.1.**C**:
We train hℎhitalic_h, and f𝑓fitalic_f as follows | ACB | ACB | BCA | ABC | Selection 3 |
**A**: We plan to develop statistically valid and more accurate inferential tools, and explore new real-world applications within these ongoing initiatives.
**B**: The authors’ team is currently pursuing several related projects, including extensions to advanced nonparametric domains such as functional data analysis, s... | ABC | ACB | BCA | CAB | Selection 4 |
**A**:
Properties of the Archimedean copulas have been studied extensively under continuous distribution functions but limited work has been done for discrete or mixed distribution functions**B**: This is the approach taken in this paper.**C**: The study of copula’s properties for non-continuous distributions can be a... | ABC | ACB | CAB | CBA | Selection 2 |
**A**: The popular maximum mean discrepancies (Oates et al., 2017) have been coupled with kernel methods, for which Sobolev properties cover a fundamental role, and the reader is referred to the most recent contribution in this direction by Barp et al. (2022).
The class of kernels proposed in this paper is a very good ... | ABC | BCA | ABC | CBA | Selection 4 |
**A**: Thus, given sufficient computational resources, the score function method can be both fast and ensure accuracy and unbiasedness.
**B**: Moreover, the score function method is unbiased and can be implemented in parallel**C**: On the other hand, our score function method can sample from posterior distributions and... | CBA | CAB | BAC | CAB | Selection 1 |
**A**: Unlike CRPS (see Figure 1), optimal transport compares CDFs not vertically but horizontally, see the illustration in Figure 2.**B**: The lemma makes it possible to consider the probability distribution’s quantile function (inverse cumulative distribution function) instead of the probability distribution for comp... | CBA | ACB | ABC | ACB | Selection 1 |
**A**: We are allowed m𝑚mitalic_m measurement, so we
have blue liquid of volume σ−2msuperscript𝜎2𝑚\sigma^{-2}mitalic_σ start_POSTSUPERSCRIPT - 2 end_POSTSUPERSCRIPT italic_m units at our disposal**B**: When**C**: λi−1superscriptsubscript𝜆𝑖1\lambda_{i}^{-1}italic_λ start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT st... | CBA | CAB | BCA | BAC | Selection 3 |
**A**: The suggested approach has been applied to the 2022 FIFA World Cup**B**: Ensuring the attractiveness and competitiveness of the matches played in the last round of the group stage seems to require a more fundamental change in the tournament format such as the following:
**C**: Its design could have been improved... | CAB | BCA | CAB | ACB | Selection 4 |
**A**: In particular, we work with TTN built from low-rank tensors**B**: Local low-rank tensors then reduce the number of components in each layer of the TTN by a factor of 1/b1𝑏1/b1 / italic_b. The output of the tensor in the top layer is the decision function.
The resulting low-rank TTN classifiers have several adva... | ABC | BAC | BAC | ACB | Selection 4 |
**A**: Along the line of work of generative models, studies on generative models and unsupervised learning have made headway into better understanding the properties of identifiability of a latent representation which is meant to capture some underlying factors of variation from which the data was generated**B**: Namel... | BCA | ACB | BAC | ABC | Selection 4 |
**A**: Consequently, we leverage Hamming distance and Lin1 in the methods based on dimensionality reduction in the performance comparison.
**B**: In light of above experimental results, Hamming and Lin1 distances display distinguished performance with respect to at least one or two of the three count metrics (as repres... | ABC | CAB | BAC | ACB | Selection 2 |
**A**: denote by G^∈𝒞(Sμ¯ℳ,ℝ)^𝐺𝒞subscript𝑆¯𝜇ℳℝ{\widehat{G}}\in\mathcal{C}(S_{\bar{\mu}}\mathcal{M},\mathbb{R})over^ start_ARG italic_G end_ARG ∈ caligraphic_C ( italic_S start_POSTSUBSCRIPT over¯ start_ARG italic_μ end_ARG end_POSTSUBSCRIPT caligraphic_M , blackboard_R )**B**: Since G^^𝐺{\widehat{G}}over^ start... | BAC | ABC | CAB | BCA | Selection 2 |
**A**: The resulting graph has a maximum node degree of 30, a median degree of 19, and about 18.4% of node pairs connected.
**B**: To estimate a suitable graph G𝐺Gitalic_G, we use a modified GLasso that leaves within-sector edges unpenalised**C**: This approach incorporates industry knowledge to produce a super-graph ... | ABC | CBA | CAB | BAC | Selection 3 |
**A**: Here, LkDsuperscriptsubscript𝐿𝑘𝐷L_{k}^{D}italic_L start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT start_POSTSUPERSCRIPT italic_D end_POSTSUPERSCRIPT denotes the operator
defined in Remark 3.1**B**: and Caponnetto and De Vito [5])**C**: It turns out that this is the | CBA | ABC | BAC | CBA | Selection 3 |
**A**: Agresti (1999) analyses confidence intervals for the log odds ratio when independent binomial sampling is applied to each population, and Bandyopadhyay et al. (2017) address the more general setting where the two populations are sampled using different combinations of binomial and inverse binomial sampling.**B**... | ABC | ACB | BCA | CAB | Selection 4 |
**A**: Results on both synthetic and real-world experiments validate the effectiveness of the proposed framework, and we use detailed analysis to study its underlying behavior.
**B**: By constructing a tailored combinatorial graph and sampling subgraphs progressively with a recursive algorithm, we are able to traverse ... | BAC | CBA | ACB | ACB | Selection 2 |
**A**:
Now, the tests are applied to the pre-construction data**B**: In this case, the estimated p-values are 0.2940.2940.2940.294 for the new test, and 0.8050.8050.8050.805 for the test by [5] (again, B=1000𝐵1000B=1000italic_B = 1000 bootstrap resamples were used for approximating the two p-values)**C**: Consequentl... | BCA | BAC | ABC | CBA | Selection 3 |
**A**: The literature on the estimation of bid-ask spreads from a time series of displayed prices started with Roll’s estimator, which is based on the empirical covariance of successive price increments [63]. The observable price is considered to be the sum of the mid price, that is the average between the bid and the ... | BAC | ACB | BAC | CAB | Selection 2 |
**A**: Instead of using the standard ELBO loss, we propose to regularize it with a distance-preserving loss function, which utilizes the spatial context as auxiliary information to enforce the learned representation to be geometrically similar to the reference dataset.**B**: As an initial step toward tackling this ques... | BCA | BAC | CBA | CAB | Selection 4 |
**A**: Further samples are provided in Section B.6**B**:
Figure 11: (a) Uncurated samples from the \acFAE generative model for the pressure field p𝑝pitalic_p**C**: (b) The distributions of quantities of interest computed using the \acFAE generative model closely agree with the ground truth. | BAC | ABC | ACB | ACB | Selection 1 |
**A**: Another future direction is to support POEM with the ability to handle label shift at test time. This challenge is exemplified by scenarios where the source domain has a balanced label distribution, but the test domain becomes unbalanced**B**: The first builds on ideas from [101], particularly their prediction-b... | ABC | BAC | CAB | ACB | Selection 4 |
**A**: In Chapter 4, we present the results of a simulation study that compares the performance of several location estimators. All proofs, figures, and tables are included in the appendix, and the corresponding code is available at GitHub.**B**:
In Chapter 2, we review the concept of functional data depth**C**: In Ch... | BCA | CAB | BAC | ACB | Selection 2 |
**A**: The modeling accuracy for crime was enhanced compared to the conventional spatial model (S), ignoring the temporal variation**B**: Based on the empirical result, the determinants of larceny risk vary spatially but exhibit less temporal variation. This suggests that effective countermeasures need to be tailored t... | CBA | BAC | BCA | BAC | Selection 3 |
**A**: Based on the data generation process, studies on the learning problem in RMDP can be primarily divided into three categories. RMDP with generative model has been studied in Zhou et al., (2021); Yang et al., (2022); Panaganti and Kalathil, (2022); Shi et al., (2024),**B**: Robust RL**C**: Robust MDP (RMDP) wa... | BAC | ACB | BAC | CAB | Selection 4 |
**A**: ODE solvers with adaptive step sizes aim to minimize integration error, which is crucial for accuracy**B**: However, these solvers can slow down training when the ODE becomes stiff, as step sizes shrink and make integration time-consuming [3, 7]. This is especially problematic in GFE-based training.**C**:
Using... | BCA | CAB | ABC | ACB | Selection 1 |
**A**: Simulates the bootstrap distribution of the given estimator statistic, which must have been defined as a a function with two arguments: statistic(data, indices)**B**: For multidimensonal data, each row in data is assumed to be one data point**C**: Additional arguments are passed to statistic.
| ABC | BAC | ACB | ACB | Selection 1 |
**A**: Second, it is hard to incorporate useful features, such as seasonality, into a BTYD model, as the model itself is a pure vintage based model. Thirdly, for new users with less transactions, the model does not have enough data to discriminate between potentially high value users and low value ones. Finally, if the... | BAC | ABC | ABC | CAB | Selection 4 |
**A**:
Next, we train GCNs and residual GCNs with 0,20,40,…,30002040…3000,20,40,\dots,3000 , 20 , 40 , … , 300 message-passing layers, and compare their performance on the training, validation, and test sets**B**: The training loss and training classification accuracy are shown in Figure 3, while the classification ac... | ABC | ACB | BCA | CAB | Selection 1 |
**A**: In Section 3, we present the proposed PPLS-BO algorithm for adaptive sampling in reduced dimension. We detail the formulations used to compute the posterior probability density of the GP in reduced dimension, and provide the pseudocode of the PPLS-BO algorithm**B**: Three examples are given in Section 4, demonst... | ABC | ACB | BCA | ABC | Selection 3 |
**A**: In this context, we prove some new results (Proposition 2.1 and Theorem 2.4).**B**: We also elaborate more on the concepts of σ𝜎\sigmaitalic_σ-fields and discernment**C**:
In this section, we reproduce the definitions of learning and knowledge acquisition in [24] | CBA | CAB | BAC | CAB | Selection 1 |
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