robench-2024b
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48 items • Updated
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<|MaskedSetence|> <|MaskedSetence|> In total, the mice came from 85 distinct families. The obvious confounding variable is genetic inheritance due to family relationships. <|MaskedSetence|> These 27 response variables fall into six different categories, relating to the glucose level, insulin level, immunity, EPM, FN... | **A**:
V-A2 Heterogeneous Stock Mice
The heterogeneous stock mice data set contains measurements from around 1700 mice, with 10,000 genetic variables [51].
**B**: We study the association between the genetic variables and a set of 27 response variables that could possibly be affected by inheritance.
**C**: These mi... | CBA | ACB | ACB | ACB | Selection 3 |
Figure 2 presents the three population generating models that we use, labeled Simulations 1, 2 and 3. In all cases the true model includes both the solid and dashed lines. <|MaskedSetence|> Simulated data were generated from a multivariate normal with mean vector of 00 and a covariance matrix implied by the populatio... | **A**: All data were simulated using the lavaan R package (Rosseel, \APACyear2012).
**B**: For both Simulation 1 and 2, we are interested in estimating the factor loading for Y2subscript𝑌2Y_{2}italic_Y start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT in the latent to observed variable transformed equation, which corresponds t... | CAB | CAB | CAB | BCA | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> The level of stochasticity is game dependent; however, it can be observed in many Atari games.
An example of such behavior can be observed in the game Kung Fu Master – after eliminating the current set of opponents, the game screen always looks the same (it contains only player’s c... | **A**: As can be seen in Figure 11 in the Appendix, the stochastic model learns a reasonable behavior – samples potential opponents and renders them sharply.
.
**B**:
A crucial decision in the design of world models is the inclusion of stochasticity.
**C**: Although Atari is known to be a deterministic environment,... | BCA | BCA | ABC | BCA | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> They studied stochastic form of Lanchester model and enquired whether there is role of any attacking and defending army on the number of casualties of the battle. They compared their results with the results of the Bracken and Fricker and results were found to be different. They co... | **A**: This was a battle of an air combat between German and Britain.
Wiper, Pettit and Young [44] applied Bayesian computational techniques to fit the Ardennes Campaign data.
**B**:
NR Johnson and Mackey [22]analysed the Battle of Britain using the Lanchester model.
**C**: They also concluded that the Bayesian ap... | BAC | ABC | BAC | BAC | Selection 1 |
We will consider the case of unidirectional manipulation. <|MaskedSetence|> If being part of the treatment group is beneficial, one faces incentives to manipulate the running variable to be eligible but not to be ineligible. Similarly, if being in the treatment group is detrimental, people face incentives to manipula... | **A**: In Diamond and Persson, (2016) teachers have incentives to inflate students’ scores but have no incentives to reduce students’ scores (see section 2.2 where teachers’ incentives are discussed).
.
**B**: For instance, taxpayers benefit by misreporting income below kink points but do not have any reason to misr... | CBA | CBA | CBA | CAB | Selection 1 |
<|MaskedSetence|> Thus we ran ten consecutive learning trails and averaged them. <|MaskedSetence|> The game of CARTPOLE was selected due to its widespread use and the ease with which the DQN can achieve a steady state policy.
For the experiments, fully connected neural network architecture was used. <|MaskedSetence|... | **A**: We have evaluated Dropout-DQN algorithm on CARTPOLE problem from the Classic Control Environment.
**B**: It was composed of two hidden layers of 128 neurons and two Dropout layers between the input layer and the first hidden layer and between the two hidden layers.
**C**:
To evaluate the Dropout-DQN, we empl... | CAB | BCA | CAB | CAB | Selection 1 |
<|MaskedSetence|> Zhang et al. <|MaskedSetence|> <|MaskedSetence|> (2020) analyze the performance across different dataset sizes.
Olson et al. (2018) evaluate the performance of modern neural networks using the same test strategy as Fernández-Delgado et al. (2014) and find that neural networks achieve good results b... | **A**: Bornschein et al.
**B**:
Neural networks are universal function approximators.
The generalization performance has been widely studied.
**C**: (2017) demonstrate that deep neural networks are capable of fitting random labels and memorizing the training data.
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Broadly speaking, our work is related to a vast body of work on value-based reinforcement learning in tabular (Jaksch et al., 2010; Osband et al., 2014; Osband and Van Roy, 2016; Azar et al., 2017; Dann et al., 2017; Strehl et al., 2006; Jin et al., 2018) and linear settings (Yang and Wang, 2019b, a; Jin et al., 2019;... | **A**: In particular, our setting is the same as the linear setting studied by Ayoub et al.
**B**: (2019).
**C**: It can be shown that the two settings are incomparable in the sense that one does not imply the other (Zhou et al., 2020).
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<|MaskedSetence|> <|MaskedSetence|> The top 6 representatives (according to a user-selected quality measure) are still shown at the top of the main view (Figure 1(e)), and the projection can be switched at any time if the user is not satisfied with the initial choice. We also provide the mechanism for a selection-bas... | **A**: Five quality metrics, plus their Quality Metrics Average (QMA), are also displayed to support the visual analysis.
**B**: After choosing a projection, users will proceed with the visual analysis using all the functionalities described in the next sections.
**C**: However, the hyper-parameter exploration does n... | BAC | BCA | BCA | BCA | Selection 4 |
However, the existing methods are limited to graph type data while no graph is provided for general data clustering. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> We analyze the degeneration theoretically and experimentally to understand the phenomenon. We further propose a simple but effective strategy to a... | **A**: The main contributions are listed as follows:
(1) Via extending the generative graph models into general type data, GAE is naturally employed as the basic representation learning model and weighted graphs can be further applied to GAE as well.
**B**: Since a large proportion of clustering methods are based on t... | BAC | BAC | BAC | CAB | Selection 2 |
Once, the lasso estimation has been performed, the corresponding residuals are plugged into the variance-covariance matrix. This, in turn, is used to construct the simultaneous confidence bands via a multiplier bootstrap procedure. <|MaskedSetence|> standard normal distributed random variable. <|MaskedSetence|> <|M... | **A**: It is generally recommended to use a large number of bootstrap repetitions, B≥500𝐵500B\geq 500italic_B ≥ 500.
.
**B**: The latter is based on a random perturbation of the score function, for example, by an i.i.d.
**C**: This procedure is very appealing from a computational point of view as it does not requi... | ABC | BCA | BCA | BCA | Selection 4 |
<|MaskedSetence|> (a) presents the selection of appropriate validation metrics for the specification of the data set. <|MaskedSetence|> (c) presents the per-class performance of all the models vs. the active ones per algorithm.
Figure 7: The exploration of the models’ and predictions’ spaces and the metamodel’s res... | **A**: (b) aggregates the information after the exploration of different models and shows the active ones which will be used for the stack in the next step.
**B**:
Figure 6: The process of exploration of distinct algorithms in hypotheticality stance analysis.
**C**: The predictions’ space is then updated, and the us... | BAC | BAC | BAC | CBA | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> We defer the detailed discussion on the approximation analysis to §B. Proposition 3.1 allows us to convert the TD dynamics over the finite-dimensional parameter space to its counterpart over the infinite-dimensional Wasserstein space, where the infinitely wide ne... | **A**: Thus, their analysis is not directly applicable to our setting.
**B**: (2018, 2019), the PDE in (3.4) can not be cast as a gradient flow, since there does not exist a corresponding energy functional.
**C**: In contrast to Mei et al.
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The structure of the work is the following: in Sec. 2 we provide an extension to the theory and we define a new set of Finite Change Sensitivity Indices (FCSIs) for functional-valued responses, while in Sec. 3 we then proceed to present and develop the methodology to assess the uncertainty associated with these FCSIs.
... | **A**: Sec.
**B**: 5 concludes and devises additional research directions.
In the Supplementary Material to this paper the interested reader can find an extensive simulation study that puts the proposed indices, estimation and inference technique to the test..
**C**: 4 we tackle the motivating problem: moving from ... | CAB | CAB | CBA | CAB | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> (2009); Huang et al. <|MaskedSetence|> (2012); Fan et al. (2011); Chen et al. (2018)
may enable consistent estimation of the regression function.
Nevertheless, general sparse estimators, when applied to a vectorized tensor covariate, ignore the potential tensor structure and may p... | **A**: (2010); Raskutti et al.
**B**: (2009); Ravikumar et al.
**C**: In this case, the sparsity assumption
Lin and Zhang (2006); Meier et al.
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<|MaskedSetence|> <|MaskedSetence|> For example, UCB-type exploration does not have incentive to take actions other than the one with the largest upper confidence bound of Q𝑄Qitalic_Q-value, and if it has collected sufficient number of samples, it very likely never explores the new optimal action thereby taking the ... | **A**: They are much less compared with MASTER, OPT-WLSVI, LSVI-UCB, Epsilon-Greedy.
**B**:
From Figure 1, we find that the restart strategy works better under abrupt changes than under gradual changes, since the gap between our algorithms and the baseline algorithms designed for stationary environments is larger in ... | BCA | BCA | BCA | BAC | Selection 1 |
I think I would make what these methods doing clearer. <|MaskedSetence|> <|MaskedSetence|> If the latent space is heavily regularized, not allowing enough capacity for the nuisance variables, reconstruction quality is diminished. On the other hand, if the unconstrained nuisance variables have enough capacity, the mod... | **A**: They aren’t really separating into nuisance and independent only..
**B**: This phenomena is sometimes called the "shortcut problem" and has been discussed in previous works [DBLP:conf/iclr/SzaboHPZF18].
.
**C**: they are also throwing away nuisance.
While the aforementioned models made significant progress o... | ACB | ACB | CBA | ACB | Selection 2 |
<|MaskedSetence|> In our simulations, the interpolating predictor often performed worse than the other meta-learners on at least one outcome measure. For example, when the sample size was larger than the number of views, the interpolating predictor often had the lowest TPR in view selection, as well as the lowest test... | **A**: 6 Discussion
In this article we investigated how different view-selecting meta-learners affect the performance of multi-view stacking.
**B**: The fact that its behavior varied considerably across our experimental conditions, combined with its tendency to select very dense models when the meta-learning problem ... | ABC | ABC | ABC | ACB | Selection 2 |
<|MaskedSetence|> in Abbasi-Yadkori et al. [2011]), which is in contrast to the use of an exploration bonus as seen in Faury et al. [2020], Filippi et al. <|MaskedSetence|> <|MaskedSetence|> In non-linear reward models, both approaches may not follow similar trajectory but may have overlapping analysis styles (see F... | **A**: Optimistic parameter search provides a cleaner description of the learning strategy.
**B**:
CB-MNL enforces optimism via an optimistic parameter search (e.g.
**C**: [2010].
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5 Use Case
Figure 5: The exploration of clusters of interest that contain performant ML models. View (a) presents the user’s selection that drive the analyses performed in the remaining subfigures. (b.1) provides an overview of the performance, showing that \raisebox{0.15pt}{\resizebox{!}{0.8ex}{\textbf{\textsf{C3}}}... | **A**: On the other hand, (b.2) shows that the user’s choice of models retains both performance and diversity.
**B**: Those models appear to perform better for the hard-to-classify instances; however, this is a misconception.
**C**: In (c.1), we observe that g-mean and ROC AUC scores are very low, which is a problem ... | ACB | ACB | BCA | ACB | Selection 4 |
The stochastic blockmodel (SBM) (SBM, ) is one of the most used models for community detection in which all nodes in the same community are assumed to have equal expected degrees. Some recent developments of SBM can be found in (abbe2017community, ) and references therein. <|MaskedSetence|> DCSBM is widely used for ... | **A**: To overcome this shortcoming, mixedSCORE proposed a degree-corrected mixed membership (DCMM) model.
**B**: Since in empirical network data sets, the degree distributions are often highly inhomogeneous across nodes, a natural extension of SBM is proposed: the degree-corrected stochastic block model (DCSBM) (DCS... | BAC | BAC | BAC | ACB | Selection 1 |
<|MaskedSetence|> (2009); Ring and Wirth (2012); Bonnabel (2013); Zhang and Sra (2016); Zhang et al. (2016); Liu et al. (2017); Agarwal et al. (2018); Zhang et al. <|MaskedSetence|> (2018); Boumal et al. (2018); Bécigneul and Ganea (2018); Zhang and Sra (2018); Sato et al. <|MaskedSetence|> (2019); Weber and Sra (20... | **A**: (2019); Zhou et al.
**B**:
Related Works.
There is a large body of literature on manifold optimization where the goal is to minimize a functional defined on a Riemannian manifold.
See, e.g., Udriste (1994); Ferreira and Oliveira (2002); Absil et al.
**C**: (2018); Tripuraneni et al.
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<|MaskedSetence|> This will help the system to reduce the computational time needed to train the model—something significant in a real-world scenario. <|MaskedSetence|> Thus, we exclude those features one by one with the interactive cells from the # Action # column. <|MaskedSetence|> The remaining open question that... | **A**: Indeed, if we take a closer look, the last five features underperform and are having a shallow impact on the final result (see Fig. 3(b)).
**B**: At this phase, we want to identify any number of features that can be excluded from the analysis because they contribute only slightly to the final outcome.
**C**: I... | CBA | BAC | BAC | BAC | Selection 2 |
So far, there is no study comparing methods from either group comprehensively. Often papers fail to compare against recent methods and vary widely in the protocols, datasets, architectures, and optimizers used. <|MaskedSetence|> <|MaskedSetence|> For CelebA, [46] uses ResNet-18 whereas [50] uses ResNet-50, but the ... | **A**: These discrepancies make it difficult to judge the methods on an even ground.
.
**B**: For instance, the widely used Colored MNIST dataset, where colors and digits are spuriously correlated with each other, is setup differently across papers.
**C**: Some use it as a binary classification task (class 0: digits... | BCA | ACB | BCA | BCA | Selection 3 |
<|MaskedSetence|> To predict the model output time series, sample paths from the emulated flow map are drawn and employed in an iterative fashion for one-step ahead predictions. <|MaskedSetence|> However, obtaining a GP sample path that can be evaluated at any location x∈𝒳𝑥𝒳x\in\mathcal{X}italic_x ∈ caligraphic_X ... | **A**: The resulting approximate GP sample paths are analytically tractable.
**B**: This section provides the material necessary for sampling from the GP posterior distribution using RFF.
**C**: In this framework, the GP sample paths need to effectively represent the flow map function across its entire domain.
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[Bach and Jordan (2003)], [Chen and Bickel (2006)], [Samworth and Yuan (2012)] and [Matteson and Tsay (2017)]. <|MaskedSetence|> (2014)],
[Pfister et al. <|MaskedSetence|> The traditional approach for testing independence is based on Pearson’s correlation coefficient; for instance, refer to Binet and Vaschide (1897),... | **A**: Testing independence also has many applications, including causal inference ([Pearl (2009)], [Peters et al.
**B**: (2018)], [Chakraborty and Zhang (2019)]), graphical modeling ([Lauritzen (1996)], [Gan, Narisetty and Liang (2019)]), linguistics ([Nguyen and Eisenstein (2017)]), clustering (Székely and Rizzo, 20... | ABC | ABC | ABC | ABC | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> This was also the case in Ostrovskii & Bach [2021] and Tran-Dinh et al. [2015], in which more general properties of these
pseudo-self-concordant functions were established. This was fully formalized in Sun & Tran-Dinh [2019], in which the concept of generalized s... | **A**: For example, the logistic loss function used in logistic regression is not strictly self-concordant, but it fits into a class of pseudo-self-concordant functions, which allows one to obtain similar properties and bounds as those obtained for self-concordant functions [Bach, 2010].
**B**:
Self-concordant functi... | BCA | CAB | BCA | BCA | Selection 4 |
Another line of work (e.g., Gehrke et al. (2012); Bassily et al. <|MaskedSetence|> (2011)) proposes relaxed privacy definitions that leverage the natural noise introduced by dataset sampling to achieve more average-case notions of privacy. This builds on intuition that average-case privacy can be viewed from a Bayesia... | **A**: This perspective was used Shenfeld and Ligett (2019) to propose a stability notion which is both necessary and sufficient for adaptive generalization under several assumptions.
**B**: (2021); Steinke and Zakynthinou (2020).
**C**: (2013); Bhaskar et al.
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<|MaskedSetence|> This automatically incorporates out-of-distribution uncertainty. The default implementation from the GPyTorch library gardner2018gpytorch was used. <|MaskedSetence|> A heteroscedastic likelihood function with trainable noise parameter was used and the model was trained for 50 epochs (following the ... | **A**: Optimization of the noise parameter was handled internally, so no additional validation set was required.
.
**B**: This library provides a multitude of different approximations and deep learning adaptions for Gaussian processes.
**C**:
(i)
Gaussian Process: As kernel a standard radial basis function (RBF ker... | CBA | CBA | BCA | CBA | Selection 1 |
On the other hand, the second component of reciprocity places positive weight on questions involving positive reciprocity and negative weight on questions involving negative reciprocity or punishment. <|MaskedSetence|> While there is some tradeoff in the treatment, the sign of the aggregate interaction term remains ne... | **A**: Individuals who align with this characteristic place much lower weight on the actual cost of contributing, suggesting some altruism.
**B**: This interpretation is reinforced by a large positive effect of the treatment on generalized reciprocity for this group, offset by a small decrease in direct reciprocity.
... | ACB | ACB | ACB | ACB | Selection 3 |
By looking at the picture, an immediate observation is that areas at the corners are simply too far from any of the city center hospitals, meaning that going towards the center from there would be impractical. <|MaskedSetence|> <|MaskedSetence|> Lastly, the always failing areas at the corners of the grid, and at the ... | **A**: This can be surprising at first, but a look at the broader map of the city clarifies that they are closer to hospitals that are not in our grid and, therefore cannot be fully analyzed by our model.
.
**B**: Even worse is the 3 Duomo area, which, despite being quite close to a hospital, experiences such high... | CBA | CBA | CBA | CAB | Selection 2 |
Non-Business day. Values are in percentage.
We remark that this example is just for illustration and showcasing the interpretation of the proposed tensor factor model. Again we note that for the TFM-tucker model, one needs to identify a proper representation of the loading space in order to interpret the model. <|Ma... | **A**: In Chen et al., (2022), varimax rotation was used to find the most sparse loading matrix representation to model interpretation.
**B**: For TFM-cp, the model is unique hence interpretation can be made directly.
**C**: Interpretation is impossible for the vector factor model in such a high dimensional case.
.
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The green color in the center of a point indicates that a decision is from RF, while blue is for AB. <|MaskedSetence|> The size maps the number of training instances that are classified by a specific decision, and the opacity encodes the impurity of each decision. Low impurity (with only a few training instances from ... | **A**: On the other hand, in the usage scenario of Section Usage Scenario, DBSCAN estimated 477 clusters, which tuned the hyperparameter to the maximum value.
.
**B**: The outline color reflects the training instances’ class based on a decision’s prediction.
**C**: For the first experiment in Section Use Case, n_neig... | BCA | BCA | BCA | CAB | Selection 1 |
Data used in the preparation of this article were obtained from two sources: the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Parkinson’s Progression Markers Initiative (PPMI). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through ... | **A**: The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California.
**B**: The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites i... | CBA | CBA | CBA | CBA | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> We see that DFSP returns larger fuzzy weighted modularity than its competitors except for the Karate-club-weighted network. Meanwhile, according to the fuzzy weighted modularity of DFSP in Table 3, we also find that Gahuku-Gama subtribes, Karate-club-weighted, Slovene Parliamentary... | **A**: Furthermore, the running times of DFSP, GeoNMF, SVM-cD, and OCCAM for the Cond-mat-1999 network are 29.06 seconds, 32.33 seconds, 90.63 seconds, and 300 seconds, respectively.
**B**: From now on, we use the number of communities determined by KDFSP in Table 2 for each data to estimate community memberships.
**... | BCA | BCA | CAB | BCA | Selection 2 |
<|MaskedSetence|> We then introduce our model for semiparametric CCA, a Gaussian transformation model whose multivariate margins are parameterized by cyclically monotone functions. In Section 3, we define the multirank likelihood and use it to develop a Bayesian inference strategy for obtaining estimates and confidenc... | **A**: In the first part of Section 2 of this article, we describe a CCA parameterization of the multivariate normal model for variable sets, which separates the parameters describing between-set dependence from those determining the multivariate marginal distributions of the variable sets.
**B**: However, where neces... | BCA | ACB | ACB | ACB | Selection 4 |
<|MaskedSetence|> Contrary to generating paths, we will produce “quasi-paths” from discrete observations of the Lévy process. This idea is similar to bootstrapping. <|MaskedSetence|> <|MaskedSetence|> Additionally, we can provide sufficient conditions to ensure that the M-estimator can be consistent and asymptotical... | **A**: By shuffling the order of the increments, we can create different step functions, which can be regarded as different (discrete) paths from the true distribution.
Using these “quasi-paths”, we can estimate the expected functions of X𝑋Xitalic_X and construct an estimator of ϑ0subscriptitalic-ϑ0\vartheta_{0}italic... | CBA | CBA | CBA | ABC | Selection 2 |
<|MaskedSetence|> Organization of the paper
In Section 2, we include the definitions of adjacency matrices of hypergraphs. <|MaskedSetence|> The algorithms for partial recovery are presented in Section 4. The proof for the correctness of our algorithms for Theorem 1.7 and Corollary 1.9 are given in Section 5. <|Mas... | **A**: The proof of Theorem 1.6, as well as the proofs of many auxiliary lemmas and useful lemmas in the literature, are provided in the supplemental materials..
**B**: The concentration results for the adjacency matrices are provided in Section 3.
**C**:
1.4.
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Since it is natural to expect the adjustment of migratory flows in response to climate change is not instantaneous, especially in the case of gradual phenomena, most of the studies use a panel structure with a macroeconomic focus and attempt to assess the impact of changes in climatic conditions on human migratory fl... | **A**: Analyses at Individual level tend to capture a more negative impact of climate changes on migration, whereas analyses at Country level tend to find a more positive effect.
**B**: In particular, positive and significant coefficients are found for controls as OLS and ML estimators for cross-section analyses, same... | ABC | ABC | ABC | BAC | Selection 2 |
5 Discussion
We have established the asymptotic theory in a class of directed random graph model parameterized by the differentially private bi-sequence and illustrated application to the Probit model. The result shows that statistical inference can be made using the noisy bi-sequence. We assume that the edges are m... | **A**: To avoid this problem, we need appropriately select a probability distribution for directed random graphs when using the existing method.
**B**: We should be able to obtain consistent conclusion if the edges are dependent, provided that the conditions stated in Theorem 1 are met.
**C**: However, the asymptotic... | BCA | BCA | BCA | BAC | Selection 1 |
There are considerable challenges in these contexts for efficient Bayesian computation when avoiding Gaussian distributional assumptions on the outcomes. General purpose Markov chain Monte Carlo (MCMC) methods can in principle be used to draw samples from the posterior distribution of the latent process by making local... | **A**: (2020)
show that such approximate MCMC algorithms are either slow or have large approximation error.
.
**B**: These methods are appealing because they modulate proposal step sizes using local gradient and/or higher order information of the target density.
**C**: Unfortunately, their performance very rapidly dr... | BCA | BCA | BCA | ABC | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> Then, we propose our central notion of causal validity including an example how it may fail. Proposition 1 establishes the key role of re-weighting with a likelihood-ratio process to obtain the interventional distribution. The central result providing graphical rules for non-parame... | **A**:
The outline of our paper is as follows.
**B**: We conclude by an illustration of our approach with the example of HPV-testing for cervical cancer screening; this is a more advanced analysis, and provides a formal justification for the analysis in Nygård et al.
**C**: We begin with some background, focusing o... | ACB | BCA | ACB | ACB | Selection 1 |
Note that this discrepancy between Bayesian and frequentist measures differs considerably from the situation in standard statistical inference with non-adaptive samples. For a fixed-sample problem, the Bernstein–von Mises theorem describes the asymptotic equivalence of Bayesian and frequentist inference. <|MaskedSete... | **A**: Bayesian and frequentist BAI algorithms are both robust against such randomness but with different confidence levels.
**B**: This is notable because it enables solutions that extend beyond the conventional one- or two-step lookahead.
**C**: However, in an adaptive sampling scheme, underestimation of an arm due... | CAB | CAB | CBA | CAB | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> [2019], which shows how large scale genomic data provide a fertile ground for SSPs. Although sequencing technologies have advanced the understanding of genome biology, observed samples may not be perfectly representative of the molecular heterogeneity or species composition of the ... | **A**: Due to the impossibility of sequencing DNA libraries up to complete saturation, it is common to make use of the observed samples, typically collected under suitable budget constraints, to infer the molecular heterogeneity of additional unobserved samples from the library, as well as of the library itself.
**B**... | BAC | CBA | CBA | CBA | Selection 4 |
The appealing properties of the Lorenz curve are well captured by the formulation given in Gastwirth (1971). <|MaskedSetence|> The relation to majorization and the convex order follows immediately, as shown in section C of Marshall et al. (2011). As pointed out by Arnold (2008), this makes the Lorenz ordering an unco... | **A**: In that formulation, the Lorenz curve is the graph of the Lorenz map, and the latter is the cumulative share of individuals below a given rank in the distribution, i.e., the normalized integral of the quantile function.
**B**: Even for utilitarian welfare inequality, Atkinson and Bourguignon (1982) motivate the... | ABC | ABC | ABC | BCA | Selection 2 |
<|MaskedSetence|> She receives a manually-labeled data set with 9 features related to breast cancer [DG17b]. <|MaskedSetence|> From her experience, she knows that instance hardness and class imbalance can be troublesome for the ML model. Thus, she wants to experiment with well-known algorithms for undersampling and o... | **A**: This data set is rather imbalanced, with 458 benign and 241 malignant cases.
**B**: The doctors need explanations, and the minority class in this binary classification problem is of more importance than the majority consisting of healthy patients.
**C**:
5.1 Usage Scenario: Local Assessment of Undersampling
... | BCA | CAB | CAB | CAB | Selection 2 |
Although many recent works focus on learning in the presence of strategic behavior, learning in the presence of capacity constraints and strategic behavior has not previously been studied in depth. <|MaskedSetence|> Competition for the treatment arises when agents are strategic and the decision maker is capacity-cons... | **A**: Many motivating applications for learning with strategic behavior, such as college admissions and hiring, are precisely settings where the decision maker is capacity-constrained.
**B**: Depending on the context, strategic behavior may be harmful, beneficial, or neutral for the decision maker.
**C**: (2022), wh... | ABC | ABC | ABC | BCA | Selection 1 |
We experiment with Voronoi diagrams in which the cells in the center of the diagram tend to be smaller. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Further details of the data generation process may be found in Appendix B.
We compare the performances of the proposed filtration against that of the distanc... | **A**: This results in a higher sampling density on boundaries of smaller cells.
**B**: We further inject additive noise.
**C**: A point is sampled by first choosing a random cell and then choosing a uniform point on its boundary.
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<|MaskedSetence|> (2019) collected data before, during, and after the intervention period. The pre-intervention ran between January 2009 and April 2010. During this period, the sample average of the week of the first prenatal visit is 16.97, and only 34.46% of these visits occur before week 13. Figure 2 shows a histog... | **A**:
Celhay et al.
**B**: The aforementioned treatment occurred exclusively during the intervention period, which ran between May 2010 and December 2010.
**C**: Figure 3 provides a histogram of the number of patients attending each clinic for their first prenatal visit during the intervention period.
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More specifically, partial observability poses both statistical and computational challenges. <|MaskedSetence|> In particular, predicting the future often involves inferring the distribution of the state (also known as the belief state) or its functionals as a summary of the history, which is already challenging eve... | **A**: Such statistical challenges are already prohibitive even for the evaluation of a policy (Nair and Jiang, 2021; Kallus et al., 2021; Bennett and Kallus, 2021), which forms the basis of policy optimization.
**B**: From a statistical perspective, it is challenging to predict future rewards, observations, or states... | CAB | BCA | BCA | BCA | Selection 2 |
The primary implication of this analysis is that if the standard of evidence required by the FDA is loosened, it may cease to be incentive-aligned for the more profitable drugs. The right standard of evidence for the FDA is a source of ongoing debate, and some call for much looser protocols. <|MaskedSetence|> <|Mask... | **A**: For example, the Bayesian decision analysis of Isakov
et al.
**B**: (2019) prompts the authors to call for thresholds from 1% to 30%, depending on the class of drug.
**C**: In particular, in view of Table 1, we worry that greatly loosening the standard of evidence may incentivize
clinical trials for unpromisin... | ABC | ABC | CAB | ABC | Selection 2 |
For the bounds on the marginal probability an individual benefits from treatment which are estimated with data from a randomized experiment or observational study with known treatment probabilities, we derive in Section 2 a closed-form concentration inequality depending on only the sample size and the desired frequenti... | **A**: Our main results are presented in a general manner in terms of sub-groups, delineated by pre-treatment features, and estimators for bounds on pibt based on inverse probability of treatment weighting (IPTW) (Imbens and Rubin,, 2015; Hernán and Robins,, 2020).
**B**: Also different from a margin of error that can... | ACB | ACB | CBA | ACB | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Since then, some KD methods regard knowledge as final responses to input samples [3, 31, 58], some regard knowledge as features extracted from different layers of neural networks [24, 23, 41], and some regard knowledge as relations between such layers [57, 40, 9]... | **A**: [19] propose an original teacher-student architecture that uses the logits of the teacher model as the knowledge.
**B**: Hinton et al.
**C**: Knowledge Distillation (KD).
| CBA | CBA | CBA | CBA | Selection 2 |
<|MaskedSetence|> Such approaches typically recover the filtering of predictive representations by solving moment equations. <|MaskedSetence|> <|MaskedSetence|> (2016) establishes such moment equations based on structural assumptions on the filtering of such predictive states. Similarly, Anandkumar et al. (2012); Ji... | **A**: In particular, Hefny et al.
**B**: (2015); Sun et al.
**C**: In the case that maintaining a belief or conducting the prediction is intractable, previous approaches establish predictive states (Hefny et al., 2015; Sun et al., 2016), which is an embedding that is sufficient for inferring the density of future ob... | CAB | CAB | CAB | CAB | Selection 4 |
(Mariadassou et al.,, 2010). The block memberships are not known a priori, they are recovered a posteriori by the inference algorithm.
In social (resp. <|MaskedSetence|> species) with the same block membership play the same social/ecological role in its system (Boorman and White,, 1976; Luczkovich et al.,, 2003). <... | **A**: In food webs, species playing the same ecological role are said to be ecologically equivalent (see Cirtwill et al.,, 2018, for a review of species role concepts in food webs).
**B**: ecological) networks, individuals (resp.
**C**: This notion of regular equivalence is a relaxation of structural equivalence whi... | BAC | BAC | BAC | BAC | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> It aims essentially at demonstrating the interest of carrying out the selection of covariates from the data of all the individuals simultaneously thanks to the mixed effects model. <|MaskedSetence|> To both show the flexibility of our approach and simplify the presentation of this... | **A**: The first part is a comparison with strategies that can be easily implemented from existing methods.
**B**: The numerical study is divided into three parts.
**C**: The second part studies in great detail the influences of the number of subjects, the number of covariates, the signal-to-variability ratio and the... | ACB | BAC | BAC | BAC | Selection 4 |
Lamy et al. (2019) study fairness for binary fairness attributes under a noise model of Scott et al. <|MaskedSetence|> <|MaskedSetence|> They then show how this model can be combined with previously proposed fairness constraints to yield an adapted constrained optimization formulation for this restricted model.
An ... | **A**: In this model, it is assumed that the fairness attribute is noisy independently of the classifier’s prediction, leading to a model in which the confusion matrix conditioned on the attribute value is a mixture of the two true confusion matrices conditioned on the two attribute values.
**B**: As we show below, mu... | CAB | BAC | CAB | CAB | Selection 3 |
Although regularization is an effective method to deal with linear regression problems, it brings essential difficulties to the convergence analysis of the algorithm. Compared with the non-regularized decentralized linear regression algorithm, the estimation error equation of this algorithm contains a non-martingale di... | **A**: and bounded..
**B**: and mutually independent and it is required that the expectations of the regression matrices be known in [28]-[29].
Liu et al.
**C**: In addition, they require that the graphs be strongly connected and the observation vectors and the noises be i.i.d.
| BCA | ABC | BCA | BCA | Selection 3 |
While (III) and (IV) hold for mmd with a bounded kernel without additional assumptions, the currently–available bounds on the Rademacher complexity ensure that mmd with an unbounded kernel meets the above conditions only under specific models and data generating processes, even within the i.i.d. setting. <|MaskedSet... | **A**: In this context, it is however possible to revisit the results for the Wasserstein case in Proposition 3 of Bernton et al.
**B**: (2019) under the new Rademacher complexity framework introduced in the present article.
**C**: This in turn yields informative concentration inequalities that are reminiscent of tho... | ACB | ABC | ABC | ABC | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> Any continuous function over a bounded domain can be approximated by a depth-2222 network [3, 11, 22] and this universality result holds for networks with threshold or ReLU as activation functions. Our first main result supports the contrary to this belief. <|MaskedSetence|> There... | **A**:
Given the above result, it may seem that, similarly to the case of monotone networks with ReLU activations, the class of monotone networks with threshold activations is too limited, in the sense that it cannot approximate any monotone function with a constant depth (allowing the depth to scale with the dimens... | BAC | ACB | ACB | ACB | Selection 3 |
This is, of course, not a novel observation, and the disconnection between evaluating raters’ reliability and whether the best applicants were selected was noted earlier [[, e.g.,]]kraemer1991we, nelson1991process, mayo2006peering. In cases where applicant selection is based on a fixed threshold (i.e., pass/fail tests)... | **A**: In fact, connecting reliability to binary classification recalls classical models that evaluate selection procedures based on validity [[, e.g.,]]taylor1939relationship, cronbach1957psychological.
While our approach is related to univariate classification under measurement error, this use case differs.
**B**:... | CAB | ACB | ACB | ACB | Selection 2 |
In contrast, as a special case of the low-rank model, linear MDPs have a similar form of structures but with an extra assumption that the linear representation is known a priori (Du et al., 2019b; Yang & Wang, 2019; Jin et al., 2020; Xie et al., 2020; Ayoub et al., 2020; Cai et al., 2020; Yang & Wang, 2020; Chen et al.... | **A**: Our theory is motivated by the recent progress in low-rank MDPs (Agarwal et al., 2020; Uehara et al., 2021), which show that the transition dynamics can be effectively recovered via maximum likelihood estimation (MLE).
**B**: Our work focuses on the more challenging low-rank setting and aims to recover the unkn... | CAB | BAC | BAC | BAC | Selection 4 |
<|MaskedSetence|> In Section 2, we introduce the MV-SDE and associated notation, motivate MC methods to estimate expectations associated with its solution and set forth the problem to be solved. In Section 3, we introduce the decoupling approach for MV-SDEs (dos Reis et al., 2023) and formulate a DLMC estimator. Next,... | **A**: Finally, we apply the proposed methods to the Kuramoto model from statistical physics in Section 6 and numerically verify all assumptions in this work and the derived complexity rates for the multilevel DLMC estimator for two observables..
**B**:
The remainder of this paper is structured as follows.
**C**:... | BCA | BCA | BCA | BCA | Selection 2 |
3.5 Predicting Abnormal Returns for Simulated Stock Market Events
In this exercise, we rely on data used in Baker and Gelbach (2020) to predict abnormal stock returns for simulated events. This exercise is well suited for assessing the performance of the various estimators for use in financial event study analyses – i... | **A**: For each firm-level event, we use returns data for the 250 trading days prior to the event to predict the returns on the event date.
**B**: As in Baker and Gelbach (2020), for each firm, our pool of control units contains all firms with the same four-digit SIC industry code; if there are fewer than eight such f... | CAB | CAB | CBA | CAB | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> This approach marks a departure from traditional descriptive inference by treating weights as random variables in analytic inference, thereby leveraging the weight model to improve the efficiency of parameter estimation.
Third, to mitigate bias arising from potential misspecificati... | **A**: Our proposed method, therefore, has broad applicability across numerous survey sampling scenarios where sampling weights are known.
Second, we have introduced adaptive estimators that asymptotically attain the semiparametric efficiency bound, rendering them asymptotically optimal within the extensive class of Re... | CAB | ABC | ABC | ABC | Selection 3 |
In the literature, the closest work to ours is the one of Bilokon et al. (2021). Given a set of paths, they propose to compute the signature of these paths in order to apply the clustering algorithm of Azran and Ghahramani (2006) and to ultimately identify market regimes. <|MaskedSetence|> Using Black-Scholes sample p... | **A**: The similarity function underlying the clustering algorithm relies in particular on the Maximum Mean Distance.
**B**: Finally, our numerical experiments are conducted in a setting closer to our practical applications where the marginal one-year distributions of the two samples are the same or very close while t... | ACB | ACB | ACB | ACB | Selection 2 |
Online learning methods enable model updates incrementally from sequential data, offering greater efficiency and scalability than traditional batch learning. Regularization technique is widely used in online convex optimization problems [40]. <|MaskedSetence|> Adaptive subgradient method [42] dynamically adjusts regu... | **A**: Online Mirror Descent, an extension of Mirror Descent [41], utilizes a gradient update rule in the dual space, leading to improved bounds.
**B**: We have three main contributions: (i) We introduce a novel strategy that leverages PINNs alongside the Total Variation method for detecting changepoints within PDE dy... | ABC | ABC | ABC | CAB | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> The resulting statistical uncertainty, as we will argue here, inhibits how well quantum kernel methods may perform.
The heart of the problem is that, in a wide range of circumstances, the value of quantum kernels exponentially concentrate. That is, as the size ... | **A**: By virtue of their convex optimization landscapes, kernel methods are guaranteed to obtain the optimal model from a given Gram matrix.
**B**: However, due to the probabilistic nature of quantum devices, in practice the entries of the Gram matrix can only be estimated via repeated measurements on a quantum devic... | ABC | ABC | ABC | CBA | Selection 1 |
II Related Work
Learning invariant representations. Due to the limitations of covariate shift, particularly in the context of image data, most current research on domain adaptation primarily revolves around addressing conditional shift. <|MaskedSetence|> (2016); Zhao et al. (2018); Saito et al. (2018); Mancini et al... | **A**: This approach focuses on learning invariant representations across domains, a concept explored in works such as Ganin et al.
**B**: Various techniques are employed to achieve this, such as maximum classifier discrepancy (Saito et al., 2018), domain discriminator for adversarial training (Ganin et al., 2016; Zha... | ACB | ACB | BCA | ACB | Selection 1 |
Finally, and most recently, it was asked in previous work (Velychko et al., 2024) if entropy sums can also be used as learning objective.
This may not be intuitive at first because the entropy sums of Theorems 1 and 2
are by themselves no objectives (and for some models do not even depend on the data, see Damm et al.,... | **A**: (2024) made use of this observation for a probabilistic sparse coding model.
**B**: However, considering Theorems 1 and 2 and their proofs, only a subset of parameters has to be at stationary points.
**C**: All remaining parameters can then be learned
using an entropy sum objective.
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While the methods have different finite-sample properties, SVARs and LPs are equivalent in population, as established by Plagborg-Møller and Wolf (2021) who show that the underlying impulse response estimands are the same for both. This implies that the well-documented necessity for SVARs to add assumptions in order to... | **A**: LP impulse responses are obtained by estimating only univariate linear regressions, and performing standard inference on (typically) a single parameter of interest across these univariate regressions.
**B**: In contrast, SVARs require estimating the whole system of equations, and transforming these into the Vec... | ABC | ABC | ABC | ABC | Selection 3 |
<|MaskedSetence|> The recovery process is demonstrated in Figure 2(a). The recovery path starts from the fail vertex and ends in the AGAN vertex which means the observed maintenance returns the status of the failed part to full working order. Suppose the CEG in Figure 1(b) failthfully models the unmanipulated bushing ... | **A**:
A remedial intervention is perfect if the root cause of the failure is correctly identified and successfully fixed by the observed maintenance so that the post-intervention status of the part being maintained is AGAN [45, 47].
**B**: We can visualise the status change of the maintained equipment from Figure 2(... | BAC | ABC | ABC | ABC | Selection 2 |
<|MaskedSetence|> We download publicly available demographic, examination and laboratory data from (https://www.cdc.gov/nchs/nhanes.htm). We combine data from the 2015-2016 and 2017-2018 cycles and adjust the survey weights as instructed in the official documentation (https://wwwn.cdc.gov/nchs/nhanes/tutorials/module3... | **A**: The data is included in our package as ‘nhanes1518’.
**B**:
In the second example, we generate predictors by estimating multivariate associations in NHANES data.
**C**: We log-transform the phthalate metabolites and Creatinine.
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The rest of the paper is organized as follows. <|MaskedSetence|> We present the ℒ2superscriptℒ2\mathcal{L}^{2}caligraphic_L start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT convergence of eigenfunctions in Section 3, and discuss the uniform convergence problem of functional data in Section 5. Asymptotic normality of eige... | **A**: In Section 2, we give a synopsis of covariance and eigencomponents estimation in functional data.
**B**: The proofs of Theorem 1 can be found in Appendix, while the proofs of other theorems and lemmas are collected in the Supplementary Material.
.
**C**: Section 6 provides an illustration of the phase transit... | ACB | ACB | ACB | BCA | Selection 1 |
Our purpose is to test the null hypothesis that the sample of temperatures from 1944 to 1981 comes from the same (functional) distribution to that of the period 1982-2019. The rejection of this null hypothesis could be interpreted as a hint of possible warming in the area. <|MaskedSetence|> This is hardly surprising,... | **A**: Indeed, we observe that, in absence of any significant climate change, one would expect that both samples are made of independent trajectories from the same underlying process.
All the considered tests give a nearly null p𝑝pitalic_p-value.
**B**: 5, where the temperature curves are displayed (the blue curves... | ABC | ACB | ABC | ABC | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> However, the information conveyed by the serial correlations is directly discarded, which prevents us from modeling the generative processes of observations. Another way is to explicitly develop the formation of serial correlations based on further assumptions. For example, dynamic... | **A**: In general, there are two ways to perform model estimation in the presence of serial correlations: (1) by directly processing the nonstationary data and eliminating serial correlations (e.g., performing the differencing operation), so that one can safely ignore the model inadequacy function and obtain stationary... | ABC | ABC | BCA | ABC | Selection 4 |
I-A Characterizing the MI of MIMO Systems by RMT
The MI of the full-rank MIMO channels has been characterized by setting up its CLT using RMT. <|MaskedSetence|> derived the closed-form expressions for the mean and variance of the MI over the i.i.d. MIMO fading channel. In [19], Hachem et al. derived the CLT for the... | **A**: In [24], Kamath et al.
**B**: extended the CLT to the non-Gaussian MIMO channel with a given variance profile and the non-centered MIMO channel in [20] and [21], respectively, which shows that the pseudo-variance and non-zero fourth order cumulant of the random fading affects the asymptotic variance.
**C**: Co... | ACB | ABC | ABC | ABC | Selection 2 |
geometric decay, whereas Ray et al. <|MaskedSetence|> So, we assume that the loss functions ltsubscript𝑙𝑡l_{t}italic_l start_POSTSUBSCRIPT italic_t end_POSTSUBSCRIPT are adversarially
chosen, whereas Ray et al. <|MaskedSetence|> <|MaskedSetence|> (2022) assume. | **A**: (2022) adopt a stochastic
optimization one.
**B**: (2022) assume lt=lsubscript𝑙𝑡𝑙l_{t}=litalic_l start_POSTSUBSCRIPT italic_t end_POSTSUBSCRIPT = italic_l are fixed.
**C**: Second,
we assume that the dynamics (𝒟𝒟{\cal D}caligraphic_D and ρ𝜌\rhoitalic_ρ) are
known (Assumption A1), whereas Ray et al.
| ABC | ABC | ABC | CBA | Selection 1 |
Boomerang earns its name from its principle mechanism—adding noise of a certain variance to push data away from the image manifold, and then using a diffusion model to pull the noised data back onto the manifold. The variance of the noise is the only parameter in the algorithm, and governs how similar the new image is ... | **A**: (2020).
**B**: We show that the proposed local sampling technique is able to: (1a) anonymize entire datasets to varying degrees; (1b) trick facial recognition algorithms; and (1c) anonymize datasets while maintaining better classification accuracy when compared with SOTA synthetic datasets.
**C**: The images g... | ABC | ABC | ABC | CAB | Selection 2 |
The rest of the paper is organized as follows. Section 2 introduces the model. Section 3 introduces the algorithm. <|MaskedSetence|> Section 5 introduces the strategy to generate missing edges. <|MaskedSetence|> <|MaskedSetence|> | **A**: Section 4 shows the consistency of the algorithm and provides some examples for different distributions.
**B**: Section 7 concludes.
2 The Bipartite Mixed Membership Distribution-Free model.
**C**: Section 6 conducts extensive experiments.
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<|MaskedSetence|> Earlier proposals were based on evaluating the nuisance models associated with the estimators, and the utility of decision policy (Zhao et al., 2017) based on the heterogeneous treatment effects of the estimator. Recently, the focus has shifted towards designing surrogate metrics that approximate the... | **A**: Also, there is often a lack of fair comparison between the various metrics as some of them are excluded from the baselines when authors evaluate their proposed metrics.
**B**: Towards this, surrogate metrics have been proposed that perform model selection using only observational data.
**C**: However, most of ... | BAC | BCA | BCA | BCA | Selection 4 |
RFFNet’s objective function (14) is highly non-convex due to the oscillatory behavior of the random features map and the parity symmetry with respect to θ𝜃\thetaitalic_θ. Even if the objective might exhibit a favorable optimization landscape with “natural” input distributions, as discussed in G, the landscape with re... | **A**: We give a meta-algorithm describing how RFFNet is trained in Algorithm 1.
**B**: This regularity suggests we solve (14) using carefully initialized first-order optimization methods.
**C**: Importantly, these defaults were only established after analyzing the ablation studies in D.
.
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Predicting power flows on the electricity transmission network is a key motivating application for probabilistic, spatially coherent modelling in energy forecasting. <|MaskedSetence|> <|MaskedSetence|> They are also constrained by the physics of the network and must be forecasted to identify and mitigate any risk o... | **A**: This is important for both network operators, who are responsible for system security, and traders who must be aware of spatial variation in prices.
**B**: Further, as the configuration of the network may change, any forecasting system must be flexible enough to allow the aggregation of supply and demand on the... | ACB | ACB | ACB | ACB | Selection 3 |
<|MaskedSetence|> For a broad overview, we refer the reader to [FVRS22]. <|MaskedSetence|> By taking advantage of this characterization, AMP methods have been used to derive exact high-dimensional asymptotics for convex penalized estimators such as LASSO [BM12], M-estimators [DM16], logistic regression [SC19], and SL... | **A**: AMP algorithms have been initialized via spectral methods in the context of low-rank matrix estimation [MV21c] and generalized linear models [MV21a].
**B**: Approximate message passing (AMP) algorithms.
AMP is a family of iterative algorithms that has been applied to several problems in high-dimensional statis... | BCA | BAC | BCA | BCA | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> In contrast, the DPBM only requires 9999 parameters for this task (4444 parameters for the numerator of (12) and 5555 parameters for the denominator of (12)), offering a more compact representation of the density function.
Additionally, considering the time consumption, the PF ou... | **A**:
From an RMSE standpoint, the DPBM does not outperform the PF, but a notable drawback of the Particle filter is its requirement to store massive amount of data.
**B**: Our proposed filtering scheme is very suitable for this task.
**C**: For instance, in this simulation, the state of each particle consists of t... | ACB | ACB | CAB | ACB | Selection 4 |
<|MaskedSetence|> Overcoming these hurdles is critical to establishing trust in the development of DT frameworks. <|MaskedSetence|> (2021): (i) Modeling & Simulation (M&S), which includes uncertainty quantification (UQ) and data analytics Kumar et al. (2019, 2022) through trustworthy Artificial Intelligence/Machine L... | **A**: It’s essential to note that DT-enabling technologies encompass three main domains as identified by Yadav et al.
**B**:
From a DT perspective, the development of novel Accident-Tolerant Fuel (ATF) technology encounters several challenges, including (i) data unavailability, (ii) lack of data, missing data, and i... | ACB | BAC | BAC | BAC | Selection 2 |
Gradients in Active and Curriculum Learning. Gradients have been successfully used as a criterion to select data to process in previous work. Settles et al. <|MaskedSetence|> A batch active learning method introduced in Ash et al. <|MaskedSetence|> In the area of curriculum learning, Graves et al. <|MaskedSetence|>... | **A**: (2007) introduce Expected Gradient Length (EGL), computed under the current belief, as a criterion for active learning.
**B**: (2017) considers Gradient Prediction Gain (GPG), which is defined as the gradient’s magnitude and is meant to be a proxy for expected learning progress.
**C**: (2020) also targets data... | ACB | ACB | CAB | ACB | Selection 2 |
<|MaskedSetence|> First, in Section 2 we describe a variety of deep ReLU neural network constructions which will be used to prove Theorem 1. Many of these constructions are trivial or well-known, but we collect them for use in the following Sections. Then, in Section 3 we prove Theorem 4 which gives an optimal represe... | **A**:
The rest of the paper is organized as follows.
**B**: The constant C𝐶Citalic_C may depend upon some parameters and this dependence will be made clear in the presentation.
.
**C**: Finally, in Section 5 we prove the lower bound Theorem 3 and also prove the optimality of Theorem 4.
| ACB | ACB | ABC | ACB | Selection 4 |
However, the constant-width property of standard conformal prediction intervals can be overly restrictive. <|MaskedSetence|> For instance, time series data is often heteroskedastic with the variance increasing along with the horizon due to the accumulation of uncertainty over time. <|MaskedSetence|> CQR borrows techn... | **A**: Constant-width conditional prediction intervals computed for data with heterogeneous variance tend to be inefficient, meaning that they are wider than necessary.
In an effort to achieve more efficient intervals while retaining marginal validity, Romano et al. introduced an elegant method known as Conformalized... | CAB | CAB | ABC | CAB | Selection 4 |
In this paper, we focus on designing Universal Perturbations for Interpretation (UPI) as universal attacks aimed to change the saliency maps of neural nets over a significant fraction of input data. <|MaskedSetence|> <|MaskedSetence|> We demonstrate that the spectral UPI-PCA scheme yields the first-order approximatio... | **A**: To achieve this goal, we formulate an optimization problem to find a UPI perturbation with the maximum impact on the total change in the gradient-based feature maps over the training samples.
**B**: We can summarize the contributions of this work as follows:.
**C**: We propose a projected gradient method calle... | ACB | ACB | BCA | ACB | Selection 2 |
<|MaskedSetence|> View (a) presents the performance of the best-performing metamodel for each cluster according to the seven validation metrics and confidence. The UMAP visible in (b) gathers base models and metamodels predicting similarly the same test instances in groups (Gs) such as G1– G4. On the other hand, (c) v... | **A**:
Figure 1: The investigation of all and cluster_2 comprising 12 base models.
**B**: The last metric is the average predicted probability for all test instances.
**C**: The legend for this view maps the metrics to the different color encodings.
| ABC | ABC | ABC | CAB | Selection 3 |
2.4 Proof of the main result
In [13], the coincidence of posterior modes with minimizers of the Onsager–Machlup functional is stated in a separable Banach space setting as Theorem 3.5 and Corollary 3.10. However, the proof given in [13] is incomplete. <|MaskedSetence|> On the other hand, even in the Hilbert space ca... | **A**: Our proof follows the fundamental approach of [13], incorporating corrections where necessary.
**B**: On the one hand, parts of the proof only hold for separable Hilbert spaces, as pointed out in section 1.1 of [22].
**C**: Most of these corrections have been introduced in [24], while some were just recently f... | BAC | BAC | BAC | ACB | Selection 1 |
The rest of this paper is organized as follows. In Section 2, we first establish the strong duality with respect to y𝑦yitalic_y under some feasibility assumption for nonconvex-concave minimax problem (P) with linearly coupled equality or inequality constraints. Then, we propose a primal-dual alternating proximal grad... | **A**: Numerical results in Section 4 show the efficiency of the two proposed algorithms.
**B**: In Section 3, we propose another primal-dual proximal gradient (PDPG-L) algorithm for nonsmooth nonconvex-linear minimax problem with coupled linear constraints, and also establish its iteration complexity.
**C**: Some co... | BAC | BAC | BAC | BAC | Selection 4 |
6.8 Target Group
In most cases, the visualization tools cover at least the target group of domain experts/practitioners [EGG∗12, FMH16, FCS∗20, GNRM08, HNH∗12, KPN16]. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Providing different prediction capabilities allows for assessing the predictions during the m... | **A**: Beginners/novice users [JSO19, MXQR19, SRM∗15, TLRB18] are rarely considered.
**B**: Then, other target groups such as ML experts [JC17b, KJR∗18, SSK10, WLN∗17] and developers are in the focus of the authors [KFC16, Mad19, RL15b, YZR∗18] (commonly together).
**C**: To give two examples, Bögl et al. [BAF∗14] su... | BCA | BAC | BAC | BAC | Selection 4 |
We apply BR-DTRs to analyze the data from the DURABLE study (Fahrbach et al., 2008). The DURABLE study is a two-phase trial designed to compare the safety and efficacy of insulin glargine versus insulin lispro mix in addition to oral antihyperglycemic agents in T2D patients. During the first phase trial, patients were... | **A**: Under this assumption, in the second stage, patients in the maintenance study are already receiving optimal treatment so it is not necessary to estimate their optimal decision rules.
**B**: Patients who achieved an HbA1c level lower than 7% and entered the maintenance study would not be re-randomized with new t... | CBA | CBA | CBA | ACB | Selection 2 |
<|MaskedSetence|> We commence by examining scenarios in Sec. V.1 under the premise that the function 𝓕𝓕\pmb{\mathcal{F}}bold_caligraphic_F is accessible for analytical estimation. In this section, we also employ training data drawn from a uniform distribution 𝒰(0,1)𝒰01\mathcal{U}(0,1)caligraphic_U ( 0 , 1 ), ensu... | **A**: We end this section with a discussion on considerations regarding training with noisy and irregularly sampled time series data.
**B**:
V Approximation, prediction and long-range forecasting of dynamics
In this section, we detail the primary findings of this paper, focusing on neural approximation, prediction... | CBA | BAC | BAC | BAC | Selection 4 |
Several architectures using the Cox partial log-likelihood have been proposed for conventional tabular datasets. DeepSurv is a neural network-based proportional hazards model (Katzman et al., 2018), which has been used to develop prediction models for several clinical scenarios (Yu et al., 2022) (Bice et al., 2020) (Ki... | **A**: (2021) assess simple Cox neural networks in a high dimensional setting against random forest and penalized regression approaches.
**B**: Kaynar et al.
**C**: Ching et al.
| CAB | CAB | CAB | CAB | Selection 4 |
<|MaskedSetence|> Local interpretability generally refers to the ability to explain the ML predictions for a specific data point rather than the whole model. <|MaskedSetence|> However, the weight and effect of a feature are specific to the individual data point being explained and may not be generalizable to other da... | **A**:
5.2.1 Local Interpretability
Figure 14 shows the local exact interpretability in terms of weight and effect.
**B**: The weight and effect of a feature can provide insight into the most important prediction factors.
**C**: Figure 14b shows the local interpretability of EBM for local feature importance (left i... | ABC | ABC | CBA | ABC | Selection 2 |
The architecture of DeepONet is defined as follows: both the branch and trunk networks are fully connected neural networks. The branch network has a size of [100, 40, 40], while the trunk network has a size of [1, 40, 40]. The activation functions utilized in both networks are Rectified Linear Units (ReLU). Weight init... | **A**: The learning rate for training is set to 0.001, which controls the step size during gradient descent and affects the convergence speed and accuracy of the training process.
**B**: These choices of optimization algorithm, evaluation metric, and learning rate are consistent across the problems discussed in this p... | CBA | CAB | CAB | CAB | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> The new divergence can be applied to a variety of time series and sequential decision making applications in a versatile way. With regard to time series clustering, it demonstrated obvious performance gain for multivariate or high-dimensional time series. <|MaskedSetence|> We addi... | **A**: With regard to reinforcement learning without explicit rewards, it outperforms the popular maximum entropy strategy and encourages significantly exploration to states that have not been visited sufficiently for the agent to be familiar with it.
**B**: 6 Conclusions and Implications for Future Work
We develope... | BCA | BCA | BCA | BCA | Selection 3 |
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