Paper tackles distinguishing discrete distributions with local minimax rates.
problem Distinguishing between two discrete distributions when they are close in L1-norm. method Local minimax approach to adapt rates to distribution shapes.
result First local minimax rate for separation distance up to logarithmic factors.
Tests assess if predictions are prudent by comparing observations and predictions.
problem Assessing the prudence of predictions in samples of observations and predictions.
method Bootstrap and normal approximation algorithms for testing unweighted and weighted means, accounting for randomness.
result Tests reveal whether predictions are prudent by showing significantly negative mean differences.
A new test compares latent variable models with kernel methods.
problem Comparing latent variable models with intractable marginal distributions.
method Kernel Stein Test, calibrated threshold, low-dimensional latent structure exploitation.
result Significantly outperforms Maximum Mean Discrepancy test in certain latent structure cases.
The paper proposes a nonparametric test for incomplete samples quantized to B bits.
problem Statistical inference with lossy or incomplete samples.
method Nonparametric testing procedure based on B-bit quantized samples.
result The proposed test achieves the classical minimax rate of testing for spline models when B exceeds a threshold.
New method relaxes TV distance for two-sample testing without distributional assumptions.
problem Challenges in certifying equality or providing tight bounds on TV distance for two distributions.
method Examined blurred total variation distance, a relaxation of TV distance.
result Provided theoretical guarantees for upper and lower bounds on blurred TV distance.
New test detects conditional dependence using GAN approximations.
problem Detecting conditional dependence in high-dimensional feature spaces.
method Generative adversarial networks (GANs) for approximating conditional distributions.
result Significant gains in power over competing methods.
CTT compresses samples to test distributions near-linearly, outperforming existing methods.
problem Efficiently testing distributions with high power and near-linear runtime.
method Sample compression followed by permutation testing.
result CTT achieves near-linear runtime while maintaining high statistical power.
Paper resolves open problems on sample complexity in binary hypothesis testing.
problem Open problems in distributed simple binary hypothesis testing under information constraints.
method One-shot lower bound on Bayes error, streamlined sample complexity formula, reverse data-processing inequality.
result Optimally tight sample complexity bounds for communication-constrained simple binary hypothesis testing.
New tests for high-dimensional data improve on existing methods.
problem Testing mean vectors in high-dimensional data.
method Generalized multivariate sign transformation, using different norm functions.
result Tests using generalized signs have higher power than existing tests.
A new test statistic speeds up MMD while maintaining power.
problem Efficiently testing two distributions without permutations.
method Cross-MMD statistic based on sample-splitting and studentization.
result Cross-MMD has a limiting standard Gaussian distribution under the null.
Deep neural networks have almost linear sample complexity.
problem Sample complexity of deep neural networks.
method o-minimal expansion of the real field to bound sample complexity.
result Almost linear bound on sample complexity of neural networks.
Optimal tests for nonparametric one- and two-sample testing are derived using MMD and KSD.
problem Developing optimal tests for nonparametric one- and two-sample testing.
method Using Sanov's theorem and Maximum Mean Discrepancy (MMD), the optimal error exponents are derived for one-sample tests. For two-sample tests, the quadratic-time Kernel Stein Discrepancy (KSD) is shown to achieve the optimal type-II error exponent.
result Achievement of optimal error exponents for nonparametric one- and two-sample testing in the universal setting.
New framework detects out-of-distribution samples efficiently.
problem Detecting samples from different distributions in deep neural networks.
method Statistical hypothesis testing framework combining evidence from entire network.
result Framework maintains Type I Error and achieves comparable results to state-of-the-art methods.
Study improves statistical power for detecting algorithmic bias in educational data.
problem Challenges in measuring algorithmic bias using ABROCA due to skewed distribution.
method Investigates ABROCA's distributional properties and proposes nonparametric randomization tests.
result ABROCA-based bias assessments are underpowered in typical EDM sample sizes.
The study compares various methods for stochastic optimization, finding that sample average approximation is effective but outperformed by Bayesian approaches.
problem Improving on sample average approximation for stochastic optimization when distributional information is available.
method Compared bagging, kernel smoothing, maximum likelihood estimation, and a Bayesian approach.
result Sample average approximation is effective but outperformed by Bayesian approaches in certain scenarios.
Study uses neural networks to detect nonlinear dynamics in short time series.
problem Challenges in testing dynamical nonlinearities in short time series.
method Recurrent neural network classification framework using raw time series data.
result Classifier accuracy is higher than 50% for chaotic processes, around 50% for nonlinearly correlated noise.
ACORE improves hypothesis testing and confidence sets in likelihood-free inference.
problem Constructing hypothesis tests and confidence sets in likelihood-free inference settings.
method Formulates classical LRT as a classification problem, uses machine learning to improve estimates.
result Demonstrates improved accuracy in hypothesis testing and confidence sets.
Markov Chain Monte Carlo (MCMC) sampling from a posterior distribution corresponding to a massive data set can be computationally prohibitive since producing one sample requires a number of operations that is linear in the data size. In this paper, we introduce a new communication-free parallel method, the Likelihood I…
New research shows CI in few-shot learning is misleading due to sampling with replacement.
problem Misleading confidence intervals in few-shot learning due to sampling with replacement.
method Comparative analysis of CIs computed with and without replacement.
result Significant underestimation of CI by the predominant method.
AutoGAN improves classifier robustness against adversarial attacks.
problem Classifiers fail to correctly classify perturbed images.
method AutoGAN uses a GAN with an autoencoder generator and a classifier discriminator to enhance the training data manifold and project perturbed data points onto it.
result AutoGAN can surpass FGSM method by up to 25% points on FGSM perturbed samples and achieves 89% accuracy without augmented training data.
This paper introduces the hypervolume maximization with a single solution as an alternative to the mean loss minimization. The relationship between the two problems is proved through bounds on the cost function when an optimal solution to one of the problems is evaluated on the other, with a hyperparameter to control t…
Paper develops new method for detecting latent structure in large symmetric data matrices.
problem Testing for latent structure in large symmetric data matrices.
method Introduces Wilcoxon--Wigner random matrices based on normalized rank statistics.
result Establishes asymptotic Gaussian fluctuations for leading eigenvalue and eigenvector of Wilcoxon--Wigner matrices.
This paper develops dimension-agnostic inference methods for high-dimensional data.
problem Understanding how classical inference methods behave in high-dimensional settings.
method Using variational representations, sample splitting, and self-normalization to create a refined test statistic.
result The resulting statistic has a Gaussian limiting distribution regardless of how dimensionality scales with sample size.
We consider the problem of exact recovery of any m×n matrix of rank ϱ from a small number of observed entries via the standard nuclear norm minimization framework. Such low-rank matrices have degrees of freedom (m+n)ϱ−ϱ2. We show that any arbitrary low-rank matrices can be recovered exa…
New method uses birth-death process and exploration component to accelerate sampling from multimodal distributions.
problem Sampling from multimodal probability distributions efficiently.
method Combines birth-death process and exploration component to accelerate sampling.
result Proves exponential asymptotic convergence under mild assumptions.
MineRL Competition reduced reinforcement learning sample needs.
problem Sample inefficiency in reinforcement learning.
method Human demonstrations and imitation learning integrated into reinforcement learning algorithms.
result Top solutions used deep reinforcement learning and imitation learning.
Most machine learning algorithms, such as classification or regression, treat the individual data point as the object of interest. Here we consider extending machine learning algorithms to operate on groups of data points. We suggest treating a group of data points as an i.i.d. sample set from an underlying feature dis…
Dynamic Ensemble Selection (DES) techniques aim to select locally competent classifiers for the classification of each new test sample. Most DES techniques estimate the competence of classifiers using a given criterion over the region of competence of the test sample (its the nearest neighbors in the validation set). T…
Robust subgroup discovery finds non-redundant, statistically significant subgroups.
problem Finding interpretable, robust subgroups from data.
method Formulated subgroup lists for univariate and multivariate targets, used MDL principle and greedy heuristic SSD++.
result SSD++ outperforms previous methods in quality and size of subgroup lists.
The paper explores how machine learning models can be learnable despite label shifts.
problem Learnability of binary classification models in the presence of label shifts.
method Developed a performative empirical risk function that is an unbiased estimate of the true risk on the shifted distribution.
result PAC-learnable hypothesis spaces remain PAC-learnable for performative scenarios.
Approximate Markov chain Monte Carlo (MCMC) offers the promise of more rapid sampling at the cost of more biased inference. Since standard MCMC diagnostics fail to detect these biases, researchers have developed computable Stein discrepancy measures that provably determine the convergence of a sample to its target dist…
Proposes a method for NNs to estimate uncertainty without sampling or OOD data.
problem Calibrated uncertainty measures for safety-critical NNs.
method Evidential priors over Gaussian likelihood for aleatoric and epistemic uncertainty.
result Well-calibrated uncertainty measures on various benchmarks, including complex computer vision tasks.
Paper combines QRM and CNN for better stock option price forecasting.
problem Forecasting stock option prices in a complex market.
method Solves Black-Scholes equation using QRM, trains CNN models on data.
result CNN models improve option price prediction accuracy.
In this paper, we investigate a multivariate multi-response (MVMR) linear regression problem, which contains multiple linear regression models with differently distributed design matrices, and different regression and output vectors. The goal is to recover the support union of all regression vectors using l1/l2-reg…
Discovering statistically significant patterns from databases is an important challenging problem. The main obstacle of this problem is in the difficulty of taking into account the selection bias, i.e., the bias arising from the fact that patterns are selected from extremely large number of candidates in databases. In …
PS-DME evaluates model performance and reliability after data-dependent selection.
problem Evaluating model performance and reliability when data is used for selection and evaluation.
method Post-selection distributional model evaluation (PS-DME) using e-values to control false coverage rate.
result PS-DME provides reliable comparison of model configurations across different reliability levels.
BOSS learns from one labeled sample per class to match fully supervised performance.
problem Achieving fully supervised performance with minimal labeled data.
method Combines class prototype refining, class balancing, and self-training.
result BOSS achieves comparable test accuracies to fully supervised learning.
The paper improves spectral ranking methods for diverse comparison graphs.
problem Estimating preference scores from multiway comparisons with heterogeneous sizes.
method Develops a two-step spectral method for estimating preference scores and their uncertainties.
result The two-step spectral method achieves the same asymptotic efficiency as the Maximum Likelihood Estimator (MLE).
Transfer entropy shows abnormal brain connectivity in depression.
problem Abnormal brain connectivity in depression.
method Transfer entropy analysis on EEG recordings.
result Aberrated dynamics and direction of information between brain centers in depression.
Study improves treatment effect estimation using unlabeled covariates.
problem Estimating treatment effects with limited labeled data.
method Developed efficiency bounds and estimators for semi-supervised setting.
result Estimators using unlabeled covariates have lower asymptotic variance.
Method learns SDEs from one trajectory using GP priors and randomized cross-validation.
problem Learning SDEs from a single trajectory.
method Combining CGC and data-adapted kernels learned via randomized cross-validation.
result Efficacy, robustness, and scope of the method demonstrated in numerical experiments.
Study of classification in asymmetric quasi-metric spaces.
problem Classification in asymmetric quasi-metric spaces.
method Sample compression and nearest neighbor algorithm.
result Algorithm has favorable statistical properties.
We consider the problem of offline, pool-based active semi-supervised learning on graphs. This problem is important when the labeled data is scarce and expensive whereas unlabeled data is easily available. The data points are represented by the vertices of an undirected graph with the similarity between them captured b…
Study provides bounds for estimating intrinsic dimension using Gaussian kernels.
problem Estimating intrinsic dimension from data.
method Finite-sample concentration and anti-concentration bounds for Gaussian kernel sums.
result Explicit dependence on sample size, bandwidth, and geometric parameters.
Researchers compare lower bounds and tractability in reinforcement learning.
problem Intractability of reinforcement learning with misspecified representations.
method Comparison of lower bounds and eluder dimension approach.
result Reconciliation of interpretations between different lines of work.
Introduces MLM dataset for multitask learning across multiple languages and modalities.
problem Training and evaluating multitask systems on diverse data.
method Develops a new benchmark dataset with semantic data and modalities.
result Demonstrates challenges in generalizing on diverse data.
Develops a prediction method based on sampling design.
problem Creating accurate individual predictions.
method Design-based approach using expected cross-validation results.
result Valid inference of unobserved prediction errors defined with respect to sampling design.
Quantum computers can simulate flow models efficiently.
problem Efficiently simulating continuous flow models on quantum computers.
method Relating flow models to the Schrödinger equation and proving efficient Hamiltonian simulation.
result Quantum computers can prepare qsamples for flow models efficiently.