1,205 records
- Empirical Evaluation of No Free Lunch Violations in Permutation-Based Optimization
- Riemannian Langevin Dynamics: Strong Convergence of Geometric Euler-Maruyama Scheme
- Generalization Properties of Score-matching Diffusion Models for Intrinsically Low-dimensional Data
- The projected isotropic normal distribution with applications in neuroscience
- Beyond Mixtures and Products for Ensemble Aggregation: A Likelihood Perspective on Generalized Means
- A spectral inference method for determining the number of communities in networks
- Bayesian Modeling of Collatz Stopping Times: A Probabilistic Machine Learning Perspective
- Combinatorial Sparse PCA Beyond the Spiked Identity Model
- A Covering Framework for Offline POMDPs Learning using Belief Space Metric
- Density-Matrix Spectral Embeddings for Categorical Data: Operator Structure and Stability
- Mini-Batch Covariance, Diffusion Limits, and Oracle Complexity in Stochastic Gradient Descent: A Sampling-Design Perspective
- The Theory behind UMAP?
- Random Features for Operator-Valued Kernels: Bridging Kernel Methods and Neural Operators
- Robust Power and Sample Size Calculations in Quasi-likelihood Models: Methods and Practice
- A Consistency-Centric Approach to Set-Based Optimization with Multiple Models of Unranked Fidelity
- Multivariate Spatio-Temporal Neural Hawkes Processes
- General Bayesian Policy Learning
- The Partition Principle Revisited: Non-Equal Volume Designs Achieve Minimal Expected Star Discrepancy
- A General (Non-Markovian) Framework for Covariate Adaptive Randomization: Achieving Balance While Eliminating the Shift
- Testing Partially-Identifiable Causal Queries Using Ternary Tests
- Semiparametric Joint Inference for Sensitivity and Specificity at the Youden-Optimal Cut-Off
- Identifiability of Treatment Effects with Unobserved Spatially Varying Confounders
- Efficient Inference after Directionally Stable Adaptive Experiments
- A Researcher's Guide to Empirical Risk Minimization
- Adaptive Penalized Doubly Robust Regression for Longitudinal Data
- Scalable Kernel-Based Distances for Statistical Inference and Integration
- Robust Model Selection for Discovery of Latent Mechanistic Processes
- Flow Matching is Adaptive to Manifold Structures
- Local Fréchet regression with toroidal predictors
- Estimating the Partially Linear Zero-Inflated Poisson Regression Model: a Robust Approach Using a EM-like Algorithm
- On the non-uniformity of the 2026 FIFA World Cup draw
- Detecting Where Effects Occur by Testing Hypotheses in Order
- Zero Variance Portfolio
- The generalized underlap coefficient with an application in clustering
- Smoothness Adaptivity in Constant-Depth Neural Networks: Optimal Rates via Smooth Activations
- Path-conditioned training: a principled way to rescale ReLU neural networks
- Optimality of the Half-Order Exponent in the Turing-Good Identities for Bayes Factors
- Conformal Risk Control for Non-Monotonic Losses
- Implicit Bias and Convergence of Matrix Stochastic Mirror Descent
- Distributional Discontinuity Design
- Stochastic Gradient Variational Inference with Price's Gradient Estimator from Bures-Wasserstein to Parameter Space
- Optimal and Structure-Adaptive CATE Estimation with Kernel Ridge Regression
- Model Error Embedding with Orthogonal Gaussian Processes
- A variational framework for modal estimation
- On the Generalization and Robustness in Conditional Value-at-Risk
- Semiparametric Uncertainty Quantification via Isotonized Posterior for Deconvolutions
- Design-based theory for covariate adjustment in generalized pairwise comparisons
- Minimally Discrete and Minimally Randomized p-Values
- genriesz: A Python Package for Automatic Debiased Machine Learning with Generalized Riesz Regression
- Interactive Learning of Single-Index Models via Stochastic Gradient Descent