- 1 Simulation-based Calibration of Uncertainty Intervals under Approximate Bayesian Estimation ↗
- 2 LazyDINO: Fast, Scalable, and Efficiently Amortized Bayesian Inversion via Structure-Exploiting and Surrogate-Driven Measure Transport ↗
- 3 Nonparametric Estimation of a Factorizable Density using Diffusion Models ↗
- 4 Stochastic Gradient Methods: Bias, Stability and Generalization ↗
- 5 Online Detection of Changes in Moment--Based Projections: When to Retrain Deep Learners or Update Portfolios? ↗
- 6 A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design ↗
- 7 A Data-Augmented Contrastive Learning Approach to Nonparametric Density Estimation ↗
- 8 Two-way Node Popularity Model for Directed and Bipartite Networks ↗
- 9 Convergence and complexity of block majorization-minimization for constrained block-Riemannian optimization ↗
- 10 Contrasting Local and Global Modeling with Machine Learning and Satellite Data: A Case Study Estimating Tree Canopy Height in African Savannas ↗
- 11 A Symplectic Analysis of Alternating Mirror Descent ↗
- 12 Boosted Control Functions: Distribution Generalization and Invariance in Confounded Models ↗
- 13 DCatalyst: A Unified Accelerated Framework for Decentralized Optimization ↗
- 14 Covariate-dependent Hierarchical Dirichlet Processes ↗
- 15 Extrapolated Markov Chain Oversampling Method for Imbalanced Text Classification ↗
- 16 Bayesian Inference of Contextual Bandit Policies via Empirical Likelihood ↗
- 17 Flexible Functional Treatment Effect Estimation ↗
- 18 A causal fused lasso for interpretable heterogeneous treatment effects estimation ↗
- 19 Nonlinear function-on-function regression by RKHS ↗
- 20 Unsupervised Feature Selection via Nonnegative Orthogonal Constrained Regularized Minimization ↗
- 21 skwdro: a library for Wasserstein distributionally robust machine learning ↗
- 22 Reparameterized Complex-valued Neurons Can Efficiently Learn More than Real-valued Neurons via Gradient Descent ↗
- 23 Identifying Weight-Variant Latent Causal Models ↗
- 24 Hierarchical Causal Models ↗
- 25 Online Bernstein-von Mises theorem ↗
- 26 Optimizing Attention with Mirror Descent: Generalized Max-Margin Token Selection ↗
- 27 The Distribution of Ridgeless Least Squares Interpolators ↗
- 28 Adaptive Forward Stepwise: A Method for High Sparsity Regression ↗
- 29 Learning Bayesian Network Classifiers to Minimize Class Variable Parameters ↗
- 30 Optimization and Generalization of Gradient Descent for Shallow ReLU Networks with Minimal Width ↗
- 31 An Anytime Algorithm for Good Arm Identification ↗
- 32 Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection ↗
- 33 Neural Network Parameter-optimization of Gaussian Pre-marginalized Directed Acyclic Graphs ↗
- 34 CHANI: Correlation-based Hawkes Aggregation of Neurons with bio-Inspiration ↗
- 35 Error Analysis for Deep ReLU Feedforward Density-Ratio Estimation with Bregman Divergence ↗
- 36 Persistence Diagrams Estimation of Multivariate Piecewise H{"o}lder-continuous Signals ↗
- 37 UQLM: A Python Package for Uncertainty Quantification in Large Language Models ↗
- 38 Exploring Novel Uncertainty Quantification through Forward Intensity Function Modeling ↗
- 39 Nonlocal Techniques for the Analysis of Deep ReLU Neural Network Approximations ↗
- 40 Generative Bayesian Inference with GANs ↗
- 41 Guaranteed Nonconvex Low-Rank Tensor Estimation via Scaled Gradient Descent ↗
- 42 Communication-efficient Distributed Statistical Inference for Massive Data with Heterogeneous Auxiliary Information ↗
- 43 Extending Mean-Field Variational Inference via Entropic Regularization: Theory and Computation ↗
- 44 Decorrelated Local Linear Estimator: Inference for Non-linear Effects in High-dimensional Additive Models ↗
- 45 Classification Under Local Differential Privacy with Model Reversal and Model Averaging ↗
- 46 Refined Risk Bounds for Unbounded Losses via Transductive Priors ↗
- 47 Efficient frequent directions algorithms for approximate decomposition of matrices and higher-order tensors ↗
- 48 A Common Interface for Automatic Differentiation ↗
- 49 The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks ↗
- 50 Transformers Can Overcome the Curse of Dimensionality: A Theoretical Study from an Approximation Perspective ↗