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- Optimal approximation of smooth functions by ReLU neural networks
- Singular vectors of sparse random rectangular matrices: Transition between delocalized and semilocalized phases
- On strongly coherent confidence envelopes for the false discoveries
- The optimal information complexity of VC learning
- Sharp Asymptotic Theory of Maximum Likelihood Estimation for Gaussian Processes with an RBF Kernel
- On the Sample Complexity of Active Learning with Membership Queries
- Explore Once, Evaluate Many: Multiple Policy Evaluation for Stochastic Linear Bandits
- SurvICL: a Foundation Model for in-context Survival Analysis
- Extending the Oracle Approximating Shrinkage to General Distributions
- Difference-in-Differences for Heterogeneous Effects of General Treatment Strategies Accounting for Time-Varying Covariates
- Learning Changes the World and Then What Was Learned has Changed: Training and Deploying Neural Networks in the Control Loop
- Rethinking Successive Rejects Algorithms
- Designing Experiments for External Decision Makers
- A Bayesian Spatially Varying Coefficient Model for Surface Urban Heat Island Estimation
- SelectPFN: Amortized Bayesian Variable Selection for Sparse Nonlinear Regression with Prior-Data Fitted Networks
- Clustering extremal dependence structures via Wasserstein distances between angular measures
- Prescreen, Then Subsample: Efficient model training for large-scale biomedical data with correlated covariates
- In-Context Learning Before Specialisation: Theory of Learning in a Solvable Topic Model
- Multiplex coupling: Connecting the intellectual base and the topics of a research field
- Covariance-Calibrated Prediction-Powered Inference
- Contact, Power, and Balls in Play: A Three-Rate Decomposition of Batting Average and the Disappearance of the .400 Hitter
- Benchmarking Priors: A Fully Bayesian Approach to Small Area Benchmarking
- Robust Covariance Estimation for Entry-wise Contaminated and Partially Observed Data via Expectation Maximization Algorithm
- Calibrated Transfer Learning for Survival Prediction and Treatment Inference
- Classical methods for modern applications: synthetic tabular data generation using lightweight discrete response continuous latent variable model