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1,205 records
- Affine Tracing: A New Paradigm for Probabilistic Linear Solvers
- Price of Quality: Sufficient Conditions for Sparse Recovery using Mixed-Quality Data
- Uniform Scaling Limits in AdamW-Trained Transformers
- Quantitative Local Convergence of Mean-Field Stein Variational Gradient Flow
- Proximal Path-Specific Inference
- Learning stochastic multiscale models through normalizing flows
- Core-Halo Decomposition: Decentralizing Large-Scale Fixed-Point Problems
- Measuring and Decomposing Mode Separation via the Canonical Diffusion
- Tight Generalization Bounds for Noiseless Inverse Optimization
- Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity
- TopoFisher: Learning Topological Summary Statistics by Maximizing Fisher Information
- Expectation-Maximization as a Spectrally Governed Relaxation Flow
- Parametric ROC Analysis and Optimal Cutoff Selection under Scale Mixtures of Skew-Normal Distributions: A Decision-Theoretic Framework with Asymptotic Inference
- Cellwise and Casewise Robust Multivariate Regression with Inference
- A Note on Non-Negative $L_1$-Approximating Polynomials
- Sinkhorn Treatment Effects: A Causal Optimal Transport Measure
- Sliced Inner Product Gromov-Wasserstein Distances
- Variational Smoothing and Inference for SDEs from Sparse Data with Dynamic Neural Flows
- A Stein Characterization-type Omnibus Tests for the Discrete Pareto Distribution
- Ratio-based Loss Functions
- CITE: Anytime-Valid Statistical Inference in LLM Self-Consistency
- Expressivity of Bi-Lipschitz Normalizing Flows: A Score-Based Diffusion Perspective
- Risk-Controlled Post-Processing of Decision Policies
- How Does Attention Help? Insights from Random Matrices on Signal Recovery from Sequence Models
- One Operator for Many Densities: Amortized Approximation of Conditioning by Neural Operators
- Locally Near Optimal Piecewise Linear Regression in High Dimensions via Difference of Max-Affine Functions
- Causal discovery under mean independence and linearity
- Augmented transfer regression learning for completely missing covariates
- Multiscale Euclidean Network Trajectories: Second-Moment Geometry, Attribution, and Change Points
- Tests for white noise via asymptotically independent U-statistics in high-dimensions
- Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation
- A renormalization-group inspired lattice-based framework for piecewise generalized linear models
- Tests for constancy of model parameters Over time
- Intrinsic effective sample size for manifold-valued Markov chain Monte Carlo via kernel discrepancy
- Imbalanced Classification under Capacity Constraints
- Free Decompression with Algebraic Spectral Curves
- More Permutations Do Not Always Increase Power: Non-monotonicity in Monte Carlo Permutation Tests
- Perturbation is All You Need for Extrapolating Language Models
- Fast and accurate conditioning for large-scale Gaussian process prediction problems
- Tweedie-based nonparametric estimation for semicontinuous mixed densities
- Structural and Lagrangian properties of analogue ensembles to characterize multifractality of stochastic processes
- Adaptive Targeted Maximum Likelihood Estimation of the Mean Potential Outcome under a Treatment Rule
- Adaptive Estimation and Inference in Semi-parametric Heterogeneous Clustered Multitask Learning via Neyman Orthogonality
- Extrapolation in Statistical Learning with Extreme Value Theory
- Mean Testing under Truncation beyond Gaussian
- The Pearson IV distribution: Random variate generation and applications
- L-Estimation of Population Quantiles Using Ranked Set Sampling
- Self-Normalized Martingales and Uniform Regret Bounds for Linear Regression
- Beyond Independence: on Jointly Normal Priors in Bayesian Inversion
- Concentration and Calibration in Predictive Bayesian Inference