It's all In the (Exponential) Family: An Equivalence between Maximum Likelihood Estimation and Control Variates for Sketching Algorithms

Adaptive multi-fidelity optimization with fast learning rates

Enhancing AI and Dynamical Subseasonal Forecasts with Probabilistic Bias Correction

Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model

The Harder Path: Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback

Stylistic-STORM (ST-STORM) : Perceiving the Semantic Nature of Appearance

Collective Kernel EFT for Pre-activation ResNets

PRIM-cipal components analysis

One-Shot Generative Flows: Existence and Obstructions

Structural interpretability in SVMs with truncated orthogonal polynomial kernels

Amortized Optimal Transport from Sliced Potentials

MinShap: A Modified Shapley Value Approach for Feature Selection

Unsupervised feature selection using Bayesian Tucker decomposition

Multi-User mmWave Beam and Rate Adaptation via Combinatorial Satisficing Bandits

Best of both worlds: Stochastic & adversarial best-arm identification

Scalable Model-Based Clustering with Sequential Monte Carlo

Expert-Guided Class-Conditional Goodness-of-Fit Scores for Interpretable Classification with Informative Missingness: An Application to Seismic Monitoring

Lightweight Geometric Adaptation for Training Physics-Informed Neural Networks

Gating Enables Curvature: A Geometric Expressivity Gap in Attention

Zeroth-Order Optimization at the Edge of Stability

Differentially Private Conformal Prediction

CLion: Efficient Cautious Lion Optimizer with Enhanced Generalization

Generative Augmented Inference

Improving Machine Learning Performance with Synthetic Augmentation

PAC-MCTS: Bias-Aware Pruning for Robust LLM-Guided Search and Planning

Path-Sampled Integrated Gradients

Heat and Matérn Kernels on Matchings

Doubly Outlier-Robust Online Infinite Hidden Markov Model

Momentum Further Constrains Sharpness at the Edge of Stochastic Stability

Multistage Conditional Compositional Optimization

BOAT: Navigating the Sea of In Silico Predictors for Antibody Design via Multi-Objective Bayesian Optimization

Sandpile Economics: Theory, Identification, and Evidence

Online learning with noisy side observations

Spectral Thompson sampling

Covariance-adapting algorithm for semi-bandits with application to sparse rewards

Ordinary Least Squares is a Special Case of Transformer

Metric-Aware Principal Component Analysis (MAPCA):A Unified Framework for Scale-Invariant Representation Learning

Robust Low-Rank Tensor Completion based on M-product with Weighted Correlated Total Variation and Sparse Regularization

Joint Representation Learning and Clustering via Gradient-Based Manifold Optimization

Universality of Gaussian-Mixture Reverse Kernels in Conditional Diffusion

Interpretable and Explainable Surrogate Modeling for Simulations: A State-of-the-Art Survey and Perspectives on Explainable AI for Decision-Making

Estimating Continuous Treatment Effects with Two-Stage Kernel Ridge Regression

A short proof of near-linear convergence of adaptive gradient descent under fourth-order growth and convexity

Some Theoretical Limitations of t-SNE

Bias-Corrected Adaptive Conformal Inference for Multi-Horizon Time Series Forecasting

Identifiability of Potentially Degenerate Gaussian Mixture Models With Piecewise Affine Mixing

Rare Event Analysis via Stochastic Optimal Control

Adaptive Learning via Off-Model Training and Importance Sampling for Fully Non-Markovian Optimal Stochastic Control. Complete version

Generalization Guarantees on Data-Driven Tuning of Gradient Descent with Langevin Updates

Minimizing classical resources in variational measurement-based quantum computation for generative modeling

Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers

ADD for Multi-Bit Image Watermarking

Beyond Fixed False Discovery Rates: Post-Hoc Conformal Selection with E-Variables

Regional Explanations: Bridging Local and Global Variable Importance

ShapShift: Explaining Model Prediction Shifts with Subgroup Conditional Shapley Values

Cost-optimal Sequential Testing via Doubly Robust Q-learning

Query Lower Bounds for Diffusion Sampling

Tail-Aware Information-Theoretic Generalization for RLHF and SGLD

Beyond Augmented-Action Surrogates for Multi-Expert Learning-to-Defer

Hierarchical Kernel Transformer: Multi-Scale Attention with an Information-Theoretic Approximation Analysis

Policy-Aware Design of Large-Scale Factorial Experiments

Towards Verified and Targeted Explanations through Formal Methods

Portfolio Optimization Proxies under Label Scarcity and Regime Shifts via Bayesian and Deterministic Students under Semi-Supervised Sandwich Training

Spectral methods: crucial for machine learning, natural for quantum computers?

The Devil Is in Gradient Entanglement: Energy-Aware Gradient Coordinator for Robust Generalized Category Discovery

A Tutorial Review of Bayesian Optimization with Gaussian Processes to Accelerate Stationary Point Searches

Certified and accurate computation of function space norms of deep neural networks

Mini-Batch Covariance, Diffusion Limits, and Oracle Complexity in Stochastic Gradient Descent: A Sampling-Design Perspective

Conformal Policy Control

Diagnostics for Individual-Level Prediction Instability in Machine Learning for Healthcare

Neural Networks With Dense Weights Are Not Universal Approximators

Continuous-time reinforcement learning: ellipticity enables model-free value function approximation

Scalable spatial point process models for forensic footwear analysis

A Review of Diffusion-based Simulation-Based Inference: Foundations and Applications in Non-Ideal Data Scenarios

Active Learning with Selective Time-Step Acquisition for PDEs

Joint Score-Threshold Optimization for Interpretable Risk Assessment

Revisiting Entropy Regularization: Adaptive Coefficient Unlocks Its Potential for LLM Reinforcement Learning

Discrete Guidance Matching: Exact Guidance for Discrete Flow Matching

PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems

Online Distributionally Robust LLM Alignment via Regression to Relative Reward

Heavy-Tailed Class-Conditional Priors for Long-Tailed Generative Modeling

Random Walk Learning and the Pac-Man Attack

Sequential Regression Learning with Randomized Algorithms

Diagnosing and Improving Diffusion Models by Estimating the Optimal Loss Value

Random Matrix Theory for Deep Learning: Beyond Eigenvalues of Linear Models

Scalable Spatiotemporal Inference with Biased Scan Attention Transformer Neural Processes

Towards AI-assisted Neutrino Flavor Theory Design

Towards Reasonable Concept Bottleneck Models

Practical estimation of the optimal classification error with soft labels and calibration

Flow-based Generative Modeling of Potential Outcomes and Counterfactuals

The Gaussian Latent Machine: Efficient Prior and Posterior Sampling for Inverse Problems

Two-Dimensional Deep ReLU CNN Approximation for Korobov Functions: A Constructive Approach

FSPO: Few-Shot Optimization of Synthetic Preferences Personalizes to Real Users

Identifying Information from Observations with Uncertainty and Novelty

A ghost mechanism: An analytical model of abrupt learning in recurrent networks

A Multiparty Homomorphic Encryption Approach to Confidential Federated Kaplan Meier Survival Analysis

Large Language Models for Market Research: A Data-augmentation Approach

Transformer Neural Processes - Kernel Regression

FIT-GNN: Faster Inference Time for GNNs that 'FIT' in Memory Using Coarsening

Estimating Joint Interventional Distributions from Marginal Interventional Data

Nonparametric Sparse Online Learning of the Koopman Operator

推荐订阅源

stat.ML updates on arXiv.org

Submission history