Is Offline Decision Making Possible with Only Few Samples? Reliable Decisions in Data-Starved Bandits via Trust Region Enhancement

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

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