AISTATS 2026 Papers

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DP-SPRT: Differentially Private Sequential Probability Ratio Tests

Sparse Linear Bandits with Blocking Constraints

Stochastic Bandits on Mixture Distributions: Metrics & Regret Bounds

Differentially Private and Federated Structure Learning in Bayesian Networks

Orthogonal Representation Learning for Estimating Causal Quantities

The Good, the Bad, and the Sampled: a No-Regret Approach to Safe Online Classification

Robust Estimation of a Sparse Linear Model: Provable Guarantees with Non-convexity

SPIRE: Conditional Personalization for Federated Diffusion Generative Models

Optimal Transport Guarantees to Nonparametric Regression for Locally Stationary Time Series

Minimax-Optimal Two-Sample Test with Sliced Wasserstein

Scalable Learning of Multivariate Distributions via Coresets

DAG-Informed Structure Learning from Multi-Dimensional Point Processes

Integrating Feature Correlation in Differential Privacy with Applications in DP-ERM

Mixture Proportion Estimation and Weakly-supervised Kernel Test for Conditional Independence

Loss Gaps Parity for Fairness in Heterogeneous Federated Learning

Partially Lazy Gradient Descent for Smoothed Online Learning

Welfare-Centric Clustering

Learning Physical Operators using Neural Operators

Beyond Black-Box Predictions: Identifying Marginal Feature Effects in Tabular Transformer Networks

Learning Linear Regression with Low-Rank Tasks In-Context

Auto-Regressive Masked Diffusion Models

Lloyd's $K$-Means Clustering Algorithm is Frank-Wolfe in Disguise

Archetypal Graph Generative Models: Explainable and Identifiable Communities via Anchor-Dominant Convex Hulls

$k$-PCA for (non-squared) Euclidean Distances: Deterministic Polynomial Time Approximation

Happiness as a Measure of Fairness

Prior shift estimation for positive unlabeled data through the lens of kernel embedding

RoseCDL: Robust and Scalable Convolutional Dictionary Learning for rare-event and anomaly detection

Beyond Real Data: Synthetic Data through the Lens of Regularization

Revisiting Social Welfare in Bandits: UCB is (Nearly) All You Need

Beyond Pooling: Matching for Robust Generalization Under Data Heterogeneity

DeepRV: Accelerating Spatiotemporal Inference with Pre-trained Neural Priors

Duality-based Residual Estimation for Fully Offline Value-based Reinforcement Learning

Examining the Bias of In-Batch Sampling in Similarity Learning with Two-Tower Models

Policy-Oriented Binary Classification: Improving (KD-)CART Final Splits for Subpopulation Targeting

Rate-optimal Design for Anytime Best Arm Identification

Improved Algorithms for Clustering with Noisy Distance Oracles

General Weighted Averaging in Stochastic Gradient Descent: CLT and Adaptive Optimality

Boltzmann Exploration for Heavy-Tailed Bandits

On the Intrinsic Dimensions of Data in Kernel Learning

The Majority Vote Paradigm Shift: When Popular Meets Optimal

High-dimensional Learning with Noisy Labels

Split-Flows: Measure Transport and Information Loss Across Molecular Resolutions

FocusViT: Faithful Explanations for Vision Transformers via Gradient-Guided Layer-Skipping

Structural Alignment Improves Graph Test-Time Adaptation

Dualformer: Time-Frequency Dual Domain Learning for Long-term Time Series Forecasting

Weighted quantization using MMD: From mean field to mean shift using gradient flows

In-memory Training on Analog Devices with Limited Conductance States via Multi-tile Residual Learning

Preference-based Conditional Treatment Effects and Policy Learning

On the Expressivity of Selective State-Space Layers: A Multivariate Polynomial Approach

Fair Clustering via Hierarchical Fair-Dirichlet Prior

Incorporating Expert Knowledge into Bayesian Causal Discovery of Mixtures of Directed Acyclic Graphs

Differentially Private Clustering in Data Streams

Efficient Learning of Stationary Diffusions with Stein-type Discrepancies

Provable Effects of Data Replay in Continual Learning: A Feature Learning Perspective

On the Latent Information Geometry of the Grassmann Manifold

FedCCA: Federated Canonical Correlation Analysis

Causal Additive Models with Unobserved Causal Paths and Backdoor Paths

Efficient Subgroup Analysis via Optimal Trees with Global Parameter Fusion

TAS-EGNN: Task-Aware Spectral Ego-Graphs for Efficient GNNs-Based Classification

Orientability of Causal Relations in Time Series using Summary Causal Graphs and Faithful Distributions

ADOPT: Additive Optimal Transport Regression

Noise-Free Dynamic Rank-Adaptation via Riemannian Methods in Federated Fine-Tuning

Optimal Posterior Sampling for Policy Identification in Tabular Markov Decision Processes

Sample Average Approximation for Alpha-Divergence Minimization with Exponential Convergence Guarantees

Generalizing Behavior via Inverse Reinforcement Learning with Closed-Form Reward Centroids

Unmixing Mean Embeddings for Domain Adaptation with Target Label Proportion

On Barycenter Computation: Analyzing Semi-Unbalanced Optimal Transport-based Method on Bures-Wasserstein manifold.

Identifiability of Potentially Degenerate Gaussian Mixture Models With Piecewise Affine Mixing

Robust Generalization with Adaptive Optimal Transport Priors for Decision-Focused Learning

Low-Rank Bias, Weight Decay, and Model Merging in Neural Networks

Meet Me at the Arm: The Cooperative Multi Armed Bandits Problem with Shareable Arms

Convergence of projected stochastic natural gradient variational inference for various step size and sample or batch size schedules

Sparse Linear Bandits with Fixed Sparsity Support: Adversarial and Stochastic Regimes

On Computational Limits of FlowAR Models: Expressivity and Efficiency

A Recovery Theory for Diffusion Priors: Deterministic Analysis of the Implicit Prior Algorithm

A Finite Time Analysis of Thompson Sampling for Bayesian Optimization with Preferential Feedback

On Different Notions of Redundancy in Conditional-Independence-Based Discovery of Graphical Models

High-dimensional Level Set Estimation with Trust Regions and Double Acquisition Functions

Lyapunov-Guided Self-Alignment: Test-Time Adaptation for Offline Safe Reinforcement Learning

High-Dimensional Analysis of Bootstrap Ensemble Classifiers

Convexified Message-Passing Graph Neural Networks

MDPs with a State Sensing Cost

Feature Importance via Sets of Locally Performant Linear Models

Accelerating Byzantine-Robust Distributed Learning with Compressed Communication via Double Momentum and Variance Reduction

Multi-Component VAE with Gaussian Markov Random Field

The Minimax Lower Bound of Kernel Stein Discrepancy Estimation

Design-Based Finite-Sample Analysis for Regression Adjustment

Provable Guarantees for Estimating Covariances between Latent Variables with Application to Precision Matrix Estimation

Policy Testing in Markov Decision Processes

Narrowing Action Choices with AI Improves Human Sequential Decisions

Provable Target Sample Complexity Improvements as Pre‑Trained Models Scale

Counterfactual Credit Guided Bayesian Optimization

Deconfounding Scores and Representation Learning for Causal Effect Estimation with Weak Overlap

On Kernel based Variational Autoencoders

An Information-Theoretic Approach to Understanding Transformers' In-Context Learning of Variable-Order Markov Chains

Contraction rates for generalized posteriors based on $f$-divergences: a diffusion process approach

Adaptive Replay Buffer for Offline-to-Online Reinforcement Learning

Differentially Private Minimum Spanning Tree in Euclidean Graphs

Regret Guarantees for Linear Contextual Stochastic Shortest Path

E-Scores for (In)Correctness Assessment of Generative Model Outputs

Filter, Augment, Forecast: Online Data Selection for Robust Time Series Forecasting

Catoni-Style Change Point Detection for Regret Minimization in Piecewise-Stationary Heavy-Tailed Bandits

Understanding Generalization in Node and Link Prediction

Low Rank Based Subspace Inference for the Laplace Approximation of Bayesian Neural Networks

Preconditioned Attention: Enhancing Efficiency in Transformer Blocks

Improving Coverage in Combined Prediction Sets with Weighted p-values

Probabilistic multi-dimensional classification with incomplete data at the prediction time

Sequential 1-bit Mean Estimation with Near-Optimal Sample Complexity

Projection-free Algorithms for Online Convex Optimization with Adversarial Constraints

Structured Matrix Scaling for Multi-Class Calibration

Explanation Design in Strategic Learning: Sufficient Explanations That Induce Non-harmful Responses

A Hessian-Free Actor-Critic Algorithm for Bi-Level Reinforcement Learning with Applications to LLM Fine-Tuning

Corruption Robust Thompson Sampling for Gaussian Bandits

PENGUIN: Enhancing Transformer with Periodic-Nested Group Attention for Long-term Time Series Forecasting

GeoTTER: Leveraging Local Geometry of Optimal Transport for Zero-Shot Classification

Enhancing LLM Safety through a Theoretical Minimax Game Lens

Amortized Safe Active Learning for Real-Time Data Acquisition: Pretrained Neural Policies from Simulated Nonparametric Functions

Influence Attributions can be Systematically Altered by Model Manipulation

Structured Difference-of-Q via Orthogonal Learning

Implicit Updates for Average-Reward Temporal Difference Learning

Bandit-based Maximum Inner Product Search with Data-Dependent Confidence Intervals

Linear Convergence of the Frank-Wolfe Algorithm over Product Polytopes

LLMs Judging LLMs: A Simplex Perspective

On the Weight Density of L2-Regularized Linear Classification and Regression

Rethinking Cross-Modal Fine-Tuning: Optimizing the Interaction Between Feature Alignment and Target Fitting

Neural Additive Experts: Context-Gated Experts for Controllable Model Additivity

SFBD Flow: A Continuous-Optimization Framework for Training Diffusion Models with Noisy Samples

FedDuA: Doubly Adaptive Federated Learning

Active learning for stochastic contextual linear bandits

Linearly Separable Features in Shallow Nonlinear Networks: Width Scales Polynomially with Intrinsic Data Dimension

Dyno-Net: A Dynamic Feature Extraction Model for Gastrointestinal Polyp Detection

Learning in Continuous State-Space MDPs for Network Inventory Management

Topological Alignment of Shared Vision-Language Embedding Space

Where You Place the Norm Matters: From Prejudiced to Neutral Initializations

Regularized Operator Extrapolation Method For Stochastic Hierarchical Variational Inequality Problems

Prior Knowledge Makes It Possible: From Sublinear Graph Algorithms to LLM Test-Time Methods

From Guess2Graph: When and How Can Unreliable Experts Safely Boost Causal Discovery in Finite Samples?

Differentially Private Algorithms for the Stochastic Compositional Optimization Problem

Learning with Incomplete Context: Linear Contextual Bandits with Pretrained Imputation

A Covering Framework for Offline POMDPs Learning Using Belief Space Metric

Variance Reduction Methods Do Not Need to Compute Full Gradients: Improved Efficiency Through Shuffling

On Relation-Aware Slicing in Cross-Domain Alignment

FIELDING: Clustered Federated Learning with Data Drift

Conditional Vendi Score: Prompt-Aware Diversity Evaluation for Generative AI Models and LLMs

Secretary Problem with Predictions and Ordering

Train Less, Infer Faster: Efficient Model Finetuning and Compression via Structured Sparsity

Refining Covariance Matrix Estimation in Stochastic Gradient Descent Through Bias Reduction

Rethinking Intrinsic Dimension Estimation in Neural Representations

Optimal rates for density and mode estimation with expand-and-sparsify representations

Improving Semantic Uncertainty Quantification in Language Model Question-Answering via Token-Level Temperature Scaling

Optimal Variance and Covariance Estimation under Differential Privacy in the Add-Remove Model and Beyond

Dashed Line Defense: Plug-And-Play Defense Against Adaptive Score-Based Query Attacks

Sparse Offline Reinforcement Learning with Corruption Robustness

When Can Federated Learning Match Centralized Learning? A PAC-Bayesian Generalization Gap Analysis

Almost Sure Convergence of Differential Temporal Difference Learning for Average Reward Markov Decision Processes

An Indicator of Membership Inference Security in Post-Training Quantized Models

Beyond Binary Out of Distribution Detection: Characterizing Distributional Shifts with Multi-Statistic Diffusion Trajectories

Regularizing Extrapolation in Causal Inference

Low-Complexity and Consistent Graphon Estimation from Multiple Networks

A Scalable Lift-and-Project Differentiable Approach For the Maximum Cut Problem

Information Hidden in Gradients of Regression with Target Noise

Harnessing the Power of Reinforcement Learning for Adaptive MCMC

Semi-Implicit Variational Inference via Kernelized Path Gradient Descent

Brenier Isotonic Regression

DISPO: Enhancing Training Efficiency and Stability in Reinforcement Learning for Large Language Model Mathematical Reasoning

Conformal Robust Control of Linear Systems

Variance Constrained Distribution Alignment in Few-shot Models

Causal-DRF: Conditional Kernel Treatment Effect Estimation using Distributional Random Forest

MLorc: Momentum Low-rank Compression for Memory Efficient Large Language Model Adaptation

Laplace approximation for Bayesian variable selection via Le Cam's one-step procedure

A Geometric Approach to Optimal Experimental Design

Balanced and Robust Multi-Treatment Experimental Designs via Randomized Differencing

RL-finetuning LLMs from on- and off-policy data with a single algorithm

Likelihood-Free Inference via Structured Score Matching

A Pure Hypothesis Test for Inhomogeneous Random Graph Models Based on a Kernelised Stein Discrepancy

Practical and Efficient Rashomon Set Sampling for Model Interpretability

Root Cause Analysis of Outliers in Unknown Cyclic Graphs

Random Features for Operator-Valued Kernels: Bridging Kernel Methods and Neural Operators

Optimal Arm Elimination Algorithms for Combinatorial Bandits

Spectral Clustering for Directed Graphs via Likelihood Estimation on Stochastic Block Models

Meta Sparse Principal Component Analysis

Neural Variance-aware Dueling Bandits with Deep Representation and Shallow Exploration

Off-policy Distributional Q($\lambda$): Distributional RL without Importance Sampling

Gaussian Equivalence for Self-Attention: Asymptotic Spectral Analysis of Attention Matrix

A Proof of Learning Rate Transfer under $\mu$P

Precise Dynamics of Diagonal Linear Networks: A Unifying Analysis by Dynamical Mean-Field Theory

A projection-based framework for gradient-free and parallel learning

Generalized and Optimal Straight-Through Estimators

Standard Acquisition Is Sufficient for Asynchronous Bayesian Optimization

GiVA: Gradient-Informed Bases for Vector-Based Adaptation

PAC-Bayesian Bounds on Constrained $f$-Entropic Risk Measures

Rethinking Probabilistic Circuit Parameter Learning

Tyler’s M-estimator Through the Lens Of Convex-Concave Programming

Support Basis: Fast Attention Beyond Bounded Entries

Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent

Learning Hyperparameters via a Data-Emphasized Variational Objective

On the Interplay of Priors and Overparametrization in Bayesian Neural Network Posteriors

Composable Coresets for Constrained Determinant Maximization and Beyond

Minimax Generalized Cross-Entropy

Hellinger Multimodal Variational Autoencoders

Dendrograms of Mixing Measures for Softmax-Gated Gaussian Mixture of Experts: Consistency Without Model Sweeps

Near-Optimal Sample Complexities of Divergence-based S-rectangular Distributionally Robust Reinforcement Learning

Beyond Johnson-Lindenstrauss: Uniform Bounds for Sketched Bilinear Forms

High Effort, Low Gain: Fundamental Limits of Active Learning for Linear Dynamical Systems

Predictive performance of power posteriors

Scalable resampling in massive generalized linear models via subsampled residual bootstrap

Distributed estimation and inference for semiparametric binary response models

Conformal Prediction in Hierarchical Classification with Constrained Representation Complexity

Consistent PCA and Spectral Clustering

Non-Asymptotic Generalization and Optimization Bounds for Stochastic Gauss-Newton in Deep Neural Networks

On the hardness of Reinforcement Learning with Transition Lookahead

Towards Motion-aware Referring Image Segmentation

Scalable Spatiotemporal Inference with Biased Scan Attention Transformer Neural Processes

Doctor Rashomon and the UNIVERSE of Madness: Variable Importance with Unobserved Confounding and the Rashomon Effect

Graphon Mixtures

Efficient Uncoupled Learning Dynamics with $\tilde{O}\left(T^{-1/4}\right)$ Last-Iterate Convergence in Bilinear Saddle-Point Problems over Convex Sets under Bandit Feedback

A Divergence-Based Method for Weighting and Averaging Model Predictions

Optimal Local Convergence Rates of Stochastic First-Order Methods under Local Alpha-PL

NeST-BO: Fast Local Bayesian Optimization via Newton-Step Targeting of Gradient and Hessian Information

Online Learning-to-Defer with Varying Experts

Efficient Swap Regret Minimization in Combinatorial Bandits

Explore-then-Commit for Nonstationary Linear Bandits with Latent Dynamics

Power Transform Revisited: Numerically Stable, and Federated

Rank Lifting and Random Non-Linear Maps

Faster Parallel MCMC: Metropolis Adjustment Is Best Served Warm

Canopy Tree Height Estimation Using Quantile Regression: Modeling and Evaluating Uncertainty in Remote Sensing

Bounds and Identification of Joint Probabilities of Potential Outcomes and Observed Variables under Monotonicity Assumptions

Adaptive Coverage Policies in Conformal Prediction

Lag Operator SSMs: A Geometric Framework for Structured State Space Modeling

Demystifying Transition Matching: When and Why It Can Beat Flow Matching

Corruption-robust Offline Multi-agent Reinforcement Learning from Human Feedback

Statistical Inference for Explainable Boosting Machines

Towards Sensitivity-Aware Language Models

Reinforcement Learning Using Known Invariances

LLMPhy: Parameter-Identifiable Physical Reasoning Combining Large Language Models and Physics Engines

Tight Analysis of Decentralized SGD: a Markov Chain Perspective

Entropic Projection Alignment: Estimating, Explaining, and Improving Model Performance Under Distribution Shift

Tight Lower Bounds and Optimal Algorithms for Stochastic Nonconvex Optimization with Heavy-Tailed Noise

Neural Doubly Robust Proximal Causal Estimation

Asymptotic optimality theory of confidence intervals of the mean

Injecting Measurement Information Yields a Fast and Noise-Robust Diffusion-Based Inverse Problem Solver

Differentially Private E-Values

Sequential Off-Policy Learning with Logarithmic Smoothing

Tractable Shapley Values and Interactions via Tensor Networks

Partial VOROS: A Cost-aware Performance Metric for Binary Classifiers with Precision and Capacity Constraints

Learnability with Partial Labels and Adaptive Nearest Neighbors

Statistical-computational gap in multiple Gaussian graph alignment

Uncertainty Quantification for Named Entity Recognition via Conformal Prediction

Efficient and Accurate Tensor Compression via Recursive Sketching

Semi-Random Noisy and One-Bit Matrix Completion via Nonconvex Optimization

Recency Biased Causal Attention for Time-series Forecasting

Incoherence in Goal-Conditioned Autoregressive Models

Fact-Augmented Lookahead Planning for LLM Agents

SiGHT: A Self-Supervised Graph-based Hallucination DeTection Framework for Domain-Specific LLMs

Time-Aware Synthetic Control

Best Policy Learning from Trajectory Preference Feedback

Graph Learning is Suboptimal in Causal Bandits

From Transformers to State Spaces: GeoMamba-SE(3) for Fast and Accurate Molecular Learning

Interpretable DNA Sequence Classification via Dynamic Feature Generation in Decision Trees

Structured Matching via Cost-Regularized Unbalanced Optimal Transport

T$_k$CP: Context-Aware Pooling via Top-k% Activation Selection

Free Random Projection for In-Context Reinforcement Learning

Personalized Incentive Alignment: Correcting Utility-Driven Selection Bias in A/B Tests

Scalable Model-Based Clustering with Sequential Monte Carlo

The Rashomon Effect for Visualizing High-Dimensional Data

Adaptive Combinatorial Experimental Design: Pareto Optimality for Decision-Making and Inference

Mask-Conditional Conformal Prediction: Valid Uncertainty For All Missing Data Mechanisms

Local Causal Discovery for Statistically Efficient Causal Inference

Near-Optimal Clustering in Mixture of Markov Chains

GL-LowPopArt: A Nearly Instance-Wise Minimax-Optimal Estimator for Generalized Low-Rank Trace Regression

Connectome-Guided Optimization for Deep Networks

Multi-Metric Adaptive Experimental Design Under a Fixed Budget with Validation

Three-operator splitting with stale gradients for faster non-linear optimal transport

In-Context Function Learning in Large Language Models

Hyperbolic Learning with Supervision from any Granularity

Lipschitz Multiscale Deep Equilibrium Models: A Theoretically Guaranteed and Accelerated Approach

Hybrid Meta-Learners for Estimating Heterogeneous Treatment Effects

Causal Partial Identification via Conditional Optimal Transport

Formally Exploring Time-Series Anomaly Detection Evaluation Metrics

Bad Values but Good Behavior: Learning Highly Misspecified Bandits with Function Approximation

Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data

Accelerated Distributed Optimization with Compression and Error Feedback

FastRank: Fast Tensor Rank Approximation based on Spectral Energy

Information-Theoretic Error Bounds for Source Localization in Neural Sensing

Nonparametric Multi Change Point Detection for Markov Chains via Adaptive Clustering

Learning Geometry and Topology via Multi-Chart Flows

Lower Bounds for Public-Private Learning under Distribution Shift

From Token Imbalance to Balanced Routing: An ELBO-Regularized Probabilistic Framework for Contrastive Multimodal Learning

From Cells to Sentences: An End-to-End Framework for Table Understanding

FairSHAP: Preprocessing for Fairness Through Attribution-Based Data Augmentation

Fundamental Limits of Non-Adaptive Group Testing With Markovian Correlation

Fast Quasar-Convex Optimization with Constraints

Learning to Choose or Choosing to Learn: Best-of-N vs. Supervised Fine-Tuning for Bit String Generation

Moonwalk: Inverse-Forward Differentiation

Towards Characterizing the Complexity of Riemannian Online Convex Optimization

Efficient Inference for Coupled Hidden Markov Models in Continuous Time and Discrete Space

Set to Be Fair: Demographic Parity Constraints for Set-Valued Classification

Do we need rebalancing strategies? A theoretical and empirical study around SMOTE and its variants

Process-Tensor Tomography of SGD: Measuring Non-Markovian Memory via Back-Flow of Distinguishability

Learning How Deep to Go: Self-Scaling Deep Reinforcement Learning

Stationarity-Aware Causal Discovery in Time Series via Minimal Separating Sets

Computationally lightweight classifiers with frequentist bounds on predictions

Where the Score Lives: A Wavelet View of Diffusion

Zeroth-Order Stochastic Compositional Gradient Descent: Towards Black-Box Sparse AUC Maximization

Tensor Gaussian Processes: Efficient Solvers for Nonlinear PDEs

Partial Monotonicity for Submodular Maximization with a Knapsack Constraint

Transportability Without Graphs: A Bayesian Approach to Identifying s-Admissible Backdoor Sets

Boosted GFlowNets: Improving Exploration via Sequential Learning

Busemann Functions in the Wasserstein Space: Existence, Closed-Forms, and Applications to Slicing

Data Distribution Valuation Using Generalized Bayesian Inference

Provable Accelerated Bayesian Optimization with Knowledge Transfer

Generalized Correlation Shifting for Lasso

Identification and Estimation of "Probabilities of Causation" in the Presence of Confounding and Selection Bias

Enforcing Fair Predicted Scores on Intervals of Percentiles by Difference-of-Convex Constraints

Beyond ReLU: How Activations Affect Neural Kernels and Random Wide Networks

CTRLS: Chain-of-Thought Reasoning via Latent State-Transition

Stick-Breaking Embedded Topic Model with Continuous Optimal Transport for Online Analysis of Document Streams

Linear Reasoning Vs. Proof by Cases: Obstacles for Large Language Models in FOL Problem Solving

Ergodic and Subhomogeneous Dynamics in Hyperbolic Neural Networks

Minimizing Human Intervention in Online Classification

Structured Temporal Inference in State-Space Models

Incentivizing Truthful Submissions in a Data Marketplace for Mean Estimation

Kernel Treatment Effects with Adaptively Collected Data

AlphaFold's Bayesian Roots in Probability Kinematics

Gradient Regularized Natural Gradients

Learning Under Moral Hazard with Instrumental Regression and Generalized Method of Moments

Private Synthetic Graph Generation and Fused Gromov-Wasserstein Distance

Momentum SVGD-EM for Accelerated Maximum Marginal Likelihood Estimation

Rate optimal learning of equilibria from data

Hypergraph Neural Networks Accelerate MUS Enumeration

Adversarial Robustness in One-Stage Learning-to-Defer

From Hawkes Processes to Attention: Time-Modulated Mechanisms for Event Sequences

Gradient-Flow SDEs Have Unique Transient Population Dynamics

Time Series Forecasting with Hahn Kolmogorov-Arnold Networks

Undersmoothing Black-Box Models for Functional Estimation

Representative, Informative, and De-Amplifying: Requirements for Robust Bayesian Active Learning under Model Misspecification

Community-Enhanced Semi-seeded Network Alignment (CESSNA): A Robust Method with Application to Microbiome Networks

Evaluation of Large Language Models via Coupled Token Generation

Training Latent Diffusion Models with Interacting Particle Algorithms

Optimal Query Allocation in Extractive QA with LLMs: A Learning-to-Defer Framework with Theoretical Guarantees

On the Misinformation in a Statistical Experiment

LatticeVision: Image to Image Networks for Modeling Non-Stationary Spatial Data

Reconciling Communication Compression and Byzantine-Robustness in Distributed Learning

ECAI: Efficient Convolution Activation Inversion for Constant-Memory Convolutional Neural Networks Training

Exact and Approximate MCMC for Doubly-intractable Probabilistic Graphical Models Leveraging the Underlying Independence Model

Evidence Estimation in Gaussian Graphical Models Using a Telescoping Block Decomposition of the Precision Matrix

Learning to Bid in Discriminatory Auctions with Budget Constraints

Sharp Risk Bounds for Early-stopping in Gaussian Linear Regression

A Bayesian Information-Theoretic Approach to Data Attribution

Variational inference via radial transport

ConMeZO: Adaptive Descent-Direction Sampling for Gradient-Free Finetuning of Large Language Models

Optimistic Actor-Critic with Parametric Policies for Linear Markov Decision Processes

Counterfactual Explanations via Latent Structure for Time Series Classification

ReTrack: Data Unlearning in Diffusion Models through Redirecting the Denoising Trajectory

Thompson Sampling-like Algorithms for Stochastic Rising Bandits

The Riemannian Geometry Associated to Gradient Flows of Linear Convolutional Networks

Active Subspaces in Infinite Dimension

Learning When Not to Learn: Risk-Sensitive Abstention in Bandits with Unbounded Rewards

Fast and Robust Simulation-Based Inference With Optimization Monte Carlo

Deformed Decomposition for Non-negative Tensors

On the optimal regret of collaborative personalized linear bandits

Bayesian Inverse Transition Learning: Learning Dynamics from Near-Optimal Trajectories

Conservative Inference in Switchback Experiments

The Role of Causal Features in Strategic Classification for Robustness and Alignment

Efficient Logistic Regression with Mixture of Sigmoids

An Evaluation of Cost Functions for Algorithmic Recourse

Neuron Block Dynamics for XOR Classification with Zero-Margin

Hyperbolic Part-Whole Image Segmentation

A Goemans-Williamson type algorithm for identifying subcohorts in clinical trials

Provably Efficient Reinforcement Learning for Sparse Dynamical Systems with Non-Gaussian Noise

Conditional Flow Matching for Bayesian Posterior Inference

DIVERSED: Relaxed Speculative Decoding via Dynamic Ensemble Verification

Near-optimal Rank Adaptive Inference of High Dimensional Matrices

On the Convergence and Stability of Distributed Sub-model Training

We Still Don’t Understand High-Dimensional Bayesian Optimization

EventFlow: Forecasting Temporal Point Processes with Flow Matching

Complexity-Aware Deep Symbolic Regression with Robust Risk-Seeking Policy Gradients

Creator Incentives in Recommender Systems: A Cooperative Game-Theoretic Approach for Stable and Fair Collaboration in Multi-Agent Bandits

Pure Exploration with Infinite Answers

A Modularized Framework for Piecewise-Stationary Restless Bandits

Representation Learning via Non-Contrastive Mutual Information

Why is prompting hard? Understanding prompts on binary sequence predictors

On the Role of Depth in the Expressivity of RNNs

Towards Blackwell Optimality: Bellman Optimality Is All You Can Get

Tight Regret Upper and Lower Bounds for Optimistic Hedge in Two-Player Zero-Sum Games

Parameter-Free Dynamic Regret for Unconstrained Linear Bandits

Certifying Reading Comprehension in Large Language Models

In-Context Learning for Discrete Optimal Transport: Can Transformers Sort?

LAMP: Extracting Local Decision Surfaces from Large Language Models

Beyond Binning: Soft Task Reformulation for Deep Regression

An Information-Geometric Approach to Artificial Curiosity

Online Statistical Inference for Stochastic Optimization via Kiefer-Wolfowitz Methods

Root cause discovery via permutations and Cholesky decomposition

Revisiting RIP Guarantees for Sketching Operators on Mixture Models

Deflated HeteroPCA: Overcoming the curse of ill-conditioning in heteroskedastic PCA

Functional Integrative Bayesian Analysis of High-Dimensional Multiplatform Clinicogenomic Data

A new integrative learning framework for integrating multiple secondary outcomes into primary outcome analysis: a case study on liver health

Minimax Optimal Deep Neural Network Classifiers Under Smooth Decision Boundary

Inference for Dispersion and Curvature of Random Objects

Graphical Model Inference with Erosely Measured Data

High-Dimensional Spatial Autoregression with Latent Factors by Diversified Projections

On the eigenvalue decay rates of a class of neural-network related kernel functions defined on general domains

Settling the sample complexity of model-based offline reinforcement learning

Clustering risk in Non-parametric Hidden Markov and I.I.D. Models

Scalable community detection in massive networks via predictive assignment

A Consequentialist Critique of Binary Classification Evaluation: Theory, Practice, and Tools

Multilayer Correlation Clustering

Variational Grey-Box Dynamics Matching

Local Regression on Path Spaces with Signature Metrics

FlowPINNs: A Variational Framework for PDE Parameter Inference and Uncertainty Quantification

i-IF-Learn: Iterative Feature Selection and Unsupervised Learning for High-Dimensional Complex Data

Interpreting and Controlling Model Behavior via Constitutions for Atomic Concept Edits

Unified Causal Discovery and Missing Data Imputation

Batch-Adaptive Causal Annotations

Loss-Driven Bayesian Active Learning

Uncovering Hidden Training Dynamics in Neural Networks via Inter-Sample Influence Graphs

$\epsilon$-Identifiability of Causal Quantities

Impact of Positional Encoding: Clean and Adversarial Rademacher Complexity for Transformers under In-Context Regression

Two mathematical models of knowledge distillation

Meta-probabilistic Modeling

An Illusion of Unlearning? Assessing Machine Unlearning Through Internal Representations

Quantifying Epistemic Uncertainty in Diffusion Models

The Reasoning-Creativity Trade-off: Toward Creativity-Driven Problem Solving

Conformal Margin Risk Minimization: An Envelope Framework for Robust Learning under Label Noise

Amortized Structural Variational Inference

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer

Generalization Bounds under Heavy-Tailed Losses

Parameter-Efficient Multi-Task Learning via Progressive Task-Specific Adaptation

HGT-FD: Hypergraph transformer for Fraud Detection

Clustering-Based Edge Augmentation for Minimizing the Kirchhoff Index

Auditing Pay-Per-Token in Large Language Models

Differentially Private Clipped-SGD: High-Probability Convergence with Arbitrary Clipping Level

Regularizing attention scores with bootstrapping

Direct Preference Optimization With Unobserved Preference Heterogeneity

Learning to Explore with Lagrangians for Bandits under Unknown Constraints

SenTSR-Bench: Thinking with Injected Knowledge for Time-Series Reasoning

Visual Prompting Reimagined: The Power of Activation Prompts

Optimistic Reinforcement Learning with Quantile Objectives

The Unseen Adversaries: Robust and Generalized Defense Against Adversarial Patches

ConDiSim: Conditional Diffusion Models for Simulation-Based Inference

Deep Feedback Models

WSBD: Freezing-Based Optimizer for Quantum Neural Networks

A Unifying Framework for Unsupervised Concept Extraction

Calibrated Predictive Lower Bounds on Time-to-Unsafe-Sampling in LLMs

Convex Markov Games and Beyond: New Proof of Existence, Characterization and Learning Algorithms for Nash Equilibria

Guided by the Experts: Provable Feature Learning Dynamic of Soft-Routed Mixture-of-Experts

A Gaussian Process View on Observation Noise and Initialization in Wide Neural Networks

High-Probability Bounds for Heterogeneous Local Differential Privacy

Accelerated Learning on Large-Scale Screens using Generative Library Models

Recovery Guarantees for Continual Learning of Dependent Tasks: Memory, Data-Dependent Regularization, and Data-Dependent Weights

TENDE: Transfer Entropy Neural Diffusion Estimation

Calibrated Principal Component Regression

Modeling Multi-Objective Tradeoffs with Monotonic Utility Functions

Explicit Density Approximation for Neural Implicit Samplers Using a Bernstein-Based Convex Divergence

On the Normalization of Confusion Matrices: Methods and Geometric Interpretations

How to Approximate Inference with Subtractive Mixture Models

SQuaT: Self-Supervised Knowledge Distillation via Student-Aware Quantized Teacher Features

Shift is Good: Mismatched Data Mixing Improves Test Performance

Confidence-Guided Self-Training for Gradual Domain Adaptation

ACE-KT: Cascaded Cognitive Modeling for Stage-wise Knowledge Tracing

Differentially Private Linear Regression and Synthetic Data Generation with Statistical Guarantees

Beyond Spectral Clustering: Probabilistic Cuts for Differentiable Graph Partitioning

Deliberate-When-Needed: Flow-Reasoner for Neuro-Symbolic Continuous Thought

Finite-Time Analysis of Gradient Descent for Shallow Transformers

Predictive Deep Sets

ProxRouter: Proximity-Weighted LLM Query Routing for Improved Robustness to Outliers

BASTION: A Bayesian Framework for Trend and Seasonality Decomposition

Optimal Learning in Games under Delayed Feedback

CONTEXTUAL RANKING AND MATCHING. OPTIMAL REGRET UNDER LST

Beyond the Ideal: Analyzing the Inexact Muon Update

Slithering Through Gaps: Capturing Discrete Isolated Modes via Logistic Bridging

Multiple Invertible and Partial-Equivariant Function for Latent Vector Transformation to Enhance Disentanglement in VAEs

Q-Learning with Shift-Aware Upper Confidence Bound in Non-Stationary Reinforcement Learning

Counterfactually Fair Conformal Prediction

Learning Markov Processes as Sum-of-Square Forms for Analytical Belief Propagation

Regularized $f-$Divergence Kernel Tests

Proof of The TAP Free Energy for High-Dimensional Linear Regression with Spherical Priors at All Temperatures

Understanding SAM's Robustness to Noisy Labels through Gradient Down-weighting

PolarQuant: Vector Quantization with Polar Transformation

Latent-IMH: Efficient Bayesian Inference for Inverse Problems with Approximate Operators

Nearly Optimal Best Arm Identification for Semiparametric Bandits

Regression Descent: A Statistical Framework for Neural Network Optimization

Multi-Agent Lipschitz Bandits

On the Finite-Sample Bias of Minimizing Expected Wasserstein Loss Between Empirical Distributions

ZipMoE: A Theoretically-Grounded Mixture of Experts Approach forParameter-Efficient Deep Learning

It’s All In The (Exponential) Family: An Equivalence Between Maximum Likelihood Estimation and Control Variates For Sketching Algorithms

On propagation of chaos for the Fisher-Rao gradient flow in entropic mean-field optimization

Three-Step Nav: A Hierarchical Global–Local Planner for Zero-Shot Vision-and-Language Navigation

Efficient Flow Matching using Latent Variables

Closed-Form Coordinate Ascent Variational Inference for Student-t Process Regression with Student-t Likelihood

From Counts to Preferences: Preference-Driven Models for Spatio-Temporal Event Data

A Correlation Analysis Approach to Finding Interpretable Latent Representations via Conditional Generative Models

Exact Tensor Completion Beyond Isotropy and Invertibility

Learning Right Monotone Permutation Matrices for Neural Subsequence Search

Non-Stationary Functional Bilevel Optimization

Provably Efficient and Agile Randomized Q-Learning

Monotone and Conservative Policy Iteration Beyond the Tabular Case

Retrieval Augmented Time Series Forecasting

The Information Geometry of Local Generalization Dynamics

Breaking Data Symmetry is Needed For Generalization in Feature Learning Kernels

Bayesian Fourier Features for Reduced Rank Gaussian Processes

PowerSoftmax: Towards Secure LLM Inference Over Encrypted Data

CADENT: Gated Hybrid Distillation for Sample-Efficient Transfer in Reinforcement Learning

Adversary-Free Counterfactual Prediction via Information-Regularized Representations

Accelerating PDE Surrogates via RL-Guided Mesh Optimization

On the Hardness of Auditing Model Properties Under Updates: Complexity of Property-Preserving Updates

Black-Box Optimization from Small Offline Datasets via Meta Learning with Synthetic Tasks

Unsupervised Ensemble Learning Through Deep Energy-based Models

Value Gradient Sampler: Learning Invariant Value Functions for Equivariant Diffusion Sampling

Generalization Bounds for Spectral GNNs via Fourier Domain Analysis

Efficient Model Performance Evaluation Using a Combination of Expert and Crowd-sourced Labels

Deep Polynomial Chaos Expansion

On Global Convergence Rates for Federated Softmax Policy Gradient under Heterogeneous Environments

Model Selection for Average Reward RL with Application to Utility Maximization in Repeated Games

RamPINN: Recovering Raman Spectra From Coherent Anti-Stokes Spectra Using Embedded Physics

LLM-as-a-Judge on a Budget

Multiclass Local Calibration with the Jensen-Shannon Distance

Private and Efficient Federated Statistical Learning

TESLA: Taylor Expansion of Sinusoidal Learnable Activations

Aggregation on Learnable Manifolds for Asynchronous Federated Optimisation

Differential Privacy in Kernelized Contextual Bandits via Random Projections

Randomized HyperSteiner: A Stochastic Delaunay Triangulation Heuristic for the Hyperbolic Steiner Minimal Tree

Empirically Calibrated Conditional Independence Tests

MineGrad: Gradient Inversion Attacks on LoRA Fine-Tuning

Discrete State Diffusion Models: A Sample Complexity Perspective

Optimized Projection-Free Algorithms for Online Learning: Construction and Worst-Case Analysis

Numerical Fragility in Transformers: A Layer-wise Theory for Risk Estimation and Selective Stabilization

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

Tractable Uncertainty-Aware Meta-Learning

CAWI: Copula-Aligned Weight Initialization for Randomized Neural Networks

GRANITE: A Generalized Regional Framework for Identifying Agreement in Feature-Based Explanations

Adaptive A/B Testing under Nonstationary Dynamics using State-Space Models

Fundamental Limits for Weighted Empirical Approximations of Exponentially Tilted Distributions

Network Inversion for Extreme-Case Training-Like Data Reconstruction

Robust Federated Clustering under Heterogeneity and Adversaries

On the Neural Feature Ansatz for Deep Neural Networks

Adaptive Memory Momentum via a Model-Based Framework for Deep Learning Optimization

A New Perspective on Minimum-Norm Interpolation Under Gaussian Covariates

CoreSPECT: Enhancing Clustering Algorithms via an Interplay of Density and Geometry

Eliciting Truthful Feedback for Preference-Based Learning via the VCG Mechanism

Dual Averaging Converges for Nonconvex Smooth Stochastic Optimization

Adaptive Diffusion Guidance via Stochastic Optimal Control

Replicable Machine Learning: Theory and Algorithms for Stochastic Convex and Non-Convex Optimization

Efficient Bilevel Optimization with KFAC-Based Hypergradients

A Polynomial-Time Approximation for Pairwise Fair $k$-Median Clustering

UniPROT: Uniform Prototype Selection via Partial Optimal Transport with Submodular Guarantees

Bandits in Flux: Dynamic Regret under Adversarial Constraints

Amortized In-Context Mixed Effect Transformer Models: A Zero-Shot Approach for Pharmacokinetics

One-Step Diffusion Samplers via Self-Distillation and Deterministic Flow

Policy Learning with Abstention

Tractable Gaussian Phase Retrieval with Heavy Tails and Adversarial Corruption with Near-Linear Sample Complexity

On the convergence and straightness of Rectified Flow

Fast and Robust Convergence Rate for TD(0) with Linear Function Approximation, Universal Learning Steps and I.I.D. Samples

From Restless to Contextual: A Thresholding Bandit Reformulation for Finite-horizon Improvement

Functional Properties of the Focal-Entropy

DRAUN: An Optimization-Agnostic Data Reconstruction Attack on Federated Unlearning

Spectral Thresholds in Correlated Spiked Models and Fundamental Limits of Partial Least Squares

Leveraging Machine-Learned Advice in Strategic Interactions with No-Regret Learners

Gradient Descent with Provably Tuned Learning-rate Schedules

On the Bias of Variational Resampling

Q-ShiftDP: A Differentially Private Parameter-Shift Rule for Quantum Machine Learning

Spectral Text Fusion: A Frequency-Aware Approach to Multimodal Time-Series Forecasting

On the Identifiability of Tensor Ranks via Prior Predictive Matching

Learning Equivariant Functions via Quadratic Forms

TexTSC: Class-Texture Preserving Data Condensation for Time Series Classification

Multi-Armed Sampling Problem and the End of Exploration

Fast Private Adaptive Query Answering for Large Data Domains

Is Supervised Learning Really That Different from Unsupervised?

The Cross-Context Threshold Test: Detecting Discrimination Under Environmental Shifts

Scalable Policy Maximization Under Network Interference

Adaptive Candidate Point Thompson Sampling for High-Dimensional Bayesian Optimization

Disentangling Federated Learning Heterogeneity: A Dual-Perspective Analysis of Quantifying Skew versus Scarcity

Out-of-Distribution Generalization of In-Context Learning: A Low-Dimensional Subspace Perspective

Active Measuring in Reinforcement Learning With Delayed Negative Effects

Atlas-based Manifold Representations for Interpretable Riemannian Machine Learning

Robust Learning of A Group DRO Neuron

Provable FDR Control for Deep Feature Selection: Deep MLPs and Beyond

Inverse-Free Sparse Variational Gaussian Processes

Panprediction: Optimal Predictions for Any Downstream Task and Loss

OEUVRE: OnlinE Unbiased Variance-Reduced loss Estimation

On the Number of Conditional Independence Tests in Constraint-based Causal Discovery

On the calibration of survival models with competing risks

Patch2Loc: Learning to Localize Patches for Unsupervised Brain Lesion Detection

A Semi-Supervised Kernel Two-Sample Test

Empirical PAC-Bayes Bounds for Markov Chains

Longitudinal Flow Matching for Trajectory Modeling

AMRM-Pure: Semantic-Preserving Adversarial Purification

Scalable Utility-Aware Multiclass Calibration

Near Optimal Dropout-Robust Sortion

Denoising Score Matching with Random Features: Insights on Diffusion Models From Precise Learning Curves

Simplex-to-Euclidean Bijections for Categorical Flow Matching

Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation

SetPINNs: Set-based Physics-informed Neural Networks

A Continuous Time Markov Chain Framework for Insertion Language Models

RealStats: A Rigorous Real-Only Statistical Framework for Fake Image Detection

Distribution free M-estimation

High-Performance Self-Supervised Representation Learning by Joint Training of Flow Matching and Representation Encoder

VIPaint: Image Inpainting with Pre-Trained Diffusion Models via Variational Inference

Open Multi-agent Multi-armed Bandit with Applications in Permissionless Blockchain

KQ-SVD: Compressing the KV Cache with Provable Guarantees on Attention Fidelity

Robustness and Generalization in Uncertainty-Aware Message Passing Neural Networks

Improving Adaptive Moment Optimization via Preconditioner Diagonalization

Understanding the Benefits of SimCLR Pre-Training in Two-Layer Convolutional Neural Networks

Adversarial Debiasing for Parameter Recovery

Securing Model Weights Against Eavesdropping Adversaries in Federated Learning Using Quantization

Active Measurement of Two-Point Correlations

Frequency-Based Hyperparameter Selection in Games

Provable Affine Identifiability of Nonlinear CCA under Latent Distributional Priors

Local Inconsistency Resolution: The Interplay between Attention and Control in Probabilistic Models