2/20/2026
genriesz: A Python Package for Automatic Debiased Machine Learning with Generalized Riesz Regression
Open-source Python package implementing debiased machine learning via Riesz regression for causal parameter estimation.
2/20/2026
Adaptive Decentralized Composite Optimization via Three-Operator Splitting
Decentralized optimization method with adaptive stepsizes for multi-agent networks using three-operator splitting.
2/20/2026
Learning to Stay Safe: Adaptive Regularization Against Safety Degradation during Fine-Tuning
Adaptive regularization framework for maintaining LLM safety during fine-tuning while preserving utility.
2/20/2026
Optimal Unconstrained Self-Distillation in Ridge Regression: Strict Improvements, Precise Asymptotics, and One-Shot Tuning
Analysis of self-distillation in ridge regression showing generalization improvements with precise asymptotics.
2/20/2026
Simultaneous Blackwell Approachability and Applications to Multiclass Omniprediction
Omniprediction algorithm extending binary case to multiclass settings with bounds across multiple losses.
2/20/2026
Conditional Flow Matching for Continuous Anomaly Detection in Autonomous Driving on a Manifold-Aware Spectral Space
Unsupervised anomaly detection framework using conditional flow matching for autonomous vehicle safety validation.
2/20/2026
Asymptotically Optimal Sequential Testing with Markovian Data
Sequential hypothesis testing algorithm for Markovian data with asymptotically optimal expected stopping time.
2/20/2026
Adapting Actively on the Fly: Relevance-Guided Online Meta-Learning with Latent Concepts for Geospatial Discovery
Online meta-learning approach for geospatial discovery using latent concepts with strategic sampling under constraints.
2/20/2026
AutoNumerics: An Autonomous, PDE-Agnostic Multi-Agent Pipeline for Scientific Computing
Multi-agent autonomous framework for designing, implementing, and verifying numerical PDE solvers without manual tuning.
2/20/2026
Mine and Refine: Optimizing Graded Relevance in E-commerce Search Retrieval
Contrastive learning framework for semantic embeddings in e-commerce search with graded relevance.
2/20/2026
Sink-Aware Pruning for Diffusion Language Models
Pruning technique for diffusion language models reducing inference cost by reconsidering attention sink preservation.
2/20/2026
Data Augmentation Scheme for Raman Spectra with Highly Correlated Annotations
Data augmentation scheme for Raman spectroscopy with correlated annotations in biotechnology applications.
2/20/2026
Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects
Graph machine learning method for estimating causal effects in social networks with interference and confounding.
2/20/2026
Gradient Testing and Estimation by Comparisons
Algorithm for gradient testing and estimation using only comparison oracle for smooth functions.
2/20/2026
Risk-Aware Decision Making in Restless Bandits: Theory and Algorithms for Planning and Learning
Risk-aware decision making algorithms for restless bandits incorporating downside risk mitigation.
2/20/2026
Defining and Evaluating Physical Safety for Large Language Models
Benchmark for evaluating physical safety risks of LLMs controlling robotic systems like drones with threat classification.
2/20/2026
Point-DeepONet: Predicting Nonlinear Fields on Non-Parametric Geometries under Variable Load Conditions
Surrogate model combining PointNet and DeepONet for predicting nonlinear fields on complex 3D geometries.
2/20/2026
Nearly-Optimal Bandit Learning in Stackelberg Games with Side Information
Online learning algorithm for Stackelberg games with contextual information achieving improved regret bounds.
2/20/2026
Self-Improving Skill Learning for Robust Skill-based Meta-Reinforcement Learning
Hierarchical meta-reinforcement learning approach using self-improving skills to handle noisy offline demonstrations.
2/20/2026
Rex: A Family of Reversible Exponential (Stochastic) Runge-Kutta Solvers
Reversible Runge-Kutta solvers for neural differential equations in generative models with improved numerical stability.
2/20/2026
Unlocking [CLS] Features for Continual Post-Training
Continual learning approach for foundation models addressing stability-plasticity trade-off during post-training on new classes/domains.
2/20/2026
Efficient Orthogonal Fine-Tuning with Principal Subspace Adaptation
Parameter-efficient fine-tuning method using orthogonal adaptation on principal subspaces for adapting large models efficiently.
2/20/2026
Oversmoothing, Oversquashing, Heterophily, Long-Range, and more: Demystifying Common Beliefs in Graph Machine Learning
Survey demystifying common beliefs about oversmoothing, oversquashing, heterophily and long-range tasks in graph neural networks.
2/20/2026
On the Design of KL-Regularized Policy Gradient Algorithms for LLM Reasoning
Analyzes KL-regularization design choices in policy gradient algorithms for LLM reasoning, comparing forward/reverse KL variants.
2/20/2026
Supervised Graph Contrastive Learning for Gene Regulatory Networks
Supervised graph contrastive learning framework for gene regulatory networks addressing biological validity of perturbations.
2/20/2026
Two-Player Zero-Sum Games with Bandit Feedback
Game theory algorithms for two-player zero-sum games with unknown payoffs estimated through bandit feedback.
2/20/2026
Generating Directed Graphs with Dual Attention and Asymmetric Encoding
Generative model for directed graphs using dual attention and asymmetric encoding to capture ordered relationships.
2/20/2026
Strict Subgoal Execution: Reliable Long-Horizon Planning in Hierarchical Reinforcement Learning
Strict Subgoal Execution method for hierarchical RL that improves long-horizon planning by validating subgoal feasibility.
2/20/2026
AXLearn: Modular, Hardware-Agnostic Large Model Training
AXLearn production system for scalable hardware-agnostic training of large models with modular software architecture.
2/20/2026
Instance-Wise Adaptive Sampling for Dataset Construction in Approximating Inverse Problem Solutions
Instance-wise adaptive sampling framework for constructing efficient training datasets for supervised inverse problem learning.
2/20/2026
Continuous-Time Value Iteration for Multi-Agent Reinforcement Learning
Extends reinforcement learning to continuous-time systems using Hamilton-Jacobi-Bellman equations for irregular interaction frequencies.
2/20/2026
Advancing Universal Deep Learning for Electronic-Structure Hamiltonian Prediction of Materials
Deep learning methods for predicting electronic-structure Hamiltonians in materials, advancing generalization across diverse atomic systems.
2/20/2026
Watermarking Diffusion Language Models
First watermarking method for diffusion language models that generate tokens non-sequentially, addressing unique DLM challenges.
2/20/2026
Entropy After $\langle \texttt{/Think} \rangle$ for reasoning model early exiting
Analyzes overthinking in reasoning LLMs and proposes early exit using entropy after thinking tags to improve efficiency.
2/20/2026
Multimodal Prompt Optimization: Why Not Leverage Multiple Modalities for MLLMs
Extends prompt optimization to multimodal LLMs, proposing methods to optimize across text, images, video and other modalities.
2/20/2026
ArtNet: Hierarchical Clustering-Based Artificial Netlist Generator for ML and DTCO Application
Hardware design tool using ML to generate synthetic netlists for chip optimization training without long design turnaround times.
2/20/2026
Contrastive Diffusion Alignment: Learning Structured Latents for Controllable Generation
Introduces ConDA, a contrastive learning layer for organizing diffusion model latent spaces to enable controllable generation.
2/20/2026
pi-Flow: Policy-Based Few-Step Generation via Imitation Distillation
Proposes pi-Flow, a policy-based approach to improve few-step diffusion model distillation by predicting network-free policies.
2/20/2026
Alternatives to the Laplacian for Scalable Spectral Clustering with Group Fairness Constraints
ML research on mitigating algorithmic bias in clustering through fairness constraints and group balance representation.
2/20/2026
Accelerating Mobile Inference through Fine-Grained CPU-GPU Co-Execution
System for accelerating neural network inference on mobile devices through fine-grained CPU-GPU co-execution and synchronization optimization.
2/20/2026
LRT-Diffusion: Calibrated Risk-Aware Guidance for Diffusion Policies
LRT-Diffusion applies risk-aware sequential hypothesis testing to improve diffusion policy guidance for offline reinforcement learning.
2/20/2026
Semi-Supervised Preference Optimization with Limited Feedback
Semi-supervised preference optimization framework for LLM alignment using limited labeled paired feedback data.
2/20/2026
Efficient Reinforcement Learning for Large Language Models with Intrinsic Exploration
PREPO method improving data efficiency for LLM reinforcement learning with verifiable rewards using intrinsic exploration signals.
2/20/2026
Beyond Linear Surrogates: High-Fidelity Local Explanations for Black-Box Models
Model-agnostic local explanation method using MARS and N-ball sampling for high-fidelity black-box model interpretability.
2/20/2026
Online Robust Reinforcement Learning with General Function Approximation
Distributionally robust reinforcement learning approach using general function approximation for policy robustness under environment shift.
2/20/2026
On the Sample Complexity of Learning for Blind Inverse Problems
Theoretical analysis of sample complexity for data-driven approaches to blind inverse problems with interpretability concerns.
2/20/2026
Reinforcement Learning to Discover a North-East Monsoon Index for Rainfall Prediction in Thailand
Reinforcement learning approach to discover local climate indices for improving rainfall prediction in Thailand.
2/20/2026
Learning PDE Solvers with Physics and Data: A Unifying View of Physics-Informed Neural Networks and Neural Operators
Unified framework connecting physics-informed neural networks and neural operators for learning PDE solvers.
2/20/2026
Active Learning for Decision Trees with Provable Guarantees
Theoretical analysis of active learning label complexity for decision trees with provable polylogarithmic guarantees.
2/20/2026
Temporal Graph Pattern Machine
Temporal graph pattern machine for learning transferable representations in dynamic networks without restrictive assumptions.