NeurIPS 2025PastMath & reasoning
NeurIPS 2025 Workshop MLxOR: Mathematical Foundations and Operational Integration of Machine Learning for Uncertainty-Aware Decision-Making
NeurIPS 2025 Workshop MLxOR
- Submission deadline
- Sep 6, 2025, 11:59 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (147)
Fetched from OpenReview (v2) on 2026-06-10.
A Behavioral Model for Exploration vs. Exploitation: Theoretical Framework and Experimental Evidence
A Covering Framework for Offline POMDPs Learning using Belief Space Metric
A Deep Proactive Exploration Policy Based on Asymptotic Statistics for Asynchronous Q-Learning
A Dual Perspective on Decision Focused Learning
A Near-Optimal Control Policy for Data-driven Assemble-to-order Systems
A Sharp Comparison of Prescriptive Analytic Frameworks for The Big Data Newsvendor Problem
A Theoretical Framework for Auxiliary-Loss-Free Load-Balancing of Sparse Mixture-of-Experts in Large-Scale AI Models
A Variance-Adaptive Lower Bound for Simulation Optimization in Continuous Space
Accelerating Diffusion via Compressed Sensing: Applications to Imaging and Finance
Achieving $\widetilde{\mathcal O}(1/N)$ Optimality Gap in Weakly-Coupled Markov Decision Processes through Gaussian Approximation
Achieving Exponential Asymptotic Optimality in Average-Reward Restless Bandits without Global Attractor Assumption
Achieving First-Order Statistical Improvements in Data-Driven Optimization
Active Learning for Stochastic Contextual Linear Bandits
Adaptive Frontier Exploration on Graphs with Applications to Network-Based Disease Testing
Adaptive Resolving Methods for Reinforcement Learning with Function Approximations
Admissibility of Completely Randomized Trials: A Large-Deviation Approach
Algorithmic Aspects of Strategic Trading
Almost Sure Convergence of Nonlinear Stochastic Approximation Under General Moment Conditions
Autoregressive Learning under Joint KL Analysis: Horizon-Free Approximation and Computational-Statistical Tradeoffs
Batch-Adaptive Annotations for Causal Inference with Text-Based Outcomes
Bayesian Optimization using Partially Observable Gaussian Process Network
Bayesian Surrogates for Risk-Aware Pre-Assessment of Aging Bridge Portfolios
Belief-Aware Inventory Control with Deep Mixture Models
Bellman Optimality of Average-Reward Robust Markov Decision Processes with a Constant Gain
Beyond First-Order: Training LLMs with Stochastic Conjugate Subgradient and AdamW
Blessings of many good arms in multi-objective linear bandits
Can Linear Probes Measure LLM Uncertainty ?
Chance-constrained Flow Matching for High-Fidelity Constraint-aware Generation
Compound Poisson Limits in Weighted Bernoulli Congestion Games: Theory Meets Experiments
Conformal Tail Risk Control for Large Language Model Alignment
Conformalized Decision Risk Assessment
Confounding-Robust Fitted-Q-Iteration under Observed Markovian Marginals
Contextual Bandits for Large-Scale Structured Discrete Constrained Optimization Problems
Contextual Budget Bandit for Food Rescue Volunteer Engagement
Contextual Optimization Under Model Misspecification: A Tractable and Generalizable Approach
Contextual Pricing with Heterogeneous Buyers
Contextual Value Iteration and Deep Approximation for Bayesian Contextual Bandits
DAOpt: Modeling and Evaluation of Data-Driven Optimization under Uncertainty with LLMs
Data to Dose: Efficient Synthetic Data Generation with Expert Guidance for Personalized Dosing
Data-driven generative simulation of SDEs using diffusion models
Data-Driven Sequential Search
Data-Driven Stochastic Modeling Using Autoregressive Sequence Models: Translating Event Tables to Queueing Dynamics
Decision Focused Scenario Generation for Contextual Two-Stage Stochastic Linear Programming
Decision-Focused Sequential Experimental Design: A Directional Uncertainty-Guided Approach
Deep Learning for Solving Linear Integral Equations Associated with Markov Chains
Deep Learning-Driven Contextual Stochastic Optimization for Real-Time Order Fulfillment
DeepStock: Reinforcement Learning with Policy Regularizations for Inventory Management
Differentiable Optimization for Deep Learning-Enhanced DC Approximation of AC Optimal Power Flow
Diffusion Generative Models meet Differential Privacy: A Theoretical Insight
Diffusion Models for Adapted Sequential Data Generation
Distributionally Robust Multimodal Machine Learning
Distributionally Robust Optimization via Iterative Algorithms in Continuous Probability Spaces
Distributionally Robust Regularization of Sparse Integer Programming Trained Learning Models
Dynamically Augmented CVaR for MDPs and Uncertainty Quantifications for Robust MDPs Characterizing Risk
Efficient Rashomon Set Approximation for Decision Trees
End-to-End Learning for Information Gathering
Ensuring Fairness in Priority-Based Admissions with Uncertain Scores
Estimate to Decide: Matrix Completion driven Smoothed Online Quadratic Optimization
Estimation of Treatment Effects under Nonstationarity via the Truncated Policy Gradient Estimator
Everyone Contributes! Incentivizing Strategic Cooperation in Multi-LLM Systems via Sequential Public Goods Games
Exploration via Feature Perturbation in Contextual Bandits
Fairness Is More Than Algorithms: Racial Disparities in Time-to-Recidivism
FairSVM: A Mixed-Integer Programming Framework for Fairness-Constrained Support Vector Machines
Fast Variability Approximation: Speeding up Divergence-Based Distributionally Robust Optimization via Directed Perturbation
Federated Calculation of the Transportation Barycenter by a Dual Subgradient Method
Fine-Grained Prototype-Based Interpretability for Operational Text Classification
Finite-Time Minimax Bounds in Queueing Control
Flow-based Conformal Prediction for Multi-dimensional Time Series
FlowGINO: Continuous Reconstruction from Sparse Observations along with Aleatoric and Epistemic Uncertainty Estimation
Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality
From Stacked Predictions to Decisions: A Contextual Optimization Approach
Gala: Global LLM Agents for Text-to-Model Translation
Geometric Data Valuation via Leverage Scores
Heterogeneous Treatment Effects in Panel Data
Hierarchical Implicit/Explicit Feedback Recommender System
Human-AI Interaction in Product Recommendation
Human-Centric Perishable Inventory Management with AI-Assistance
Instance-dependent Sample Complexity for Bilinear Saddle-Point Optimization with Noisy Feedback: An LP-Based Approach
Integrating qualitative data into transit service design: a stochastic estimate-then-optimize approach
Joint Pricing and Resource Allocation: An Optimal Online-Learning Approach
k-SVD with Gradient Descent
Landmark-Based Node Representations for Shortest Path Distance Approximations in Random Graphs
Landscape of Policy Optimization for Finite Horizon MDPs with General State and Action
Learning Fair And Effective Points-Based Rewards Programs
Learning from a Biased Sample
Learning to Handle Constraints in Routing Problems via a Construct-and-Refine Framework
Learning to Optimize at Scale: A Benders Decomposition-TransfORmers Framework for Stochastic Combinatorial Optimization
Learning to Select and Rank from Choice-Based Feedback: A Simple Nested Approach
LISTEN to Your Preferences: An LLM Framework for Multi-Objective Selection
Lyapunov-Based Sample Complexity Analysis for Weakly-Coupled MDPs
Measuring Informativeness Gap of (Mis)Calibrated Predictors
Mechanistic Interpretability for Neural TSP Solvers
Mechanistic Modeling of Social Conditions in Disease-Prediction Simulations via Copula-Informed Probabilistic Graphical Models: HIV Case Study
MINTS: Minimalist Thompson Sampling
Mixed Integer Programming for Change-point Detection
Model-Free Assessment of Simulator Fidelity via Quantile Curves
Multi-Armed Bandits With Machine Learning-Generated Surrogate Rewards
Near-Optimal Real-Time Personalization with Simple Transformers
Neural Decision Rule for Constrained Contextual Stochastic Optimization
Non‑Asymptotic Guarantees for Average‑Reward Q‑Learning with Adaptive Stepsizes
Offline Contextual Bandits with Covariate Shift
Offline Dynamic Pricing under Covariate Shift and Local Differential Privacy via Twofold Pessimism
Online Decision Making with Generative Action Sets
Online Learning for Dynamic Service Mode Control
Online Statistical Inference of Constrained Stochastic Optimization via Random Scaling
Optimality of Linear Policies in Distributionally Robust Linear Quadratic Control
Optimization-Driven XGBoost-PINN Framework for Building Temperature Prediction
Optimizing LLM Inference: Fluid-Based Online Scheduling under Memory Constraints
Overfitting in Adaptive Robust Optimization
Perishable Online Inventory Control with Context-Aware Demand Distributions
Plan for the Worst With Advice: Advice-Augmented Robust Markov Decision Processes
Policy Gradient Optimization for Markov Decision Processes with Epistemic Uncertainty and General Loss Functions
Post-Estimation Adjustments in Data-Driven Decision-Making with Applications in Pricing
Prediction-Driven Staffing for Emergency Departments: What to Predict and How to Predict
Preference-based Reinforcement Learning beyond Pairwise Comparisons: Benefits of Multiple Options
Probabilistic Soundness Guarantees in LLM Reasoning Chains
Provable Reinforcement Learning from Human Feedback with an Unknown Link Function
Pure Exploration via Frank--Wolfe Self-Play
Q-learning with Posterior Sampling
Quantifying policy uncertainty in generative flow networks with uncertain rewards
Rebalancing and Clearance Pricing of Near-Expiry Inventory in Online Grocery Retail
Reducing Contextual Stochastic Bilevel Optimization via Structured Function Approximation
Reinforcement Learning for Intensity Control: An Application to Choice-Based Network Revenue Management
Reusing Historical Trajectories in Natural Policy Gradient via Importance Sampling: Convergence and Convergence Rate
Revisiting Follow-the-Perturbed-Leader with Unbounded Perturbations in Bandit Problems
RiskPO: Risk-based Policy Optimization with Verifiable Reward for LLM Post-Training
Robust Offline Reinforcement Learning with Linearly Structured f-Divergence Regularization
Robust Strategic Classification under Decision-Dependent Cost Uncertainty
Safe Start: Configuring Optimization Algorithms for Decision-Making under Extreme Risks
Sample Complexity of Distributionally Robust Off-Dynamics Reinforcement Learning with Online Interaction
Scalable First-order Method for Certifying Optimal k-Sparse GLMs
Selective Cost-Aware Random Forests for Unreliable Data
Self-Normalized Resets for Plasticity in Continual Learning
SOCRATES: Simulation Optimization with Correlated Replicas and Adaptive Trajectory Evaluations
SOLID: a Framework of Synergizing Optimization and LLMs for Intelligent Decision-Making
Statistical Properties of Robust Optimization under Distribution Shifts
Structure-Informed Deep Reinforcement Learning for Inventory Management
Structured Difference-of-Q via Orthogonal Learning
Tail-Optimized Caching for LLM Inference
The Oversight Game: Learning AI Control and Corrigibility in Markov Games
Towards Efficient Foundation Model: A Novel Time Series Embedding
Training Deep-Parametric Policies Using Lagrangian Duality
Transformer-Based Next-Step Prediction for Queue Length Distribution
Uncertainty Estimation using Variance-Gated Distributions
Understanding Scaling Laws via Neural Feature Learning Dynamics
Variational Generative Modeling of Stochastic Point Processes
Who Should Do What? Adaptive Delegation in Human-AI Collaboration