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