We invite the submission of research papers and position papers for our ICML 2026 workshop on Decision-Making from Offline Datasets to Online Adaptation. This workshop aims to explore methods for learning policies, acquisition strategies, and decision rules entirely from previously collected data (offline) or with a small amount of new real-world data (online), spanning settings such as black-box optimization, contextual bandits, reinforcement learning (RL), and their synergies. The workshop will highlight both foundational advances and real-world applications in domains where online experimentation is costly, unsafe, or infeasible, including scientific discovery, engineering design, healthcare, education, recommender systems, and beyond.
Important Dates
Paper Submission Deadline: May 5th, 2026, AoE
Author Notification: May 15, 2026, AoE
Camera Ready Deadline: June 15, 2026, AoE
Presentation Format
Accepted papers will be presented as posters, with a subset selected for spotlight oral presentations.
Archival Policy
All accepted papers will be made publicly available as non-archival reports, allowing for future submission to archival conferences or journals.
Submission Instructions
Please submit your papers via the OpenReview submission website.
Topics of Interest
Topics of interest include, but are not limited to:
Offline RL: Algorithms, theory, and applications of RL trained from offline datasets, including long-horizon and safety-constrained settings.
Offline RL for Foundation Models: RLHF, reasoning model training, and alignment using offline data.
Black-Box Optimization from Offline Data: Model-based optimization and high-throughput experimental design in few- or single-round settings.
Contextual Bandits from Logged Data: Learning and evaluation using large-scale interaction logs.
Off-Policy Evaluation and Policy Comparison: Reliable evaluation, confidence estimation, and counterfactual reasoning.
Hybrid Offline-to-Online Learning: Methods combining offline datasets with limited online interaction.
Uncertainty Quantification for Offline Decision-Making: Conformal prediction and risk-aware learning.
Causal Inference from Observational Data: Leveraging causal structure for improved decision-making.
Generative Models for Decision-Making: Deep generative approaches for policy learning and design optimization.
Multi-Task and Multi-Objective Learning: Scaling offline methods across tasks and objectives.
Benchmarks and Evaluation Protocols: Realistic datasets and metrics reflecting real-world deployment challenges.
Applications in Science and Engineering: Materials discovery, drug design, chip design, robotics, healthcare, education, and industrial systems.
Submission Types
Full Papers: Up to 9 pages in ICML or NeurIPS format, describing mature research contributions with thorough empirical or theoretical analysis.
Short Papers: 2–4 pages in ICML or NeurIPS format, presenting preliminary results, novel ideas, or position papers (including demos, code, or benchmarks).