Full Papers
Up to 9 pages in ICML or NeurIPS format, describing mature research contributions with thorough empirical or theoretical analysis.
Machine Learning/CORE Unranked
DEMO 2026
Decision-Making from Offline Datasets to Online Adaptation: Black-Box Optimization to Reinforcement Learning
The ICML 2026 Workshop on Decision-Making from Offline Datasets to Online Adaptation brings together researchers from academia and industry to explore methods for learning decision policies from offline data, with synergies between offline reinforcement learning, black-box optimization, and related fields. It emphasizes safe, efficient, and scalable approaches for domains where online experimentation is costly or infeasible, such as healthcare, scientific discovery, and engineering design.
Key deadlines
Verified September 9, 2026Full paper
May 9, 2026
AoE
Conference timeline
Submission and decisions
Full paperKey deadline
May 9, 2026 · AoE
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Short Papers
2–4 pages in ICML or NeurIPS format, presenting preliminary results, novel ideas, or position papers (including demos, code, or benchmarks).
Research areas in scope
01
Topics of Interest
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.
Policies worth checking twice
- All accepted papers will be made publicly available as non-archival reports, allowing for future submission to archival conferences or journals.
- Papers must be submitted via the OpenReview submission website.
- Papers must be formatted in ICML or NeurIPS style.
- Accepted papers will be presented as posters, with a subset selected for spotlight oral presentations.
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026