Reliable ML from Unreliable Data

Call for Papers

We invite work that advances theory, empirical understanding, or systems design for robust and reliable machine learning under imperfect data – including, but not limited to, the following topics:

• Distribution shift and transfer learning
• Adversarial robustness and defenses
• Strategic behavior in socio-technical systems
• Learning with missing or biased data; and truncated statistics
• Causal inference beyond overlap, with confounders, or with errors
• LLM safety and alignment
• Robustness in interactive environments

Submissions may report new results, negative findings, benchmarks, or visionary perspectives.

• Long track: up to 9 pages (excluding references).
• Short track: up to 4 pages (excluding references).
• Formatting: use the official NeurIPS 2025 style files and submit a single PDF (which should be anonymized, like NeurIPS submissions; see these instructions on anonymization).
• Appendix: include any supplementary material in the same PDF — no page limit for the appendix.

Important Dates

• Submission deadline: Fri Aug 22 2025 (AoE)
• Notification of decisions: Mon Sep 22 2025 (AoE)
• Workshop: Sat Dec 6 or Sun Dec 7 2025 (at NeurIPS 2025)

Submit via OpenReview (link coming soon). The workshop is non‑archival; authors are free to publish revised versions elsewhere. Every submission will receive at least two reviews from our program committee, and accepted papers will be presented as talks or posters.