Distributions shift, chatbots get jail-broken, users game algorithms — how do we build reliable machine learning
when data are missing, corrupted, or strategically manipulated?
This workshop bridges theory and practice to
tackle these challenges, bringing together researchers working on distribution shift, adversarial robustness,
and strategic behaviour to chart principled yet deployable solutions for Reliable ML from Unreliable Data.
Schedule (Sat Dec 6, 2025)
All times are local San Diego time (PST).
Start
End
Agenda
8:00 AM
8:30 AM
Invited talk by Chris Harshaw Title: The Conflict Graph Design: Estimating Causal Effects Under Network Interference
8:30 AM
9:00 AM
Invited talk by Surbhi Goel Title: Reliable Human-AI Collaboration via Agreement and Competition
9:00 AM
9:30 AM
Coffee Break
9:30 AM
10:00 AM
Invited talk by Samory Kpotufe Title: Mixed-Sample SGD: an end-to-end Analysis of Supervised Transfer Learning
10:00 AM
10:30 AM
Best-paper Talks
10:30 AM
11:00 AM
Invited talk by Amin Karbasi Title: Adversarial Reasoning
Cash prizes for the best paper awards are generously sponsored by
Intelligible
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.
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.
Organizers
Andrew Ilyas
Stanford / CMU
Alkis Kalavasis
Yale University
Anay Mehrotra
Yale University
Manolis Zampetakis
Yale University
Program Committee
We thank our reviewers for their valuable contributions
