RobustML Workshop

Workshop at ICLR 2021

May 7 (Fri) 8am - 5pm (Pacific Time)

RobustML is an online workshop on robust and reliable Machine Learning in the real world, at the ICLR 2021 conference. It is on May 7th (Fri) 8am - 5pm (Pacific Time, GMT-7).

Official link:

A 1-day workshop of talks, forums, and poster sessions

2 Poster Sessions

Gather.Town Link

5 Invited Talks & 1 Forum

30-min each talk + 15-min QA

5 Oral Presentations

Presentation of selected papers

Schedule (Pacific time, GMT-7)

8:05 - Opening remarks

Opening remarks by Aditi Raghunathan (Stanford)

8:15 - Oral Presentation 1

Contributed talk: "On the Benefits of Defining Vicinal Distributions in Latent Space"
Puneet Mangla, Vedant Singh, Shreyas Havaldar, Vineeth Balasubramanian

8:30 - Talk 1: Ece Kamar

Microsoft (Senior principle research area manager)

8:30 - Invited talk: Ece Kamar

Ece Kamar is a Senior Principal Research Area Manager at Microsoft Research. She focuses on human-centered AI and manage the Adaptive Systems and Interaction Group. She earned her Ph.D. in computer science from Harvard University advised by Prof. Barbara Grosz.

9:00 - Live Q&A: Ece Kamar

Moderator: Aditi Raghunathan (Stanford)

9:15 - Talk 2: Kendra Albert

Harvard Law School (Clinical instructor & lawyer)

9:15 - Invited talk: Kendra Albert

Kendra Albert is a Clinical Instructor at the Cyberlaw Clinic and was formerly an associate at Zeitgeist Law PC, a boutique technology law firm in San Francisco.

9:45 - Live Q&A: Kendra Albert

Moderator: Aditi Raghunathan (Stanford)

10:00 - Poster session 1

Gather Town link

11:00 - Oral Presentation 2

Contributed talk: "Neural Lower Bounds for Verification"
Florian Jaeckle, M. Pawan Kumar

11:15 - Talk 3: Percy Liang

Stanford University (Associate Professor)

11:15 - Invited talk: Percy Liang

Percy Liang is an associate professor at Stanford University. He focuses on developing trustworthy systems that can communicate effectively with people and improve over time through interaction.

11:45 - Live Q&A: Percy Liang

Moderator: Zhijing Jin (Max Planck Institute)

12:00 - Panel discussion

Panel discussion: "Real world challenges for robustness in machine learning"

Moderator: Zhijing Jin (Max Planck Institute)

Panelist: Kendra Albert, Finale Doshi-Velez, Ece Kamar, Nicolas Papernot

Kendra Albert


Finale Doshi-Velez


Ece Kamar


Nicolas Papernot


13:00 - Break

14:00 - Oral Presentation 3

Contributed talk: "Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks"
Curtis G Northcutt, Anish Athalye, Jonas Mueller

14:15 - Talk 4: Ruoxi Jia

Virginia Tech

Ruoxi Jia is a professor at Virginia Tech. Her research interests lie broadly in the span of machine learning, security, privacy, and cyber-physical systems.

Moderator: Eric Wong (MIT)

14:45 - Oral Presentation 4

Contributed talk: "A Causal Lens for Controllable Text Generation"

15:00 - Poster session 2

Gather Town link

16:00 - Oral Presentation 5

Contributed talk: "On Calibration and Out-of-Domain Generalization"
Yoav Wald, Amir Feder, Daniel Greenfeld, Uri Shalit

16:15 - Talk 5: Bo Li


16:15 - Invited talk: Bo Li

Bo Li is an assistant professor at University of Illinois at Urbana-Champaign. Her research focuses on machine learning, security, privacy, and game theory, including exploring vulnerabilities of machine learning systems to various adversarial attacks, and endeavors to develop real-world robust learning systems.

16:45 - Live Q&A: Bo Li

Moderator: Eric Wong (MIT)

17:00 - Closing remarks

Closing remarks by Eric Wong (MIT)


Di Jin

Amazon (Applied Scientist)

Eric Wong

MIT (postdoc)

Tristan Naumann

Microsoft (Principal Researcher)

Mohit Bansal

UNC Chapel Hill (Associate Professor)

Yonatan Belinkov

Technion (Assistant Professor)

Kai-Wei Chang

UCLA (Assistant Professor)

Yanjun Qi

University of Virginia (Associate Professor)

Zhijing Jin

PhD (Max Planck Institute)

Aditi Raghunathan

PhD (Stanford)

Yixin Nie

UNC Chapel Hill (PhD)

Join our Slack to interact with organizers and speakers!