• Deadline for Funding Application – August 23, 2023 (11:59 pm AoE)

• Deadline for Registration – August 31, 2023 (11:59 pm AoE)

• Abstract Submissions due – September 4, 2023 (11:59 pm AoE)

• Workshop Date – September 23-24, 2023

You can register to the workshop through this form.

For those interested in applying for travel or accommodation funding, please complete this form. 

Bear in mind, our resources are limited, and the final decision will be contingent upon the number of requests received and the amount of funding requested. 

 We encourage paper submissions regarding robustness and reliability of ML systems in the real world, and highlight emerging directions, paradigms, and applications which include:

• Different perspectives on robustness:

  • Robustness to adversarial manipulation of models and data.

  • Robustness to distribution shifts.

  • The notions of robustness in the unsupervised learning landscape.

• Reliability of Real-world Systems:

  • Exploring uncertainty quantification for enhancing system reliability.

  • Reviewing the role of challenging datasets and simulations in gauging system reliability.

  • Evaluating the potential and limitations of system properties verification for reliability assurance.

• Societal and Legal Considerations:

  • Discussing legal implications of ML robustness, including hacking liabilities and performance guarantees.

  • Investigating strategies for building robust systems that promote fairness and avoid bias.

  • Exploring robustness requirements in high-stake domains like manufacturing, autonomous driving, and healthcare

Time Zone: GMT +02

(Tentative Schedule)

Day 1 (23rd September 2023)

8:30-9:15 Registration + Coffee break

9:15-9:30 Opening remarks - Professors Chris Holmes, Yee Whye Teh, Sami Kaski

9:30-10:30 Keynote 1 - Aaditya Ramdas (Distribution-free uncertainty quantification
for black-box predictors
under distribution drifts/shifts)

10:30-11:00 Coffee break

11:00-11:45 Guest 1 - Chris Williams (Automating Data Science)

11:45-12:30 Guest 2 - Arthur Gretton (Causal effect estimation with hidden confounders using instruments and proxies)

12:30-13:30 Lunch

13:30-14:15 Guest 3 - Isabel Valera (Causality for robust and ethical machine learning: from theory to practice)

14:15-15:00 Guest 4 - Chris Russell (Monsters from the I.D.)

15:00-15:30 Coffee break

15:30-16:30 Poster Session

16:30-17:30 Keynote 2 - Emmanuel Candes (Model-free selective inference)

19:00-21:00 Dinner at 10 kerros (Take elevator to 10th floor, Kaivokatu 3, Helsinki; next to Helsinki central railway station). Google map link

Day 2 (24th September 2023)

9:00-9:30 Coffee break

9:30-10:30 Keynote 3 - Kevin Murphy (Adapt or die: online and unsupervised learning for test-time adaptation)

10:30-11:00 Coffee break

11:00-11:45 Guest 5 - Maneesh Sahani (Recognition-parametrised learning: identifying latent probabilistic structure without generation)

11:45-12:30 Guest 6 - Eric Nalisnick (Learning to defer to one, multiple, or a population of expert(s))

12:30-13:30 Lunch

13:30-13:40 Closing remarks - Professors Chris Holmes, Yee Whye Teh, Sami Kaski

Venue

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