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This workshop aims to bring together researchers from academia and industry to discuss major challenges, outline recent advances, and highlight future directions pertaining to novel and existing large-scale real-world experiment design and active learning problems. We aim to highlight new and emerging research opportunities for the machine learning community that arise from the evolving needs to make experiment design and active learning procedures that are theoretically and practically relevant for realistic applications.
The intended audience and participants include everyone whose research interests, activities, and applications involve experiment design, active learning, bandit/Bayesian optimization, efficient exploration, and parameter search methods and techniques. We expect the workshop to attract substantial interest from researchers working in both academia and industry. The research of our invited speakers spans both theory and applications, and represents a diverse range of domains where experiment design and active learning are of fundamental importance (including robotics & control, biology, physical sciences, crowdsourcing, citizen science, etc.).
The schedule is with respect to UTC (i.e., Universal Time) time zone.
Sat 7:00 a.m. - 7:10 a.m.
|
Opening Statements
(
Discussion Panel
)
|
🔗 |
Sat 7:15 a.m. - 7:50 a.m.
|
"Latent Space Optimization with Deep Generative Models"
(
Invited Talk
)
SlidesLive Video » |
Jose Miguel Hernandez-Lobato 🔗 |
Sat 7:50 a.m. - 7:55 a.m.
|
Q&A
(
(w/ Jose M.H. Lobato)
)
|
🔗 |
Sat 8:00 a.m. - 8:35 a.m.
|
"Designing Bayesian-Optimal Experiments with Stochastic Gradients"
(
Invited Talk
)
SlidesLive Video » |
Tom Rainforth 🔗 |
Sat 8:35 a.m. - 8:40 a.m.
|
Q&A
(
(w/ Tom Rainforth)
)
|
🔗 |
Sat 8:45 a.m. - 9:20 a.m.
|
"Active Learning of Robot Reward Functions"
(
Invited Talk
)
SlidesLive Video » |
Dorsa Sadigh 🔗 |
Sat 9:20 a.m. - 9:25 a.m.
|
Q&A
(
(w/ Dorsa Sadigh)
)
|
🔗 |
Sat 9:30 a.m. - 10:05 a.m.
|
"Active Learning through Physically-embodied, Synthesized-from-“scratch” Queries"
(
Invited Talk
)
SlidesLive Video » |
Anca Dragan 🔗 |
Sat 10:05 a.m. - 10:10 a.m.
|
Q&A
(
(w/ Anca Dragan)
)
|
🔗 |
Sat 10:15 a.m. - 11:40 a.m.
|
Lightning Talks and Discussion
|
🔗 |
Sat 11:45 a.m. - 12:25 p.m.
|
"Uncertainty Quantification Using Martingales for Misspecified Gaussian Processes"
(
Invited Talk
)
SlidesLive Video » |
Aaditya Ramdas 🔗 |
Sat 12:25 p.m. - 12:30 p.m.
|
Q&A
(
(w/ Aaditya Ramdas)
)
|
🔗 |
Sat 12:35 p.m. - 1:10 p.m.
|
"Learning to Manage Inventory"
(
Invited Talk
)
SlidesLive Video » |
Shipra Agrawal 🔗 |
Sat 1:10 p.m. - 1:15 p.m.
|
Q&A
(
(w/ Shipra Agrawal)
)
|
🔗 |
Sat 1:20 p.m. - 1:55 p.m.
|
"Machine learning-based design (of proteins, small molecules and beyond)"
(
Invited Talk
)
|
Jennifer Listgarten 🔗 |
Sat 1:55 p.m. - 2:00 p.m.
|
Q&A
(
(w/ Jennifer Listgarten)
)
|
🔗 |
Sat 2:05 p.m. - 2:40 p.m.
|
"Safe and Efficient Active Learning Strategies for Robotics Applications"
(
Invited Talk
)
|
Angela Schoellig 🔗 |
Sat 2:40 p.m. - 2:45 p.m.
|
Q&A
(
(w/ Angela Schoellig)
)
|
🔗 |
Sat 2:50 p.m. - 3:25 p.m.
|
"Towards Causal Benchmarking of Bias in Face Analysis Algorithm"
(
Invited Talk
)
|
Pietro Perona 🔗 |
Sat 3:25 p.m. - 3:30 p.m.
|
Q&A
(
(w/ Pietro Perona)
)
|
🔗 |
Sat 3:30 p.m. - 3:35 p.m.
|
Closing Remarks
(
Discussion Panel
)
|
🔗 |
Author Information
Ilija Bogunovic (ETH Zurich)
Willie Neiswanger (Carnegie Mellon University)
Yisong Yue (Caltech)

Yisong Yue is a Professor of Computing and Mathematical Sciences at Caltech and (via sabbatical) a Principal Scientist at Latitude AI. His research interests span both fundamental and applied pursuits, from novel learning-theoretic frameworks all the way to deep learning deployed in autonomous driving on public roads. His work has been recognized with multiple paper awards and nominations, including in robotics, computer vision, sports analytics, machine learning for health, and information retrieval. At Latitude AI, he is working on machine learning approaches to motion planning for autonomous driving.
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