ICML 2022

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Workshop

Adaptive Experimental Design and Active Learning in the Real World

Mojmir Mutny · Willie Neiswanger · Ilija Bogunovic · Stefano Ermon · Yisong Yue · Andreas Krause

Fri Jul 22 05:40 AM -- 04:30 PM (PDT) @ Room 309

Event URL: https://realworldml.github.io/ »

Whether in robotics, protein design, or physical sciences, one often faces decisions regarding which data to collect or which experiments to perform. There is thus a pressing need for algorithms and sampling strategies that make intelligent decisions about data collection processes that allow for data-efficient learning. Experimental design and active learning have been major research focuses within machine learning and statistics, aiming to answer both theoretical and algorithmic aspects of efficient data collection schemes. The goal of this workshop is to identify missing links that hinder the direct application of these principled research ideas into practically relevant solutions.

Fri 5:40 a.m. - 6:10 a.m.	Doors Open and Workshop/Poster Setup (Break) Doors open, attendees can put up posters, and workshop organizers help set up room.	🔗
Fri 6:10 a.m. - 6:20 a.m.	Opening Remarks (Presentation) SlidesLive Video » Opening remarks from the workshop organizers.	Willie Neiswanger · Mojmir Mutny · Ilija Bogunovic 🔗
Fri 6:20 a.m. - 7:00 a.m.	Jeff Schneider (Invited Talk) SlidesLive Video » Invited talk from Jeff Schneider.	Jeff Schneider 🔗
Fri 7:00 a.m. - 7:40 a.m.	Ava Soleimany (Invited Talk) SlidesLive Video » Invited talk from Ava Soleimany.	Ava Amini 🔗
Fri 7:40 a.m. - 8:00 a.m.	Coffee Break (Break) Coffee break.	🔗
Fri 8:00 a.m. - 8:40 a.m.	(Update) Poster Setup/Discussion and Extended Coffee Break (Poster Session + Break) (Update) Poster Setup/Discussion and Extended Coffee Break	🔗
Fri 8:40 a.m. - 9:10 a.m.	Poster Spotlight Talks (Invited Talks) SlidesLive Video » Spotlight talks from a subset of accepted papers.	🔗
Fri 9:10 a.m. - 10:40 a.m.	Lunch (Break) Lunch break.	🔗
Fri 10:40 a.m. - 11:20 a.m.	Ruth Misener (Invited Talk) SlidesLive Video » Invited talk from Ruth Misener. Title: Autonomous research machines: Self-optimizing new chemistry Abstract: Our research seeks to boost R&D efficiency in the chemicals industry. As an example, consider "micro reactor flow systems", which are transforming chemical manufacturing by enabling flexible prototyping. Because these high-throughput microfluidic devices can control reaction conditions online, they are ideal for quantitatively characterizing diverse chemical synthesis techniques along new reaction pathways. The challenge is: How do we automate the design of experiments to "self-optimise" new chemistry? Together with the BASF Data Science for Materials & Chemistry teams, we’re interested to solve Bayesian optimization challenges which may simultaneously exhibit: multiple objectives, mixed-feature spaces, asynchronous decisions, large batch sizes, input constraints, multi-fidelity observations, hierarchical choices, and costs associated with switching between experimental points. We review the machine learning contributions that we’ve found useful towards achieving these goals and discuss our own methodological and software contributions. This work is a collaboration between Imperial (Jose Pablo Folch, Alexander Thebelt, Shiqiang Zhang, Jan Kronqvist, Calvin Tsay, Ruth Misener) and BASF (Robert Lee, Behrang Shafei, Nathan Sudermann-Merx, David Walz).	Ruth Misener 🔗
Fri 11:20 a.m. - 11:50 a.m.	Coffee Break (Break) Coffee Break	🔗
Fri 11:50 a.m. - 12:30 p.m.	Peter Frazier (Invited Break) SlidesLive Video » Invited talk from Peter Frazier. Title: Bayesian Optimization in Biochemistry Abstract: Excitement is growing in the chemical sciences about Bayesian optimization and other methods for AI-based adaptive experimentation for accelerating the design of drugs, materials, and chemical formulations. Query-efficient methods like BayesOpt seem well-suited to these problems because measuring chemical and biological properties is often time-consuming and/or expensive, because they can incorporate scientific expertise into Bayesian prior distributions, and because the automation they provide allows getting the full benefits of robotics and high-throughput experimentation. At the same time, barriers remain: designing a drug, material or consumer product is a multi-stage process involving many goals and evaluation methods, not a single optimization problem; poorly performing recommended molecules can destroy trust; training data often lacks negative examples; humans are hard to beat; experimentalists can have unrealistic expectations; and disciplinary differences in approach, expertise, and language can hinder collaboration. The talk will focus on the speaker's experience from 3 biochemical design problems in which experimentalists performed wet lab experiments based on algorithmic recommendations from a BayesOpt or other AI algorithm. Drawing on these experiences, we suggest approaches for overcoming these challenges, including grey-box BayesOpt methods, methods for BayesOpt with preference learning, and human-focused approaches supporting effective collaboration with chemists.	Peter I Frazier 🔗
Fri 12:30 p.m. - 1:10 p.m.	Christopher Langmead (Invited Talk) SlidesLive Video » Invited talk from Christopher Langmead. Title: Active Learning for the Design of Therapeutic Proteins Abstract: I will discuss ongoing work at Amgen to use Active Learning to train predictive models relevant to the design of therapeutic proteins.	🔗
Fri 1:10 p.m. - 1:50 p.m.	Caroline Uhler (Invited Talk) SlidesLive Video » Invited talk from Caroline Uhler. Title: Optimal Design of Interventions for Causal Discovery in Genomics	Caroline Uhler 🔗
Fri 1:50 p.m. - 2:00 p.m.	Best Student Paper Award (Presentation) SlidesLive Video » Best student paper award announcement.	🔗
Fri 2:00 p.m. - 2:05 p.m.	Closing Remarks and Poster Session Kickoff (Presentation) SlidesLive Video » Closing remarks and poster session kickoff.	🔗
Fri 2:05 p.m. - 4:30 p.m.	Poster Session In-person poster session.	🔗

Author Information

Mojmir Mutny (ETH Zurich)

Willie Neiswanger (Stanford University)

Ilija Bogunovic (University College London (UCL))

Stefano Ermon (Stanford 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.

Andreas Krause (ETH Zurich)

Andreas Krause is a Professor of Computer Science at ETH Zurich, where he leads the Learning & Adaptive Systems Group. He also serves as Academic Co-Director of the Swiss Data Science Center and Chair of the ETH AI Center, and co-founded the ETH spin-off LatticeFlow. Before that he was an Assistant Professor of Computer Science at Caltech. He received his Ph.D. in Computer Science from Carnegie Mellon University (2008) and his Diplom in Computer Science and Mathematics from the Technical University of Munich, Germany (2004). He is a Max Planck Fellow at the Max Planck Institute for Intelligent Systems, an ELLIS Fellow, a Microsoft Research Faculty Fellow and a Kavli Frontiers Fellow of the US National Academy of Sciences. He received the Rössler Prize, ERC Starting Investigator and ERC Consolidator grants, the German Pattern Recognition Award, an NSF CAREER award as well as the ETH Golden Owl teaching award. His research has received awards at several premier conferences and journals, including the ACM SIGKDD Test of Time award 2019 and the ICML Test of Time award 2020. Andreas Krause served as Program Co-Chair for ICML 2018, and currently serves as General Chair for ICML 2023 and as Action Editor for the Journal of Machine Learning Research.