NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in the Real World
Willie Neiswanger
Dear all,
We would like to invite you to submit your work to the Neurips 2023 Workshop on Adaptive Experimental Design and Active Learning in the Real World, which will be held in person on December 16, 2023 in New Orleans, USA.
*Important Information*
-------------------------------
Workshop date: December 16, 2023
Location: In person, New Orleans, USA.
Submission deadline: 4th October 2023, 11:59 PM (AoE time)
Contact: realml-worksh...@googlegroups.com
Best Student Paper Award: A best student paper award, worth 1000 USD, will be awarded to the best paper selected by a reviewing committee.
*Keynote Speakers*
---------------------------
Mihaela van der Schaar (Cambridge University)
Eytan Bakshy (Meta Platforms, Inc.)
Anna Goldie (Stanford University and Anthropic)
Joel Paulson (The Ohio State University)
Emma Brunskill (Stanford University)
Nathan Kallus (Cornell University and Netflix)
Erika DeBenedictis (Francis Crick Institute)
*Call for Papers*
----------------------
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 experimental design and active learning problems. We aim to highlight new and emerging research opportunities for the ML community that arise from the evolving needs to make experimental design and active learning procedures that are theoretically and practically relevant for realistic applications. Progress in this area has the potential to provide immense benefits in using experimental design and active learning algorithms in emerging high impact applications, such as materials design, drug design, causal discovery, AutoML, crowdsourcing, citizen science, robotics, and more.
We welcome submissions of 4-6 pages (excluding references) using the LaTeX template linked here. All accepted papers will be presented as posters (recently published or under-review work is also welcome). There will be no archival proceedings, however, the accepted papers will be made available online on the workshop website. Papers should be submitted via OpenReview: https://openreview.net/group?id=NeurIPS.cc/2023/Workshop/ReALML
*Topics of Interest*
-------------------------
Technical topics of interest include (but are not limited to):
Large-scale and real-world experimental design (e.g. drug design, physics, robotics, material design, protein design, causal discovery).
Efficient active learning and exploration.
Experimental design and active learning in reinforcement learning.
High-dimensional, scalable Bayesian and bandit optimization (e.g. contextual, multi-task, multi-objective).
Sample-efficient interactive learning, hypothesis and A/B testing.
Corrupted or indirect measurements, multi-fidelity experimentation.
Domain-knowledge integration (e.g. from physics, chemistry, biology, etc.).
Effective exploration in high-dimensional spaces (e.g. using deep learning).
Safety and robustness during experimentation and of resulting designs.
*Organization*
-------------------
Ava Amini (Microsoft Research)
Ilija Bogunovic (UCL)
Stefano Ermon (Stanford)
Lalit Jain (Univ. of Washington)
Andreas Krause (ETH Zurich)
Mojmír Mutný (ETH Zurich)
Willie Neiswanger (Stanford)
Zi Wang (Google DeepMind)