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bair.berkeley.edu Online Courses - March 2024

Designs From Data: Offline BlackBox Bair.berkeley.edu

As shown in Kumar et al. 2021 (Tables 3, 4), this COMs-inspired approach finds better designs than various prior state-of-the-art online MBO methods that access the simulator via time-consuming simulation. While, in principle, one can always design an online method that should perform better than any offline MBO method (for example, by wrapping

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Unsolved ML Safety Problems – The Berkeley Artificial

Dan Hendrycks Sep 29, 2021. Along with researchers from Google Brain and OpenAI, we are releasing a paper on Unsolved Problems in ML Safety . Due to emerging safety challenges in ML, such as those introduced by recent large-scale models, we provide a new roadmap for ML Safety and refine the technical problems that the field needs to address.

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Archive – The Berkeley Artificial Intelligence Research Blog

Network Generalization. 22 Oct 2021 » Making RL Tractable by Learning More Informative Reward Functions: Example-Based Control, Meta-Learning, and Normalized Maximum Likelihood. 14 Oct 2021 » Updates and Lessons from AI Forecasting. 06 Oct 2021 » PICO: Pragmatic Compression for Human-in-the-Loop Decision-Making.

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Berkeley Artificial Intelligence Research Lab

BAIR Research Experience for Undergraduates (REU) The BAIR REU program has begun accepting applications! Apply via the NSF ETAP portal under SUPERB; if you are eligible according to the eligibility criteria below, you will automatically be considered for the BAIR REU.. The deadline for applications is January 31st, 2022, at 5 PM PST, though we may make rolling ...

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