Symbolic behavior in AI: some possible lessons for whales? @SimonsInstituteTOC

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Andrew Lampinen (Google DeepMind)
https://simons.berkeley.edu/talks/andrew-lampinen-google-deepmind-2024-06-29
Decoding Communication in Nonhuman Species III (co-hosted with Project CETI)

Physical problem-solving in minds and machines

New models of games with imperfect information

Ramsey Quantifiers in First-Order Logic: Complexity and Applications to Verification

$Towards Practical Distribution Testing Yash Pote (National University of Singapore) https://simons.berkeley.edu/talks/yash-pote-national-university-singapore-2024-08-07 Workshop on Local Algorithms (WoLA) As systems that employ samplers are deployed in safety-critical software, there is a need for tests that can verify the samplers statistical correctness. This raises the question: given a sampler P and a target distribution Q, can we practically test whether P samples from a distribution close to Q? In the high-dimensional setting, where the domain is {0,1}^N for a large N, black-box testing (sample access) is well-known to be intractable; hence, richer grey-box models, such as conditional sampling, have emerged as promising alternatives for the testing problem. In this talk, I will present our work in developing grey-box algorithms that are fast in theory and practice, and I will focus on the first polynomial query algorithm for TV distance estimation, in the conditional sampling model.$

Through a glass, darkly: Approximations, hacks, and workarounds in intuitive physics and imagination

Stochastic games with neural perception mechanisms

Symbolic Finite- and Infinite-state Synthesis

Robot learning, with inspiration from child development

Synthesizing distributed protocols from global session types

An overview of classical robust statistics and generalizations to the future