Stanford RAIN (Research on Algorithms and Incentives in Networks) Seminar
The Internet is a complex network made of both machines and people, and hence, problems in this domain often require techniques from both algorithms and the social sciences. The RAIN (Research on Algorithms and Incentives in Networks) seminar, supported by SOAL, provides a gathering place for talks and social discussion in this area.
- Our talks are on Mondays from 4-5 PM PT in Y2E2 299!
- To receive updates on upcoming talks, please join our email list!
Upcoming Talks
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Bio: Vinay Rao leads Anthropic's Trust and Safety team, which develops and enforces policies governing Claude's real-world applications. His team focuses on mitigating catastrophic risks as well as critical risks such as election interference, child safety, and misinformation. They also play a key role in securing Anthropic's models to comply with the company's Responsible Scaling Policy for safe and ethical AI development and deployment. With nearly two decades of experience in trust and safety, Vinay has built and managed leading Trust and Safety functions at major tech companies, including YouTube, Google, Airbnb, and Stripe.
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Previous Talks This Year
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Although current large language models are complex, the most basic specifications of the underlying language generation problem itself are simple to state: given a finite set of training samples from an unknown language, produce valid new strings from the language that don't already appear in the training data. Here we ask what we can conclude about language generation using only this specification, without further properties or distributional assumptions. In particular, we consider models in which an adversary enumerates the strings of an unknown target language that is known only to come from one of a possibly infinite list of candidates, and the goal is to generate new strings from this target language; we show that it is possible to give certain non-trivial guarantees for language generation in this setting. The resulting guarantees contrast dramatically with negative results due to Gold and Angluin in a well-studied model of language learning where the goal is to identify an unknown language from samples; the difference between these results suggests that identifying a language is a fundamentally different problem than generating from it. (This is joint work with Sendhil Mullainathan.)Bio: Jon Kleinberg is the Tisch University Professor in the Departments of Computer Science and Information Science at Cornell University. His research focuses on the interaction of algorithms and networks, the roles they play in large-scale social and information systems, and their broader societal implications. He is a member of the National Academy of Sciences, the National Academy of Engineering, the American Academy of Arts and Sciences, and the American Philosophical Society, and he serves on the US National AI Advisory Committee. He has received MacArthur, Packard, Simons, Sloan, and Vannevar Bush research fellowships, as well as awards including the Harvey Prize, the Lanchester Prize, the Nevanlinna Prize, the World Laureates Association Prize, and the ACM Prize in Computing.
Previous talks can be found here.
About The Seminar
Seminar Organizers: Itai Ashlagi, Mohak Goyal, Tristan Pollner.
Faculty Involved: Itai Ashlagi, Ashish Goel, Ramesh Johari, Amin Saberi, Aaron Sidford, Johan Ugander, Irene Lo, Ellen Vitercik.
Note for Speakers: The talk is 55 minutes including questions (as we often start a couple of minutes late). If you are giving a talk at RAIN, please plan a 45-50 minute talk since the audience usually ask a lot of questions. Also, the audience is fairly knowledgeable, so speakers should not feel obligated to provide basic game-theoretic, algorithmic, societal, industrial, probabilistic, or statistical background.
Website template from the Stanford MLSys Seminar Series.