Algorithms and Complexity Seminars
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Understanding the Trade-Offs Between Hallucinations and Mode Collapse in Language Generation Thursday, May 1, 2025 - 4:00pm to 5:00pm Specifying all desirable properties of a language model is challenging, but certain requirements seem essential. |
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How to Appease a Voter Majority Wednesday, April 30, 2025 - 4:00pm to 5:00pm In 1785, Condorcet established a frustrating property of elections and majority rule: it is possible that, no matter which candidate you pick as the winner, a majority of voters will prefer someone else. |
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Revisiting the Predictability of Social Events Wednesday, April 23, 2025 - 4:00pm to 5:00pm Social predictions do not passively describe the future; they actively shape it. They inform actions and change individual expectations in ways that influence the likelihood of the predicted outcome. Given these dynamics, to what extent can social events be predicted? |
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DDPM Score Matching and Distribution Learning Wednesday, April 16, 2025 - 4:00pm to 5:00pm Score estimation is the backbone of score-based generative models (SGMs), especially denoising diffusion probabilistic models (DDPMs). |
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Learning from Missing and Imperfect Data Wednesday, April 9, 2025 - 4:00pm to 5:00pm Positive-Unlabeled Learning (PU Learning) is a framework for learning when only positive and unlabeled data are available, which is a common scenario in Bioinformatics, Medicine, and Fraud Detection, where obtaining negative samples is challenging or costly. |
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Constructive Criticisms of Complexity: Unifying Proofs and Algorithms Wednesday, March 19, 2025 - 4:00pm to 5:00pm For decades, fundamental questions in complexity theory have remained wide open. |
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Extractors for Samplable Distributions with Low Min-Entropy Wednesday, March 12, 2025 - 4:00pm to 5:00pm Trevisan and Vadhan (FOCS 2000) introduced the notion of (seedless) extractors for samplable distributions. |
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Correlation Clustering and (De)Sparsification: Graph Sketches Can Match Classical Algorithms Wednesday, March 5, 2025 - 4:00pm to 5:00pm Correlation clustering is a widely-used approach for clustering large data sets based only on pairwise similarity information. In recent years, there has been a steady stream of better and better classical algorithms for approximating this problem. |
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Richard Peng: Solving SDD Linear Systems in Nearly mlog1/2n Time Friday, September 12, 2014 - 4:00pm to 5:00pm |
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Carol Wang: Explicit List-Decodable Subspace Codes with High Rate Wednesday, April 9, 2014 - 4:00pm to 5:00pm |
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