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DTSTART;TZID=America/New_York:20250502T120000
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DTSTAMP:20260518T121625
CREATED:20241211T195435Z
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SUMMARY:Incentives for data sharing in federated learning
DESCRIPTION:Member Seminar \nSpeaker: Han Shao\, Harvard CMSA \nTitle: Incentives for data sharing in federated learning \nAbstract: Federated learning has recently emerged as a powerful approach for enabling collaboration across large populations of learning agents. However\, agents may have incentives to defect from the collaboration—that is\, to withdraw or contribute less data than expected—due to the costs of data curation and privacy concerns. This raises several key questions: What happens when agents defect\, and how can we prevent such defections? \n 
URL:https://cmsa.fas.harvard.edu/event/member-seminar-5225/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Member Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Member-Seminar-5.2.25.png
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DTSTART;TZID=America/New_York:20250509T120000
DTEND;TZID=America/New_York:20250509T130000
DTSTAMP:20260518T121625
CREATED:20241211T195446Z
LAST-MODIFIED:20250506T153832Z
UID:10003649-1746792000-1746795600@cmsa.fas.harvard.edu
SUMMARY:Asset pricing with heterogeneous agents
DESCRIPTION:Member Seminar \nSpeaker: Sergiy Verstyuk\, Harvard CMSA \nTitle: Asset pricing with heterogeneous agents \nAbstract: This talk will introduce the basics of continuous-time finance\, discuss important existing theories and models\, as well as present some new asset pricing results in a setting with many heterogeneous investors. (Joint work with Puskar Mondal.) \n 
URL:https://cmsa.fas.harvard.edu/event/member-seminar-5925/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Member Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Member-Seminar-5.9.25.png
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DTSTART;TZID=America/New_York:20250516T120000
DTEND;TZID=America/New_York:20250516T130000
DTSTAMP:20260518T121625
CREATED:20250218T161047Z
LAST-MODIFIED:20250513T152517Z
UID:10003714-1747396800-1747400400@cmsa.fas.harvard.edu
SUMMARY:Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining
DESCRIPTION:Member Seminar \nSpeaker: Samy Jelassi\, CMSA \nTitle: Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining \nAbstract: Reinforcement Learning has become a crucial step in training state-of-the-art language models such as DeepSeek-R1 for solving mathematical problems. In this talk\, I will first review the mechanisms of Reinforcement Learning fine-tuning. Then\, I will present a systematic end-to-end study of RL fine-tuning for mathematical reasoning\, training models entirely from scratch on different mixtures of fully open datasets and fine-tuning them with RL. Doing so allows us to investigate the effects of the pretraining data mixture on the behavior of RL\, and its interaction with the model size and choices of the algorithm hyperparameters. Our study reveals that RL algorithms consistently converge towards a dominant output distribution\, amplifying patterns in the pretraining data. We also find that models of different scales trained on the same data mixture will converge to distinct output distributions\, suggesting that there are scale-dependent biases in model generalization. \nThe second part of the talk is based on a joint work with Rosie Zhao\, Alex Meterez\, Cengiz Pehlevan\, Sham Kakade and Eran Malach: https://arxiv.org/abs/2504.07912 \n 
URL:https://cmsa.fas.harvard.edu/event/member-seminar-51625/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Member Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Member-Seminar-5.16.25.png
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