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DTSTART;TZID=America/Los_Angeles:20260120T134000
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SUMMARY:Behavioral\, Econometrics and Theory Seminar Series Presents: Roberto Corrao
DESCRIPTION:Economics Behavioral\, Econometrics\, & Theory Seminar\nDate: Tuesday\, January 20\, 2026\nTime: 1:40-3:00 p.m.\nLocation: E2-499\n\n \n\nSpeaker: Roberto Corrao\nTitle:  Assistant Professor of Economics \nAffiliation:  Stanford University\nHost: Gerelt Tserenjigmid\n \nSeminar title: Contractibility Design\n \nABSTRACT: \nWe introduce a model of incentive contracting in which the principal\, in addition to\nwriting contracts\, must engage in contractibility design: creating an evidence structure\nthat allows them to prove when the agent has breached the contract. Designing an\nevidence structure entails both (i) front-end costs borne ex ante\, such as those of\ndrafting contracts\, and (ii) back-end costs borne ex post\, such as those of generating\nevidence. We find that\, under even small front-end costs\, optimal contracts are coarse\,\nspecifying finitely many contingencies out of a continuum of possibilities. In contrast\,\nunder even large back-end costs\, optimal contracts are complete. Applied to the design\nof procurement contracts\, our results rationalize: (i) the discreteness of contracts\, (ii)\nthe presence of similarly vague contracts in low-stakes and high-stakes settings\, and\n(iii) the discontinuous adjustment of contracts to changes in the economic environment.
URL:https://events.ucsc.edu/event/behavioral-econometrics-and-theory-seminar-series-presents-roberto-corrao/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
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DTSTART;TZID=America/Los_Angeles:20260121T110000
DTEND;TZID=America/Los_Angeles:20260121T123000
DTSTAMP:20260420T091059
CREATED:20260105T203936Z
LAST-MODIFIED:20260105T205329Z
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SUMMARY:CSE Colloquium - Constraining Chaos: Toward Faithful and Semantic Decoding in Language Models
DESCRIPTION:Presenter: Loris D’Antoni\, UC San Diego \nAbstract:\nLanguage models excel at producing fluent text\, but in domains like code and math\, fluency isn’t enough — outputs must obey strict syntactic and semantic rules. A new wave of research is rethinking decoding itself: not as a process of sampling words\, but as a negotiation between probability\, structure\, and meaning. In this talk\, I’ll explore how grammar and semantics can be embedded into the decoding loop\, how we can sample from the true model conditional distribution under constraints\, and how programmable abstractions make it possible to enforce properties like type safety or program invariants. The result is a vision of decoding that is faithful to the model yet governed by rules\, pointing toward a future where LLMs generate not just plausible text\, but reliably correct output. \nBio:\nLoris D’Antoni is a Jacobs Faculty Scholar and Associate Professor in the Department of Computer Science and Engineering at the University of California San Diego. His research helps people build trustworthy software. His work has introduced new frameworks for verifying and synthesizing programs—ranging from resilient network configurations to robust decision-making systems—and\, more recently\, methods for aligning language models with user intent. \nHe is the recipient of an NSF CAREER Award and a Microsoft Research Faculty Fellowship\, and was selected as a Vilas Associate at the University of Wisconsin-Madison. He has also received Google\, Amazon\, and Meta Faculty Awards\, and the Morris and Dorothy Rubinoff Dissertation Award. His papers have earned several best paper awards and nominations\, including at TACAS\, ESOP\, ICDCN\, and SBES. \nLoris received his B.S. and M.S. in Computer Science from the University of Torino\, and his Ph.D. in Computer Science from the University of Pennsylvania. Before joining UC San Diego\, he was a faculty member at the University of Wisconsin–Madison. \nHosted by: Professor Nikos Tziavelis \nLocation: Engineering 2\, Room E2-180 \n*Light refreshments such as coffee\, pastries\, and fruit will be available. \nZoom: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-constraining-chaos-toward-faithful-and-semantic-decoding-in-language-models/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260122T014000
DTEND;TZID=America/Los_Angeles:20260122T014000
DTSTAMP:20260420T091059
CREATED:20251211T230012Z
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SUMMARY:Applied Microeconomics and Trade Seminar Series Presents: Guo Xu
DESCRIPTION:Applied Microeconomics and Trade Seminar\nDate: Thursday\, January 22\, 2026\nTime: 1:40 – 3:00 p.m.\nLocation: E2-499\n\n \n\nSpeaker: Guo Xu\nTitle: Associate Professor of Economics \nAffiliation: University of California\, Berkeley  \nHost: Ajay Shenoy \n  \nSeminar title: Personnel is Policy: Delegation and Political Misalignment in the Rulemaking Process\n\nABSTRACT: We combine comprehensive data on the U.S. federal rulemaking process with individuallevel personnel and voter registration records to study the consequences of partisan misalignment between regulators and the president. We present three main results. First\, even important pieces of new regulation are frequently delegated to bureaucrats who are politically misaligned. Second\, rules that are overseen by misaligned regulators take systematically longer to complete\, are more verbose\, generate more negative feedback from the public\, and are more likely to be challenged in court. Third\, in assigning regulators to rules\, agency leaders often face a sharp tradeoff between political alignment and expertise. Agency frictions notwithstanding\, they tend to resolve this tradeoff in favor of expertise.
URL:https://events.ucsc.edu/event/applied-microeconomics-and-trade-seminar-series-presents-guo-xu/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
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DTSTART;TZID=America/Los_Angeles:20260122T114000
DTEND;TZID=America/Los_Angeles:20260122T131500
DTSTAMP:20260420T091059
CREATED:20260115T232014Z
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SUMMARY:BME Seminar: Rotation Talks
DESCRIPTION:Presenter: Grad Students \nDescription: Rotation Talks \nBio: N/A \nHosted by: Professor Rebecca DuBois\, BME Department
URL:https://events.ucsc.edu/event/bme-seminar-rotation-talks/
LOCATION:Physical Sciences Building\, Physical Sciences Building\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260123T120000
DTEND;TZID=America/Los_Angeles:20260123T130000
DTSTAMP:20260420T091059
CREATED:20260120T214846Z
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SUMMARY:Statistics Seminar: Heterogeneous Statistical Transfer Learning
DESCRIPTION:Presenter: Subhadeep Paul\, Associate Professor\, Ohio State University \nDescription: In the first part of the talk\, we consider the problem of Transfer Learning (TL) under heterogeneity from a source to a new target domain for high-dimensional regression with differing feature sets. Most homogeneous TL methods assume that target and source domains share the same feature space\, which limits their practical applicability. In applications\, the target and source features are frequently different due to the inability to measure certain variables in data-poor target environments. Conversely\, existing heterogeneous TL methods do not provide statistical error guarantees\, limiting their utility for scientific discovery.  Our method first learns a feature map between the missing and observed features\, leveraging the vast source data\, and then imputes the missing features in the target. Using the combined matched and imputed features\, we then perform a two-step transfer learning for penalized regression. We develop upper bounds on estimation and prediction errors\, assuming that the source and target parameters differ sparsely but without assuming sparsity in the target model. We obtain results for both when the feature map is linear and when it is nonparametrically specified as unknown functions.  Our results elucidate how estimation and prediction errors of HTL depend on the model’s complexity\, sample size\, the quality and differences in feature maps\, and differences in the models across domains. In the second part of the talk\, going beyond linear models\, I will discuss a transfer learning method for nonparametric regression using a random forest. The unknown source and target regression functions are assumed to differ for a small number of features. Our method obtains residuals from a source domain-trained Centered RF (CRF) in the target domain\, then fits another CRF to these residuals with feature splitting probabilities proportional to feature-residual distance covariance. We derive an upper bound on the mean square error rate of the procedure that theoretically brings out the benefits of transfer learning in random forests. Our results explain why shallower trees in the residual random forest in the target domain provide implicit regularization. \nBio:Subhadeep Paul is an Associate Professor in the Department of Statistics at The Ohio State University. He is also a faculty fellow and previously served as a co-director of the foundations of data science and AI community at the Translational Data Analytics Institute at Ohio State. He received his PhD in Statistics from the University of Illinois at Urbana-Champaign in 2017. His research focuses on statistical analysis of complex network-linked data and transfer and federated statistical learning. His research has been funded by two NSF grants from the algorithms of threat detection and mathematics of digital twins programs. \nHosted by: Statistics Department \nZoom link: https://ucsc.zoom.us/j/94465292273?pwd=bQ6MCX0OHYxHqgqNwbEYfgbKWqgNVy.1
URL:https://events.ucsc.edu/event/statistics-seminar-heterogeneous-statistical-transfer-learning/
LOCATION:https://ucsc.zoom.us/j/94465292273?pwd=bQ6MCX0OHYxHqgqNwbEYfgbKWqgNVy.1
CATEGORIES:Lectures & Presentations,Seminars
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