Upcoming Seminars

CITP Distinguished Lecture Series: Lorrie Cranor – Designing Usable and Useful Privacy Choice Interfaces
Thu, Mar 30, 2023, 4:30 pm

Co-sponsored by the Department of Computer Science and the Department of Electrical and Computer Engineering

Users who wish to exercise privacy rights or make privacy choices must often rely on website or app user interfaces. However, too often, these user interfaces suffer from usability deficiencies ranging from being…


Previous Seminars

Large Language Models: Will they keep getting bigger? And, how will we use them if they do?
Thu, Nov 17, 2022, 12:30 pm

The trend of building ever larger language models has dominated much research in NLP over the last few years. In this talk, I will discuss our recent efforts to (at least partially) answer two key questions in this area: Will we be able to keep scaling? And, how will we actually use the models, if we do?

Explainable AI for Climate Science: Detection, Prediction and Discovery
Thu, Nov 10, 2022, 12:30 pm

Earth’s climate is chaotic and noisy. Finding usable signals amidst all of the noise can be challenging. Here, I will demonstrate how explainable artificial intelligence (XAI) techniques can sift through vast amounts of climate data and push the bounds of scientific discovery. Examples include extracting robust indicator patterns of climate…

Machine Learning advancements for design of water and energy policies in a changing climate and society
Tue, Nov 1, 2022, 4:30 pm

In this talk, we provide an overview of recent advances in data-driven modeling and control of human-water-energy systems and showcase how Machine Learning techniques can help (i) infer natural and anthropogenic drivers of observed hydroclimatic patterns and improve their predictability in space and time, (ii) understand and conceptualize the mutual influences between human behaviors and water-energy systems; and (iii) design strategic planning and management policies optimizing multiple and conflicting objectives with different dynamics and informed by heterogeneous information. 


Learning Space-Group Invariant Functions
Mon, Oct 31, 2022, 4:30 pm

The plane and space groups are groups that specify how to tile two- or three-dimensional Euclidean space with a shape: They enumerate all possible ways in which a shape can be isometrically replicated across the space. I will describe how to explicitly compute approximate eigenfunctions of the Laplace-Beltrami operator on the orbifold defined by any such group.


Using Transportability Estimators to Understand Reasons For Differences In Intervention Effects Across Sites
Tue, Oct 25, 2022, 12:00 pm

Multi-site studies are common, and intervention effect estimates frequently differ across sites. We discuss reasons why effect estimates may differ across sites and relate these to transportability. In scenarios where transport is possible, we develop transport estimators to better understand why intervention effects differ across sites/population.


A Language-Based Model of Organizational Identification Demonstrates How Within-Person Changes in Identification Relate to Network Position
Mon, Oct 10, 2022, 12:00 pm

Shifting attachments to social groups are a constant in the modern era.They are especially pronounced in the contemporary workplace. What accounts for variation in the strength of organizational identification?


Completing large low rank matrices with only few observed entries: A one-line algorithm with provable guarantees
Mon, Sep 19, 2022, 4:30 pm

Suppose you observe very few entries from a large matrix. Can we predict the missing entries, say assuming the matrix is (approximately) low rank ? We describe a very simple method to solve this matrix completion problem. We show our method is able to recover matrices from very few entries and/or with ill conditioned matrices, where many other popular methods fail. 


New Results on Universal Dynamic Regret Minimization for Learning and Control
Mon, Sep 12, 2022, 4:30 pm

Universal dynamic regret is a natural metric for the performance of an online learner in nonstationary environments.  The optimal dynamic regret for strongly convex and exponential concave losses, however, had been open for nearly two decades. In this talk, I will cover some recent advances on this problem from my group that largely settled this open problem. 


The Role of Relative Entropy in Supervised Machine Learning
Thu, Jul 28, 2022, 4:30 pm

In this talk, recent results on various aspects of the Empirical Risk Minimization (ERM) problem with Relative Entropy Regularization (ERM-RER) are presented. The regularization is with respect to a sigma-finite measure, instead of a probability measure, which provides a larger flexibility for including prior knowledge on the models. Special cases of this general formulation include the ERM problem with (discrete or differential) entropy regularization and the information-risk minimization problem.


Seminars on security and privacy in machine learning: Alexandre Sablayrolles (Meta-Facebook AI)
Tue, Jul 5, 2022, 1:00 pm

The motivation for the seminar is to build a platform to discuss and disseminate the progress made by the community in solving some of the core challenges. We intend to host weekly talks from leading researchers in both academia and industry. Each session will be split into a talk (40 mins) followed by a Q&A + short discussion session (20 mins).