LIDS Seminar Series

The LIDS seminar series serves as a focal point in the intellectual life of the lab. Seminar videos, when available, can be found on the LIDS YouTube channel.

February 23, 2021 to February 24, 2021

Challenges in Reliable Machine Learning

Kamalika Chaudhuri (University of California San Diego)

Event Recording:

As machine learning is increasingly deployed, there is a need for reliable and robust methods that go beyond simple test accuracy.  In this talk, we will discuss two challenges that arise in reliable machine learning. The first is robustness to...


March 9, 2021

Balancing Covariates In Randomized Experiments Using The Gram–Schmidt Walk

Daniel Spielman (Yale)

Event Recording:

Randomized Controlled Trials (RCTs) are the principal way we estimate the effectiveness of new medications, procedures, policies, and interventions. In a typical medical trial, the subjects are divided into two (or more) groups. One group of...


March 16, 2021

Statistical Learning in Operations: The Interplay Between Online and Offline Learning

David Simchi-Levi (MIT)

Event Recording:

Traditionally, statistical learning is focused on either (i) online learning where data is generated online according to some unknown model; or (ii) offline learning where the entire data is available at the beginning of the process. In this talk,...


April 13, 2021

Variational Formulations and Distributed Convex Optimization Methods for Modern Data Science Applications

Todd Coleman (University of California San Diego)

The need to reason about uncertainty in large, complex, and multi-modal datasets has become increasingly common. The ability to transform samples from one distribution P to another distribution Q enables the solution to many problems in data science...


April 20, 2021

Achieving the Reinforcement Learning Trifecta

John Langford (Microsoft)

Event Recording:

There are 3 key problems at the heart of reinforcement learning: How do you generalize to new unseen observations? How do you assign value to actions taken? And how do you explore so as to gather the information necessary to do the first two? ...


April 27, 2021

Feedback Control Perspectives on Learning

Jeff Shamma (University of Illinois at Urbana-Champaign)

The impact of feedback control is extensive. It is deployed in a wide array of engineering domains, including aerospace, robotics, automotive, communications, manufacturing, and energy applications, with super-human performance having been achieved...


May 5, 2021

Consensus-Based Optimization

Massimo Fornasier (Technical University of Munich)

Consensus-based optimization (CBO) is a multi-agent metaheuristic derivative-free optimization method that can globally minimize nonconvex nonsmooth functions and is amenable to theoretical analysis. In fact, optimizing agents (particles) move on...