February 23, 2021 to February 24, 2021
Speaker: Kamalika Chaudhuri (University of California San Diego)
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...
March 9, 2021
Speaker: Daniel Spielman (Yale)
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
Speaker: David Simchi-Levi (MIT)
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...
April 13, 2021
Speaker: 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...
April 20, 2021
Speaker: John Langford (Microsoft)
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
Speaker: 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...
May 5, 2021
Speaker: 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)...
