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.

September 28, 2020

Distributed Machine Learning over Networks

Francis Bach (Institut National de Recherche en Sciences et Technologies du Numérique (INRIA))

The success of machine learning models is in part due to their capacity to train on large amounts of data. Distributed systems are the common way to process more data than one computer can store, but they can also be used to increase the pace at...

October 19, 2020

SOAP: New Breakthroughs in Scheduling Theory

Mor Harchol-Balter (Carnegie Mellon University)

Scheduling policies are at the heart of computer systems. The right scheduling policy can dramatically reduce response times, ensure fairness, provide class-based priority, etc., without requiring additional resources. While stochastic response time...

October 26, 2020

Hadamard Differential Calculus and Applications

Michel Delfour (Université de Montréal)

The Hadamard differential was introduced in 1923 by Hadamard and promoted in 1937 by Fréchet who extended it to vector spaces of functions. Infinite dimension is equivalent to the Fréchet differential introduced in 1911, but in function spaces,...

November 9, 2020

Thresholds for Reliable Computation with Noisy Gates, and Applications in Quantum Nonlocality

Mary Wootters (Stanford)

Suppose you are given a bunch of logic gates -- ANDs, XORs, and NOTs -- but they are noisy, and with some probability will return the incorrect answer.  At what noise levels is reliable computation possible using these gates?  We investigate this...

November 16, 2020

*CANCELED* LIDS Seminar - Kamalika Chaudhuri (University of California San Diego)

Kamalika Chaudhuri (University of California San Diego)

November 23, 2020

Safe and Efficient Exploration in Reinforcement Learning

Andreas Krause (ETH Zürich)

At the heart of Reinforcement Learning lies the challenge of trading exploration -- collecting data for identifying better models -- and exploitation -- using the estimate to make decisions.  In simulated environments (e.g., games), exploration is...

November 30, 2020

Long Duration Autonomy With Applications to Persistent Environmental Monitoring

Magnus Egerstedt (Georgia Institute of Technology)

When robots are to be deployed over long time scales, optimality should take a backseat to “survivability”, i.e., it is more important that the robots do not break or completely deplete their energy sources than that they perform certain tasks as...