September 28, 2020 to September 29, 2020
Speaker: 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...
October 19, 2020 to October 20, 2020
Speaker: 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...
October 26, 2020 to October 27, 2020
Speaker: 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...
November 9, 2020 to November 10, 2020
Speaker: 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...
November 23, 2020 to November 24, 2020
Speaker: 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),...
November 30, 2020 to December 1, 2020
Speaker: 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...
