[H/T Jelani Nelson]
In recent years there has been increasing interest in using machine
learning to improve the performance of classical algorithms in
computer science, by fine-tuning their behavior to adapt to the
properties of the input distribution. This “data-driven” or
“learning-based” approach to algorithm design has the potential to
significantly improve the efficiency of some of the most widely used
algorithms. For example, it has been used to design better data
structures, online algorithms, streaming and sketching algorithms,
market mechanisms and algorithms for combinatorial optimization,
similarity search and inverse problems. This virtual workshop will
feature talks from experts at the forefront of this exciting area.
The workshop will take place virtually on July 13-14, 2021.
Registration is free but mandatory. Link to register: https://fodsi.us/ml4a.html
Confirmed Speakers:
- Alex Dimakis (UT Austin)
- Yonina Eldar (Weizmann)
- Anna Goldie (Google Brain, Stanford)
- Reinhard Heckel (Technical University of Munich)
- Stefanie Jegelka (MIT)
- Tim Kraska (MIT)
- Benjamin Moseley (CMU)
- David Parkes (Harvard)
- Ola Svensson (EPFL)
- Tuomas Sandholm (CMU, Optimized Markets, Strategy Robot, Strategic Machine)
- Sergei Vassilvitski (Google)
- Ellen Vitercik (CMU/UC Berkeley)
- David Woodruff (CMU)
Organizers:
- Costis Daskalakis (MIT)
- Paul Hand (Northeastern)
- Piotr Indyk (MIT)
- Michael Mitzenmacher (Harvard)
- Ronitt Rubinfeld (MIT)
- Jelani Nelson (UC Berkeley)
Unrelated announcement: JACM looking for editor in chief
(h/t Salil Vadhan)
The Journal of the ACM is looking for a new editor in chief: see the call for nominations. The (soft) deadline to submit nominations (including self nominations) is July 19th and you can do so by emailing Chris Hankin at c.hankin@imperial.ac.uk
Windows On Theory 