[Ml-stat-talks] Fwd: Ludwig Schmidt
ehazan at CS.Princeton.EDU
Wed Mar 15 10:46:14 EDT 2017
talk of interest.
Approximation Algorithms for Large-Scale Data Analysis
Ludwig Schmidt, Massachusetts Institute of Technology
EE Departmental Seminars
Thursday, March 16, 2017 - 4:30pm to 5:30pm
Over the past decade, the scale of computational problems in machine
learning has grown tremendously. At the same time, the slowdown of Moore’s
Law is making it harder to apply existing algorithms to large data sets. As
this trend will only increase in the foreseeable future, we need faster
algorithms for many computational problems in machine learning.
In this talk, I will show how ideas from approximation algorithms can be
used to speed up multiple key tasks in machine learning. Since the
underlying statistical problems are inherently noisy, computing a good
approximate solution often leads to significantly faster algorithms that
match the statistical performance of their exact counterparts. One such
connection is in the area of constrained estimation, which underlies many
problems such as compressive sensing, sparse linear regression, and matrix
completion. By introducing approximate projections, I will connect
classical tools from theoretical computer science with these estimation
In the second part of the talk, I will turn to classification and give
strong evidence that approximation is inherently necessary in order to get
sub-quadratic running time for widely used learning algorithms such as
kernel methods and neural networks. On the other hand, I will show how
ideas from nearest neighbor algorithms can exploit problem structure and
speed up large-multiclass networks.
Ludwig Schmidt is a PhD student at MIT, advised by Prof. Piotr Indyk.
Ludwig’s research interests revolve around algorithmic aspects of machine
learning, statistics, and signal processing. Ludwig received a Google PhD
Fellowship in machine learning, a Simons-Berkeley research fellowship, and
a best paper award at the International Conference on Machine Learning
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