Summer Cluster: Challenges in Quantum Computation
Small- to medium-scale quantum computers are around the corner, and the biggest upcoming challenges are expected to be algorithmic. What computational tasks can such computers speed up? And how can we test them? More specifically, what are the prospects for quantum simulation, quantum machine learning, and protocols for testing quantum devices? The answers to these questions touch on deep issues a
Past, Present and Future of Randomized Numerical Linear Algebra I
The introduction of randomization over the last decade into the design and analysis of algorithms for matrix computations has provided a new paradigm, particularly appropriate for many very large-scale applications, as well as a complementary perspective to traditional numerical linear algebra approaches to matrix computations. These novel approaches have been motivated by technological developmen
Workshop Abstracts | Simons Institute for the Theory of Computing
One of the core problems of modern statistics and machine learning is to approximate difficult-to-compute probability distributions. This problem is especially important in probabilistic modeling, which frames all inference about unknown quantities as a calculation about a conditional distribution. In this tutorial I review and discuss variational inference (VI), a method a that approximates proba
Representations for Language: From Word Embeddings to Sentence Meanings
A basic – yet very successful – tool for modeling human language has been a new generation of distributed word representations: neural word embeddings. However, beyond just word meanings, we need to understand the meanings of larger pieces of text and the relationships between pieces of text, like questions and answers. Two requirements for that are good ways to understand the structure of human l
Tutorial on Deep Learning
Lecture 1: Tutorial on Deep Learning I Lecture 2: Tutorial on Deep Learning II Lecture 3: Tutorial on Deep Learning III Lecture 4: Tutorial on Deep Learning IV This series of talks is part of the Foundations of Machine Learning Boot Camp. Videos for each talk area will be available through the links above. Speaker: Ruslan Salakhutdinov, Carnegie Mellon University The Simons Institute for the Theor
Theoretical Foundations of Big Data Analysis
We live in an era of "big data": science, engineering, and technology are producing increasingly large data streams, with petabyte and exabyte scales becoming increasingly common. In scientific fields, such data arise in part because tests of standard theories increasingly focus on extreme physical conditions (e.g., particle physics) and in part because science has become increasingly exploratory
Workshop Schedule | Simons Institute for the Theory of Computing
The Boot Camp is intended to acquaint program participants with the key themes of the program. It will consist of nine lectures or pairs of lectures given by experts on various topics as follows: Algorithmic High-Dimensional Geometry Streaming, Sketching and Sufficient Statistics Past, Present and Future of Randomized Numerical Linear Algebra Big Data: The Computation/Statistics Interface Theory a
Economics and Computation Boot Camp
The Boot Camp is intended to acquaint program participants with the key themes of the program. Each speaker will give a mini-course of two lectures on the topics below. Kenneth Arrow (Stanford University): "Approximating Competitive Equilibria: A History" Costis Daskalakis (Massachusetts Institute of Technology): "Complexity and Algorithmic Game Theory" Drew Fudenberg (Harvard University): "Learni
Foundations of Machine Learning
The goal of this program was to grow the reach and impact of computer science theory within machine learning. One central component of the program was formalizing basic questions in developing areas of practice and gaining fundamental insights into these. Target areas of particular interest were interactive learning and representation learning. Interactive learning consists of scenarios in wh
Fine-Grained Complexity and Algorithm Design Boot Camp
The Boot Camp is intended to acquaint program participants with the key themes of the program. Each speaker will give a mini-course on the topics below. Thore Husfeldt (IT University of Copenhagen): "Exponential-Time Algorithms" Russell Impagliazzo (UC San Diego): "Derandomization" Dániel Marx (Hungarian Academy of Sciences): "Parameterized and Exact Algorithms" Ramamohan Paturi (UC San Diego): "E
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https://simons.berkeley.edu/sites/default/files/docs/5969/operadiccompositionality-simons.pdf
Workshop Schedule | Simons Institute for the Theory of Computing