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Algorithms

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Publication

The Importance of Communities for Learning to Influence

Eric Balkanski, Nicole Immorlica, Yaron Singer

Advances in Neural Information Processing Systems 30 (NIPS 2017) | December 2017

Publication

Finite Sample Analysis of the GTD Policy Evaluation Algorithms in Markov Setting

Yue Wang, Wei Chen, Yuting Liu, Zhi-Ming Ma, Tie-Yan Liu

Advances in Neural Information Processing Systems | December 2017

Publication

Experience report on applying software analytics in incident management of online service

Jian-Guang Lou, Qingwei Lin 林庆维, …

Automated Software Engineering | December 2017, Vol 24: pp. 905-941

Publication

Learning to Play Stackelberg Security Games

Avrim Blum, Nika Haghtalab, Ariel D. Procaccia

Improving Homeland Security Decisions, 25 | 2017

Publication

Inferring Formal Properties of Production Key-Value Stores

Edgar Pek, Pranav Garg, Muntasir Raihan Rahman, Karl Palmskog, Indranil Gupta, Madhusudan Parthasarathy

December 2017

Publication

Identify the Nash Equilibrium in Static Games with Random Payoffs.

Yichi Zhou, Jialian Li, Jun Zhu

International Conference on Machine Learning (ICML) | December 2017

NVIDIA Pioneering Research Award

Video

Optimal Safe Path Planning Using Polynomials: From Algebra to Geometry

November 27, 2017 | Nak-seung Patrick Hyun

Optimal Safe Path Planning Using Polynomials: From Algebra to Geometry

Publication

Approximate Efficiency in Matching Markets

Nicole Immorlica, Brendan Lucier, E. Glen Weyl, Joshua Mollner

The 13th Conference on Web and Internet Economics (WINE 2017) | November 2017

Video

Foundations of Data Science – Lecture 9 – Two Applications of SVD

November 15, 2017 | Ravi Kannan

Modern data often consists of feature vectors with a large number of features. High-dimensional geometry and Linear Algebra (Singular Value Decomposition) are two of the crucial areas which form the mathematical foundations of Data Science.…

Foundations of Data Science - Lecture 9 - Two Applications of SVD

Video

Foundations of Data Science – Lecture 8 – Low Rank Approximation (LRA) via Length Squared Sampling

November 13, 2017 | Ravi Kannan

Modern data often consists of feature vectors with a large number of features. High-dimensional geometry and Linear Algebra (Singular Value Decomposition) are two of the crucial areas which form the mathematical foundations of Data Science.…

Foundations of Data Science - Lecture 8 - Low Rank Approximation (LRA) via Length Squared Sampling