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TAU papers accepted to COLT 2021

Four members of TAU's ELLIS unit: Yishay Mansour, Tomer Koren, Roi Livni and Yair Carmon will present their work at the 34th Annual Conference on Learning Theory (COLT 2021)



All selected papers address theoretical aspects of machine learning, broadly defined as a subject at the intersection of computer science, statistics and applied mathematics. here is a list of TAU's papers:


The Sparse Vector Technique, Revisited Haim Kaplan, Yishay Mansour, Uri Stemmer

The 34th Annual Conference on Learning Theory (COLT 2021)

Online Markov Decision Processes with Aggregate Bandit Feedback

Alon Cohen, Haim Kaplan, Tomer Koren, Yishay Mansour

The 34th Annual Conference on Learning Theory (COLT 2021)


SGD Generalizes Better Than GD (And Regularization Doesn’t Help) Idan Amir, Tomer Koren, Roi Livni The 34th Annual Conference on Learning Theory (COLT 2021)


Lazy OCO: Online Convex Optimization on a Switching Budget Uri Sherman, Tomer Koren The 34th Annual Conference on Learning Theory (COLT 2021)

Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford

The 34th Annual Conference on Learning Theory (COLT 2021)

Online Learning with Simple Predictors and a Combinatorial Characterization of Minimax in 0/1 Games Steve Hanneke; Roi Livni; Shay Moran

The 34th Annual Conference on Learning Theory (COLT 2021)


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