5 citations · 10 across the 3 of their papers we have counts for
11 papers
Majorizing Measures, Sequential Complexities, and Online Learning
Adam Block, Yuval Dagan, Sasha Rakhlin
We introduce the technique of generic chaining and majorizing measures for controlling sequential Rademacher complexity. We relate majorizing measures to the notion of fractional c…
Adversarial Laws of Large Numbers and Optimal Regret in Online Classification
Noga Alon, Omri Ben-Eliezer, Yuval Dagan +3
Laws of large numbers guarantee that given a large enough sample from some population, the measure of any fixed sub-population is well-estimated by its frequency in the sample. We…
Learning Ising models from one or multiple samples
Yuval Dagan, Constantinos Daskalakis, Nishanth Dikkala +1
There have been two separate lines of work on estimating Ising models: (1) estimating them from multiple independent samples under minimal assumptions about the model's interaction…
PAC learning with stable and private predictions
Yuval Dagan, Vitaly Feldman
We study binary classification algorithms for which the prediction on any point is not too sensitive to individual examples in the dataset. Specifically, we consider the notions of…
Interaction is necessary for distributed learning with privacy or communication constraints
Yuval Dagan, Vitaly Feldman
Local differential privacy (LDP) is a model where users send privatized data to an untrusted central server whose goal it to solve some data analysis task. In the non-interactive v…
Learning from weakly dependent data under Dobrushin's condition
Yuval Dagan, Constantinos Daskalakis, Nishanth Dikkala +1
Statistical learning theory has largely focused on learning and generalization given independent and identically distributed (i.i.d.) samples. Motivated by applications involving t…