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20182025
most citedLearning from weakly dependent data under Dobrushin's condition

5 citations · 12 across the 13 of their papers we have counts for

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cs.LG2024

Dimension-free Private Mean Estimation for Anisotropic Distributions

Yuval Dagan, Michael I. Jordan, Xuelin Yang +2

We present differentially private algorithms for high-dimensional mean estimation. Previous private estimators on distributions over suffer from a curse of dimension…

cs.LG2024

Breaking the Barrier for Sequential Calibration

Yuval Dagan, Constantinos Daskalakis, Maxwell Fishelson +3

A set of probabilistic forecasts is calibrated if each prediction of the forecaster closely approximates the empirical distribution of outcomes on the subset of timesteps where tha…

cs.LG2023

From External to Swap Regret 2.0: An Efficient Reduction and Oblivious Adversary for Large Action Spaces

Yuval Dagan, Constantinos Daskalakis, Maxwell Fishelson +1

We provide a novel reduction from swap-regret minimization to external-regret minimization, which improves upon the classical reductions of Blum-Mansour [BM07] and Stolz-Lugosi [SL…

cs.LG2023

Online Learning and Solving Infinite Games with an ERM Oracle

Angelos Assos, Idan Attias, Yuval Dagan +2

While ERM suffices to attain near-optimal generalization error in the stochastic learning setting, this is not known to be the case in the online learning setting, where algorithms…

cs.LG2022

EM's Convergence in Gaussian Latent Tree Models

Yuval Dagan, Constantinos Daskalakis, Anthimos Vardis Kandiros

We study the optimization landscape of the log-likelihood function and the convergence of the Expectation-Maximization (EM) algorithm in latent Gaussian tree models, i.e. tree-stru…

cs.LG2021

Statistical Estimation from Dependent Data

Yuval Dagan, Constantinos Daskalakis, Nishanth Dikkala +2

We consider a general statistical estimation problem wherein binary labels across different observations are not independent conditioned on their feature vectors, but dependent, ca…