activity
20182021
most citedDeterministic Distributed Algorithms and Lower Bounds in the Hybrid Model

2 citations · 3 across the 3 of their papers we have counts for

collaborators

10 papers

cs.DC20212 cited

Deterministic Distributed Algorithms and Lower Bounds in the Hybrid Model

Ioannis Anagnostides, Themis Gouleakis

The $\hybrid$ model was recently introduced by Augustine et al. \cite{DBLP:conf/soda/AugustineHKSS20} in order to characterize from an algorithmic standpoint the capabilities of ne…

math.ST2020

Computationally and Statistically Efficient Truncated Regression

Constantinos Daskalakis, Themis Gouleakis, Christos Tzamos +1

We provide a computationally and statistically efficient estimator for the classical problem of truncated linear regression, where the dependent variable and its co…

stat.ML2020

Robust Learning under Strong Noise via SQs

Ioannis Anagnostides, Themis Gouleakis, Ali Marashian

This work provides several new insights on the robustness of Kearns' statistical query framework against challenging label-noise models. First, we build on a recent result by \cite…

cs.DS2020

Optimal Testing of Discrete Distributions with High Probability

Ilias Diakonikolas, Themis Gouleakis, Daniel M. Kane +2

We study the problem of testing discrete distributions with a focus on the high probability regime. Specifically, given samples from one or more discrete distributions, a property…

cs.DS2020

Secretary and Online Matching Problems with Machine Learned Advice

Antonios Antoniadis, Themis Gouleakis, Pieter Kleer +1

The classical analysis of online algorithms, due to its worst-case nature, can be quite pessimistic when the input instance at hand is far from worst-case. Often this is not an iss…

cs.DS2019

Towards Testing Monotonicity of Distributions Over General Posets

Maryam Aliakbarpour, Themis Gouleakis, John Peebles +2

In this work, we consider the sample complexity required for testing the monotonicity of distributions over partial orders. A distribution over a poset is monotone if, for any…