C. Daskalakis

Publications
Influence

The complexity of computing a Nash equilibrium

C. Daskalakis, P. Goldberg, C. Papadimitriou
Computer Science, Mathematics
STOC '06
21 May 2006

We resolve the question of the complexity of Nash equilibrium by showing that the problem of computing a Nash equilibrium in a game with 4 or more players is complete for the complexity class PPAD. Expand

Training GANs with Optimism

C. Daskalakis, Andrew Ilyas, Vasilis Syrgkanis, Haoyang Zeng
Computer Science, Mathematics
ICLR
1 October 2017

We address the issue of limit cycling behavior in training Generative Adversarial Networks and propose the use of Optimistic Mirror Decent (OMD) for Wasserstein GANs. Expand

The Complexity of Computing a Nash Equilibrium

C. Daskalakis, P. Goldberg, C. Papadimitriou
Mathematics, Computer Science
SIAM J. Comput.
1 May 2009

We show that finding a Nash equilibrium in three-player games is indeed PPAD-complete; and we do so by a reduction from Brouwer's problem, thus establishing that the two problems are computationally equivalent. Expand

Optimal Testing for Properties of Distributions

J. Acharya, C. Daskalakis, Gautam Kamath
Computer Science, Mathematics
NIPS
21 July 2015

We provide a general approach via which we obtain sample-optimal and computationally efficient testers for all these distribution families. Expand

The Limit Points of (Optimistic) Gradient Descent in Min-Max Optimization

C. Daskalakis, Ioannis Panageas
Computer Science, Mathematics
NeurIPS
11 July 2018

We characterize the limit points of two basic first order methods, namely Gradient Descent/Ascent (GDA) and Optimistic Gradient Ascent (OGDA). Expand

A note on approximate Nash equilibria

C. Daskalakis, Aranyak Mehta, C. Papadimitriou
Mathematics, Computer Science
Theor. Comput. Sci.
1 April 2009

We give a simple, linear-time algorithm examining just two strategies per player and resulting in a 12-approximate Nash equilibrium in any 2-player game. Expand

The Robust Manifold Defense: Adversarial Training using Generative Models

Andrew Ilyas, A. Jalal, Eirini Asteri, C. Daskalakis, A. Dimakis
Computer Science, Mathematics
ArXiv
26 December 2017

We propose a new type of attack for finding adversarial examples for image classifiers. Expand

An algorithmic characterization of multi-dimensional mechanisms

Yang Cai, C. Daskalakis, S. M. Weinberg
Computer Science, Mathematics
STOC '12
20 December 2011

We show that every feasible, Bayesian, multi-item multi-bidder mechanism for independent, additive bidders can be implemented as a mechanism that: (a) allocates every item independently of the other items; (b) for the allocation of each item it uses a strict ordering of all bbiders' types; and allocates the item using a distribution over hierarchical mechanisms that iron this ordering into a non-strict ordering. Expand

Strong Duality for a Multiple-Good Monopolist

C. Daskalakis, Alan Deckelbaum, Christos Tzamos
Computer Science, Mathematics
EC
15 September 2014

We provide a duality-based framework for revenue maximization in a multiple-good monopoly and prove that the optimal values of these problems are always equal. Expand

Progress in approximate nash equilibria

C. Daskalakis, Aranyak Mehta, C. Papadimitriou
Mathematics, Computer Science
EC '07
11 June 2007

We give a polynomial algorithm for computing an ε-approximate Nash equilibrium in 2-player games with ε ≈ .38; our algorithm computes equilibria with arbitrarily large supports. Expand

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