Constantinos Daskalakis

research

∙ 09/21/2023

Smooth Nash Equilibria: Algorithms and Complexity

A fundamental shortcoming of the concept of Nash equilibrium is its comp...

0 Constantinos Daskalakis, et al. ∙

research

∙ 07/04/2023

Online Learning and Solving Infinite Games with an ERM Oracle

While ERM suffices to attain near-optimal generalization error in the st...

0 Angelos Assos, et al. ∙

research

∙ 02/17/2023

Consistent Diffusion Models: Mitigating Sampling Drift by Learning to be Consistent

Imperfect score-matching leads to a shift between the training and the s...

0 Giannis Daras, et al. ∙

research

∙ 02/07/2023

SDYN-GANs: Adversarial Learning Methods for Multistep Generative Models for General Order Stochastic Dynamics

We introduce adversarial learning methods for data-driven generative mod...

0 Panos Stinis, et al. ∙

research

∙ 11/23/2022

Learning and Testing Latent-Tree Ising Models Efficiently

We provide time- and sample-efficient algorithms for learning and testin...

0 Davin Choo, et al. ∙

research

∙ 11/21/2022

EM's Convergence in Gaussian Latent Tree Models

We study the optimization landscape of the log-likelihood function and t...

0 Yuval Dagan, et al. ∙

research

∙ 10/18/2022

STay-ON-the-Ridge: Guaranteed Convergence to Local Minimax Equilibrium in Nonconvex-Nonconcave Games

Min-max optimization problems involving nonconvex-nonconcave objectives ...

0 Constantinos Daskalakis, et al. ∙

research

∙ 08/25/2022

Efficient Truncated Linear Regression with Unknown Noise Variance

Truncated linear regression is a classical challenge in Statistics, wher...

0 Constantinos Daskalakis, et al. ∙

research

∙ 06/18/2022

Score-Guided Intermediate Layer Optimization: Fast Langevin Mixing for Inverse Problems

We prove fast mixing and characterize the stationary distribution of the...

7 Giannis Daras, et al. ∙

research

∙ 05/06/2022

What Makes A Good Fisherman? Linear Regression under Self-Selection Bias

In the classical setting of self-selection, the goal is to learn k model...

0 Yeshwanth Cherapanamjeri, et al. ∙

research

∙ 05/04/2022

Estimation of Standard Auction Models

We provide efficient estimation methods for first- and second-price auct...

0 Yeshwanth Cherapanamjeri, et al. ∙

research

∙ 04/08/2022

The Complexity of Markov Equilibrium in Stochastic Games

We show that computing approximate stationary Markov coarse correlated e...

0 Constantinos Daskalakis, et al. ∙

research

∙ 12/13/2021

How Good are Low-Rank Approximations in Gaussian Process Regression?

We provide guarantees for approximate Gaussian Process (GP) regression r...

0 Constantinos Daskalakis, et al. ∙

research

∙ 11/17/2021

Fast Rates for Nonparametric Online Learning: From Realizability to Learning in Games

We study fast rates of convergence in the setting of nonparametric onlin...

0 Constantinos Daskalakis, et al. ∙

research

∙ 11/11/2021

Near-Optimal No-Regret Learning for Correlated Equilibria in Multi-Player General-Sum Games

Recently, Daskalakis, Fishelson, and Golowich (DFG) (NeurIPS`21) showed ...

0 Ioannis Anagnostides, et al. ∙

research

∙ 10/25/2021

Recommender Systems meet Mechanism Design

Machine learning has developed a variety of tools for learning and repre...

0 Yang Cai, et al. ∙

research

∙ 08/16/2021

Near-Optimal No-Regret Learning in General Games

We show that Optimistic Hedge – a common variant of multiplicative-weigh...

0 Constantinos Daskalakis, et al. ∙

research

∙ 07/20/2021

Statistical Estimation from Dependent Data

We consider a general statistical estimation problem wherein binary labe...

0 Yuval Dagan, et al. ∙

research

∙ 06/30/2021

A Statistical Taylor Theorem and Extrapolation of Truncated Densities

We show a statistical version of Taylor's theorem and apply this result ...

0 Constantinos Daskalakis, et al. ∙

research

∙ 01/11/2021

Independent Policy Gradient Methods for Competitive Reinforcement Learning

We obtain global, non-asymptotic convergence guarantees for independent ...

0 Constantinos Daskalakis, et al. ∙

research

∙ 10/31/2020

Efficient Methods for Structured Nonconvex-Nonconcave Min-Max Optimization

The use of min-max optimization in adversarial training of deep neural n...

20 Jelena Diakonikolas, et al. ∙

research

∙ 10/28/2020

Sample-Optimal and Efficient Learning of Tree Ising models

We show that n-variable tree-structured Ising models can be learned comp...

0 Constantinos Daskalakis, et al. ∙

research

∙ 10/26/2020

Tight last-iterate convergence rates for no-regret learning in multi-player games

We study the question of obtaining last-iterate convergence rates for no...

0 Noah Golowich, et al. ∙

research

∙ 10/22/2020

Computationally and Statistically Efficient Truncated Regression

We provide a computationally and statistically efficient estimator for t...

0 Constantinos Daskalakis, et al. ∙

research

∙ 09/21/2020

The Complexity of Constrained Min-Max Optimization

Despite its important applications in Machine Learning, min-max optimiza...

0 Constantinos Daskalakis, et al. ∙

research

∙ 08/05/2020

Generative Ensemble-Regression: Learning Stochastic Dynamics from Discrete Particle Ensemble Observations

We propose a new method for inferring the governing stochastic ordinary ...

14 Liu Yang, et al. ∙

research

∙ 07/29/2020

Truncated Linear Regression in High Dimensions

As in standard linear regression, in truncated linear regression, we are...

0 Constantinos Daskalakis, et al. ∙

research

∙ 06/07/2020

Constant-Expansion Suffices for Compressed Sensing with Generative Priors

Generative neural networks have been empirically found very promising in...

0 Constantinos Daskalakis, et al. ∙

research

∙ 04/20/2020

Estimating Ising Models from One Sample

Given one sample X ∈{± 1}^n from an Ising model [X=x]∝(x^ J x/2), whose ...

0 Yuval Dagan, et al. ∙

research

∙ 04/03/2020

Faster Gaussian Processes via Deep Embeddings

Gaussian processes provide a probabilistic framework for quantifying unc...

0 Constantinos Daskalakis, et al. ∙

research

∙ 03/18/2020

Logistic-Regression with peer-group effects via inference in higher order Ising models

Spin glass models, such as the Sherrington-Kirkpatrick, Hopfield and Isi...

0 Constantinos Daskalakis, et al. ∙

research

∙ 03/02/2020

Subadditivity of Probability Divergences on Bayes-Nets with Applications to Time Series GANs

GANs for time series data often use sliding windows or self-attention to...

0 Mucong Ding, et al. ∙

research

∙ 02/16/2020

Simple, Credible, and Approximately-Optimal Auctions

We identify the first static credible mechanism for multi-item additive ...

0 Constantinos Daskalakis, et al. ∙

research

∙ 01/31/2020

Last Iterate is Slower than Averaged Iterate in Smooth Convex-Concave Saddle Point Problems

In this paper we study the smooth convex-concave saddle point problem. S...

0 Noah Golowich, et al. ∙

research

∙ 11/06/2019

Multi-Item Mechanisms without Item-Independence: Learnability via Robustness

We study the sample complexity of learning revenue-optimal multi-item au...

0 Johaness Brustle, et al. ∙

research

∙ 10/15/2019

SGD Learns One-Layer Networks in WGANs

Generative adversarial networks (GANs) are a widely used framework for l...

44 Qi Lei, et al. ∙

research

∙ 06/21/2019

Learning from weakly dependent data under Dobrushin's condition

Statistical learning theory has largely focused on learning and generali...

0 Yuval Dagan, et al. ∙

research

∙ 05/08/2019

Regression from Dependent Observations

The standard linear and logistic regression models assume that the respo...

0 Constantinos Daskalakis, et al. ∙

research

∙ 11/26/2018

HOGWILD!-Gibbs can be PanAccurate

Asynchronous Gibbs sampling has been recently shown to be fast-mixing an...

0 Constantinos Daskalakis, et al. ∙

research

∙ 10/28/2018

Smoothed Analysis of Discrete Tensor Decomposition and Assemblies of Neurons

We analyze linear independence of rank one tensors produced by tensor po...

0 Nima Anari, et al. ∙

research

∙ 09/11/2018

Efficient Statistics, in High Dimensions, from Truncated Samples

We provide an efficient algorithm for the classical problem, going back ...

0 Constantinos Daskalakis, et al. ∙

research

∙ 07/11/2018

Last-Iterate Convergence: Zero-Sum Games and Constrained Min-Max Optimization

Motivated by applications in Game Theory, Optimization, and Generative A...

0 Constantinos Daskalakis, et al. ∙

research

∙ 07/11/2018

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

Motivated by applications in Optimization, Game Theory, and the training...

0 Constantinos Daskalakis, et al. ∙

research

∙ 05/24/2018

Learning and Testing Causal Models with Interventions

We consider testing and learning problems on causal Bayesian networks as...

0 Jayadev Acharya, et al. ∙

research

∙ 03/01/2018

Robust Repeated Auctions under Heterogeneous Buyer Behavior

We study revenue optimization in a repeated auction between a single sel...

0 Shipra Agrawal, et al. ∙

research

∙ 12/26/2017

The Robust Manifold Defense: Adversarial Training using Generative Models

Deep neural networks are demonstrating excellent performance on several ...

0 Andrew Ilyas, et al. ∙

research

∙ 10/31/2017

Training GANs with Optimism

We address the issue of limit cycling behavior in training Generative Ad...

0 Constantinos Daskalakis, et al. ∙

research

∙ 10/11/2017

Concentration of Multilinear Functions of the Ising Model with Applications to Network Data

We prove near-tight concentration of measure for polynomial functions of...

0 Constantinos Daskalakis, et al. ∙

research

∙ 09/01/2017

Learning Multi-item Auctions with (or without) Samples

We provide algorithms that learn simple auctions whose revenue is approx...

0 Yang Cai, et al. ∙

research

∙ 02/23/2017

A Converse to Banach's Fixed Point Theorem and its CLS Completeness

Banach's fixed point theorem for contraction maps has been widely used t...

0 Constantinos Daskalakis, et al. ∙

Constantinos Daskalakis

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