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09/21/2023
Smooth Nash Equilibria: Algorithms and Complexity
A fundamental shortcoming of the concept of Nash equilibrium is its comp...
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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...
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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...
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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...
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11/23/2022
Learning and Testing Latent-Tree Ising Models Efficiently
We provide time- and sample-efficient algorithms for learning and testin...
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11/21/2022
EM's Convergence in Gaussian Latent Tree Models
We study the optimization landscape of the log-likelihood function and t...
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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 ...
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08/25/2022
Efficient Truncated Linear Regression with Unknown Noise Variance
Truncated linear regression is a classical challenge in Statistics, wher...
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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...
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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...
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05/04/2022
Estimation of Standard Auction Models
We provide efficient estimation methods for first- and second-price auct...
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04/08/2022
The Complexity of Markov Equilibrium in Stochastic Games
We show that computing approximate stationary Markov coarse correlated e...
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12/13/2021
How Good are Low-Rank Approximations in Gaussian Process Regression?
We provide guarantees for approximate Gaussian Process (GP) regression r...
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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...
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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 ...
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10/25/2021
Recommender Systems meet Mechanism Design
Machine learning has developed a variety of tools for learning and repre...
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08/16/2021
Near-Optimal No-Regret Learning in General Games
We show that Optimistic Hedge – a common variant of multiplicative-weigh...
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07/20/2021
Statistical Estimation from Dependent Data
We consider a general statistical estimation problem wherein binary labe...
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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 ...
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01/11/2021
Independent Policy Gradient Methods for Competitive Reinforcement Learning
We obtain global, non-asymptotic convergence guarantees for independent ...
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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...
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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...
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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...
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10/22/2020
Computationally and Statistically Efficient Truncated Regression
We provide a computationally and statistically efficient estimator for t...
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09/21/2020
The Complexity of Constrained Min-Max Optimization
Despite its important applications in Machine Learning, min-max optimiza...
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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 ...
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07/29/2020
Truncated Linear Regression in High Dimensions
As in standard linear regression, in truncated linear regression, we are...
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06/07/2020
Constant-Expansion Suffices for Compressed Sensing with Generative Priors
Generative neural networks have been empirically found very promising in...
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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 ...
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04/03/2020
Faster Gaussian Processes via Deep Embeddings
Gaussian processes provide a probabilistic framework for quantifying unc...
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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...
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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...
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02/16/2020
Simple, Credible, and Approximately-Optimal Auctions
We identify the first static credible mechanism for multi-item additive ...
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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...
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11/06/2019
Multi-Item Mechanisms without Item-Independence: Learnability via Robustness
We study the sample complexity of learning revenue-optimal multi-item au...
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10/15/2019
SGD Learns One-Layer Networks in WGANs
Generative adversarial networks (GANs) are a widely used framework for l...
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06/21/2019
Learning from weakly dependent data under Dobrushin's condition
Statistical learning theory has largely focused on learning and generali...
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05/08/2019
Regression from Dependent Observations
The standard linear and logistic regression models assume that the respo...
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11/26/2018
HOGWILD!-Gibbs can be PanAccurate
Asynchronous Gibbs sampling has been recently shown to be fast-mixing an...
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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...
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09/11/2018
Efficient Statistics, in High Dimensions, from Truncated Samples
We provide an efficient algorithm for the classical problem, going back ...
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07/11/2018
Last-Iterate Convergence: Zero-Sum Games and Constrained Min-Max Optimization
Motivated by applications in Game Theory, Optimization, and Generative A...
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07/11/2018
The Limit Points of (Optimistic) Gradient Descent in Min-Max Optimization
Motivated by applications in Optimization, Game Theory, and the training...
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05/24/2018
Learning and Testing Causal Models with Interventions
We consider testing and learning problems on causal Bayesian networks as...
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03/01/2018
Robust Repeated Auctions under Heterogeneous Buyer Behavior
We study revenue optimization in a repeated auction between a single sel...
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12/26/2017
The Robust Manifold Defense: Adversarial Training using Generative Models
Deep neural networks are demonstrating excellent performance on several ...
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10/31/2017
Training GANs with Optimism
We address the issue of limit cycling behavior in training Generative Ad...
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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...
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09/01/2017
Learning Multi-item Auctions with (or without) Samples
We provide algorithms that learn simple auctions whose revenue is approx...
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02/23/2017