
NearOptimal NoRegret Learning in General Games
We show that Optimistic Hedge – a common variant of multiplicativeweigh...
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Statistical Estimation from Dependent Data
We consider a general statistical estimation problem wherein binary labe...
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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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Independent Policy Gradient Methods for Competitive Reinforcement Learning
We obtain global, nonasymptotic convergence guarantees for independent ...
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Efficient Methods for Structured NonconvexNonconcave MinMax Optimization
The use of minmax optimization in adversarial training of deep neural n...
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SampleOptimal and Efficient Learning of Tree Ising models
We show that nvariable treestructured Ising models can be learned comp...
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Tight lastiterate convergence rates for noregret learning in multiplayer games
We study the question of obtaining lastiterate convergence rates for no...
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Computationally and Statistically Efficient Truncated Regression
We provide a computationally and statistically efficient estimator for t...
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The Complexity of Constrained MinMax Optimization
Despite its important applications in Machine Learning, minmax optimiza...
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Generative EnsembleRegression: Learning Stochastic Dynamics from Discrete Particle Ensemble Observations
We propose a new method for inferring the governing stochastic ordinary ...
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Truncated Linear Regression in High Dimensions
As in standard linear regression, in truncated linear regression, we are...
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ConstantExpansion Suffices for Compressed Sensing with Generative Priors
Generative neural networks have been empirically found very promising in...
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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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Faster Gaussian Processes via Deep Embeddings
Gaussian processes provide a probabilistic framework for quantifying unc...
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LogisticRegression with peergroup effects via inference in higher order Ising models
Spin glass models, such as the SherringtonKirkpatrick, Hopfield and Isi...
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Subadditivity of Probability Divergences on BayesNets with Applications to Time Series GANs
GANs for time series data often use sliding windows or selfattention to...
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Simple, Credible, and ApproximatelyOptimal Auctions
We identify the first static credible mechanism for multiitem additive ...
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Last Iterate is Slower than Averaged Iterate in Smooth ConvexConcave Saddle Point Problems
In this paper we study the smooth convexconcave saddle point problem. S...
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MultiItem Mechanisms without ItemIndependence: Learnability via Robustness
We study the sample complexity of learning revenueoptimal multiitem au...
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SGD Learns OneLayer Networks in WGANs
Generative adversarial networks (GANs) are a widely used framework for l...
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Learning from weakly dependent data under Dobrushin's condition
Statistical learning theory has largely focused on learning and generali...
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Regression from Dependent Observations
The standard linear and logistic regression models assume that the respo...
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HOGWILD!Gibbs can be PanAccurate
Asynchronous Gibbs sampling has been recently shown to be fastmixing an...
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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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Efficient Statistics, in High Dimensions, from Truncated Samples
We provide an efficient algorithm for the classical problem, going back ...
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LastIterate Convergence: ZeroSum Games and Constrained MinMax Optimization
Motivated by applications in Game Theory, Optimization, and Generative A...
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The Limit Points of (Optimistic) Gradient Descent in MinMax Optimization
Motivated by applications in Optimization, Game Theory, and the training...
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Learning and Testing Causal Models with Interventions
We consider testing and learning problems on causal Bayesian networks as...
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Robust Repeated Auctions under Heterogeneous Buyer Behavior
We study revenue optimization in a repeated auction between a single sel...
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The Robust Manifold Defense: Adversarial Training using Generative Models
Deep neural networks are demonstrating excellent performance on several ...
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Training GANs with Optimism
We address the issue of limit cycling behavior in training Generative Ad...
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Concentration of Multilinear Functions of the Ising Model with Applications to Network Data
We prove neartight concentration of measure for polynomial functions of...
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Learning Multiitem Auctions with (or without) Samples
We provide algorithms that learn simple auctions whose revenue is approx...
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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...
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Ten Steps of EM Suffice for Mixtures of Two Gaussians
The ExpectationMaximization (EM) algorithm is a widely used method for ...
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Learning in Auctions: Regret is Hard, Envy is Easy
A line of recent work provides welfare guarantees of simple combinatoria...
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Optimum Statistical Estimation with Strategic Data Sources
We propose an optimum mechanism for providing monetary incentives to the...
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Constantinos Daskalakis
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