
Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence
Policy optimization, which learns the policy of interest by maximizing t...
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How FineTuning Allows for Effective MetaLearning
Representation learning has been widely studied in the context of metal...
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Bilinear Classes: A Structural Framework for Provable Generalization in RL
This work introduces Bilinear Classes, a new structural framework, which...
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MUSBO: Modelbased Uncertainty Regularized and Sample Efficient Batch Optimization for Deployment Constrained Reinforcement Learning
In many contemporary applications such as healthcare, finance, robotics,...
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A Theory of Label Propagation for Subpopulation Shift
One of the central problems in machine learning is domain adaptation. Un...
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Provably Efficient Policy Gradient Methods for TwoPlayer ZeroSum Markov Games
Policy gradient methods are widely used in solving twoplayer zerosum g...
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Beyond Lazy Training for Overparameterized Tensor Decomposition
Overparametrization is an important technique in training neural networ...
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How Important is the TrainValidation Split in MetaLearning?
Metalearning aims to perform fast adaptation on a new task through lear...
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SanityChecking Pruning Methods: Random Tickets can Win the Jackpot
Network pruning is a method for reducing testtime computational resourc...
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Generalized Leverage Score Sampling for Neural Networks
Leverage score sampling is a powerful technique that originates from the...
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Predicting What You Already Know Helps: Provable SelfSupervised Learning
Selfsupervised representation learning solves auxiliary prediction task...
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Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy
We provide a detailed asymptotic study of gradient flow trajectories and...
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Towards Understanding Hierarchical Learning: Benefits of Neural Representations
Deep neural networks can empirically perform efficient hierarchical lear...
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Convergence of MetaLearning with TaskSpecific Adaptation over Partial Parameters
Although modelagnostic metalearning (MAML) is a very successful algori...
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Shape Matters: Understanding the Implicit Bias of the Noise Covariance
The noise in stochastic gradient descent (SGD) provides a crucial implic...
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Distributed Estimation for Principal Component Analysis: a Gapfree Approach
The growing size of modern data sets brings many challenges to the exist...
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Steepest Descent Neural Architecture Optimization: Escaping Local Optimum with Signed Neural Splitting
We propose signed splitting steepest descent (S3D), which progressively ...
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FewShot Learning via Learning the Representation, Provably
This paper studies fewshot learning via representation learning, where ...
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Kernel and Rich Regimes in Overparametrized Models
A recent line of work studies overparametrized neural networks in the "k...
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Agnostic Qlearning with Function Approximation in Deterministic Systems: Tight Bounds on Approximation Error and Sample Complexity
The current paper studies the problem of agnostic Qlearning with functi...
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When Does NonOrthogonal Tensor Decomposition Have No Spurious Local Minima?
We study the optimization problem for decomposing d dimensional fourtho...
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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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Beyond Linearization: On Quadratic and HigherOrder Approximation of Wide Neural Networks
Recent theoretical work has established connections between overparamet...
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Optimal transport mapping via input convex neural networks
In this paper, we present a novel and principled approach to learn the o...
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Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes
Policy gradient methods are among the most effective methods in challeng...
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Incremental Methods for Weakly Convex Optimization
We consider incremental algorithms for solving weakly convex optimizatio...
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Convergence of Adversarial Training in Overparametrized Networks
Neural networks are vulnerable to adversarial examples, i.e. inputs that...
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Neural TemporalDifference Learning Converges to Global Optima
Temporaldifference learning (TD), coupled with neural networks, is amon...
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Lexicographic and DepthSensitive Margins in Homogeneous and NonHomogeneous Deep Models
With an eye toward understanding complexity control in deep learning, we...
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Solving NonConvex NonConcave MinMax Games Under PolyakŁojasiewicz Condition
In this short note, we consider the problem of solving a minmax zerosu...
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Gradient Descent Finds Global Minima of Deep Neural Networks
Gradient descent finds a global minimum in training deep neural networks...
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On the Margin Theory of Feedforward Neural Networks
Past works have shown that, somewhat surprisingly, overparametrization ...
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Provably Correct Automatic Subdifferentiation for Qualified Programs
The Cheap Gradient Principle (Griewank 2008)  the computational cost ...
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Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced
We study the implicit regularization imposed by gradient descent for lea...
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Adding One Neuron Can Eliminate All Bad Local Minima
One of the main difficulties in analyzing neural networks is the noncon...
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Stochastic subgradient method converges on tame functions
This work considers the question: what convergence guarantees does the s...
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On the Power of Overparametrization in Neural Networks with Quadratic Activation
We provide new theoretical insights on why overparametrization is effec...
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Gradient PrimalDual Algorithm Converges to SecondOrder Stationary Solutions for Nonconvex Distributed Optimization
In this work, we study two firstorder primaldual based algorithms, the...
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Solving Approximate Wasserstein GANs to Stationarity
Generative Adversarial Networks (GANs) are one of the most practical str...
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Gradient Descent Learns Onehiddenlayer CNN: Don't be Afraid of Spurious Local Minima
We consider the problem of learning a onehiddenlayer neural network wi...
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Learning Onehiddenlayer Neural Networks with Landscape Design
We consider the problem of learning a onehiddenlayer neural network: w...
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Firstorder Methods Almost Always Avoid Saddle Points
We establish that firstorder methods avoid saddle points for almost all...
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When is a Convolutional Filter Easy To Learn?
We analyze the convergence of (stochastic) gradient descent algorithm fo...
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An inexact subsampled proximal Newtontype method for largescale machine learning
We propose a fast proximal Newtontype algorithm for minimizing regulari...
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Theoretical insights into the optimization landscape of overparameterized shallow neural networks
In this paper we study the problem of learning a shallow artificial neur...
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Gradient Descent Can Take Exponential Time to Escape Saddle Points
Although gradient descent (GD) almost always escapes saddle points asymp...
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A Flexible Framework for Hypothesis Testing in Highdimensions
Hypothesis testing in the linear regression model is a fundamental stati...
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Statistical Inference for Model Parameters in Stochastic Gradient Descent
The stochastic gradient descent (SGD) algorithm has been widely used in ...
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Blackbox Importance Sampling
Importance sampling is widely used in machine learning and statistics, b...
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Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and Highdimensional Data
Sketching techniques have become popular for scaling up machine learning...
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Jason D. Lee
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Assistant Professor, Data Science and Operations Department, Marshall School of Business, University of Southern California.