
Exploration and Incentives in Reinforcement Learning
How do you incentivize selfinterested agents to explore when they prefe...
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TaskOptimal Exploration in Linear Dynamical Systems
Exploration in unknown environments is a fundamental problem in reinforc...
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Learning the Linear Quadratic Regulator from Nonlinear Observations
We introduce a new problem setting for continuous control called the LQR...
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Making NonStochastic Control (Almost) as Easy as Stochastic
Recent literature has made much progress in understanding online LQR: a ...
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Constrained episodic reinforcement learning in concaveconvex and knapsack settings
We propose an algorithm for tabular episodic reinforcement learning with...
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Balancing Competing Objectives with Noisy Data: ScoreBased Classifiers for WelfareAware Machine Learning
While realworld decisions involve many competing objectives, algorithmi...
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Logarithmic Regret for Adversarial Online Control
We introduce a new algorithm for online linearquadratic control in a kn...
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RewardFree Exploration for Reinforcement Learning
Exploration is widely regarded as one of the most challenging aspects of...
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Naive Exploration is Optimal for Online LQR
We consider the problem of online adaptive control of the linear quadrat...
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Improper Learning for NonStochastic Control
We consider the problem of controlling a possibly unknown linear dynamic...
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Corruption Robust Exploration in Episodic Reinforcement Learning
We initiate the study of multistage episodic reinforcement learning und...
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The gradient complexity of linear regression
We investigate the computational complexity of several basic linear alge...
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NonAsymptotic GapDependent Regret Bounds for Tabular MDPs
This paper establishes that optimistic algorithms attain gapdependent a...
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Learning Linear Dynamical Systems with SemiParametric Least Squares
We analyze a simple prefiltered variation of the least squares estimator...
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A SuccessiveElimination Approach to Adaptive Robotic Sensing
We study the adaptive sensing problem for the multiple source seeking pr...
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Group calibration is a byproduct of unconstrained learning
Much recent work on fairness in machine learning has focused on how well...
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Adaptive Sampling for Convex Regression
In this paper, we introduce the first principled adaptivesampling proce...
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On the Randomized Complexity of Minimizing a Convex Quadratic Function
Minimizing a convex, quadratic objective is a fundamental problem in mac...
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Tight Query Complexity Lower Bounds for PCA via Finite Sample Deformed Wigner Law
We prove a query complexity lower bound for approximating the top r dime...
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Delayed Impact of Fair Machine Learning
Fairness in machine learning has predominantly been studied in static cl...
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Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification
We prove that the ordinary leastsquares (OLS) estimator attains nearly ...
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Approximate Ranking from Pairwise Comparisons
A common problem in machine learning is to rank a set of n items based o...
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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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On the Gap Between StrictSaddles and True Convexity: An Omega(log d) Lower Bound for Eigenvector Approximation
We prove a query complexity lower bound on rankone principal component ...
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The Simulator: Understanding Adaptive Sampling in the ModerateConfidence Regime
We propose a novel technique for analyzing adaptive sampling called the ...
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BestofK Bandits
This paper studies the BestofK Bandit game: At each time the player ch...
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Gradient Descent Converges to Minimizers
We show that gradient descent converges to a local minimizer, almost sur...
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Max Simchowitz
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