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We study the problem of least squares linear regression where the data-points are dependent and are sampled from a Markov chain.
Mixing properties of ARMA processes
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Analysis of temporal-diffference learning with function approximation
John N Tsitsiklis and Benjamin Van Roy · 1997
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Aad W Van der Vaart · 2000
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Stochastic Approximation and Recursive Algorithms and Applications
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Ergodic mirror descent
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An introductory study on time series modeling and forecasting
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
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Parallelizing stochastic gradient descent for least squares regression: mini-batching, averaging, and model misspecification
Prateek Jain, Sham Kakade, Rahul Kidambi, Praneeth Netrapalli, and Aaron Sidford · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Finite-time error bounds for linear stochastic approximation and TD learning
R Srikant and Lei Ying · 2019
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Regression from dependent observations
Constantinos Daskalakis, Nishanth Dikkala, and Ioannis Panageas · 2019
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Learning from weakly dependent data under dobrushin’s condition
Yuval Dagan, Constantinos Daskalakis, Nishanth Dikkala, and Siddhartha Jayanti · 2019
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Exact minimax risk for linear least squares, and the lower tail of sample covariance matrices
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Markov chains and mixing times
David A Levin and Yuval Peres · 2017
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A finite time analysis of temporal difference learning with linear function approximation
Jalaj Bhandari, Daniel Russo, and Raghav Singal · 2018
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Jaouad Mourtada · 2019
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