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Found 11 papers, 1 papers with code
Low-Rank Bandits via Tight Two-to-Infinity Singular Subspace Recovery
For policy evaluation and best policy identification, we show that our algorithms are nearly minimax optimal.
Constrained Deep Reinforcement Learning for Fronthaul Compression Optimization
In the Centralized-Radio Access Network (C-RAN) architecture, functions can be placed in the central or distributed locations.
A Tutorial on the Non-Asymptotic Theory of System Identification
This tutorial serves as an introduction to recently developed non-asymptotic methods in the theory of -- mainly linear -- system identification.
Exploiting Observation Bias to Improve Matrix Completion
Instead of treating this observation bias as a disadvantage, as is typically the case, the goal is to exploit the shared information between the bias and the outcome of interest to improve predictions.
Nearly Optimal Latent State Decoding in Block MDPs
We investigate the problems of model estimation and reward-free learning in episodic Block MDPs.
Best Policy Identification in Linear MDPs
We investigate the problem of best policy identification in discounted linear Markov Decision Processes in the fixed confidence setting under a generative model.
Learning Optimal Antenna Tilt Control Policies: A Contextual Linear Bandit Approach
In this paper, we devise algorithms learning optimal tilt control policies from existing data (in the so-called passive learning setting) or from data actively generated by the algorithms (the active learning setting).
Minimal Expected Regret in Linear Quadratic Control
Quantifying the impact of such a constantly-varying control policy on the performance of these estimates and on the regret constitutes one of the technical challenges tackled in this paper.
Optimal Best-arm Identification in Linear Bandits
We study the problem of best-arm identification with fixed confidence in stochastic linear bandits.
Finite-time Identification of Stable Linear Systems: Optimality of the Least-Squares Estimator
We present a new finite-time analysis of the estimation error of the Ordinary Least Squares (OLS) estimator for stable linear time-invariant systems.
Sample Complexity Lower Bounds for Linear System Identification
For controlled systems, our lower bounds are not as explicit as in the case of uncontrolled systems, but could well provide interesting insights into the design of control policy with minimal sample complexity.
