Zobrazeno 1 - 10
of 53
pro vyhledávání: '"Ye, Lintao"'
In a conventional Federated Learning framework, client selection for training typically involves the random sampling of a subset of clients in each iteration. However, this random selection often leads to disparate performance among clients, raising
Externí odkaz:
http://arxiv.org/abs/2408.13683
Consider a linear quadratic regulator (LQR) problem being solved in a model-free manner using the policy gradient approach. If the gradient of the quadratic cost is being transmitted across a rate-limited channel, both the convergence and the rate of
Externí odkaz:
http://arxiv.org/abs/2401.01258
We study an online mixed discrete and continuous optimization problem where a decision maker interacts with an unknown environment for a number of $T$ rounds. At each round, the decision maker needs to first jointly choose a discrete and a continuous
Externí odkaz:
http://arxiv.org/abs/2309.07630
We consider the problem of maximizing a monotone nondecreasing set function under multiple constraints, where the constraints are also characterized by monotone nondecreasing set functions. We propose two greedy algorithms to solve the problem with p
Externí odkaz:
http://arxiv.org/abs/2305.04254
We consider the problem of learning the dynamics of a linear system when one has access to data generated by an auxiliary system that shares similar (but not identical) dynamics, in addition to data from the true system. We use a weighted least squar
Externí odkaz:
http://arxiv.org/abs/2302.04344
We propose an online learning algorithm that adaptively designs a decentralized linear quadratic regulator when the system model is unknown a priori and new data samples from a single system trajectory become progressively available. The algorithm us
Externí odkaz:
http://arxiv.org/abs/2210.08886
We study the problem of identifying the dynamics of a linear system when one has access to samples generated by a similar (but not identical) system, in addition to data from the true system. We use a weighted least squares approach and provide finit
Externí odkaz:
http://arxiv.org/abs/2204.05446
We develop a model-free learning algorithm for the infinite-horizon linear quadratic regulator (LQR) problem. Specifically, (risk) constraints and structured feedback are considered, in order to reduce the state deviation while allowing for a sparse
Externí odkaz:
http://arxiv.org/abs/2204.01779
We study the simultaneous actuator selection and controller design problem for linear quadratic regulation with Gaussian noise over a finite horizon of length $T$ and unknown system model. We consider both episodic and non-episodic settings of the pr
Externí odkaz:
http://arxiv.org/abs/2201.10197
We study the problem of control policy design for decentralized state-feedback linear quadratic control with a partially nested information structure, when the system model is unknown. We propose a model-based learning solution, which consists of two
Externí odkaz:
http://arxiv.org/abs/2110.07112