29 citations · 68 across the 26 of their papers we have counts for
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cs.LG2019
Quantization-Based Regularization for Autoencoders
Hanwei Wu, Markus Flierl
Autoencoders and their variations provide unsupervised models for learning low-dimensional representations for downstream tasks. Without proper regularization, autoencoder models a…
math.OC2019★ 9 cited
Reinforcement Learning for Multi-Objective and Constrained Markov Decision Processes
Ather Gattami, Qinbo Bai, Vaneet Agarwal
In this paper, we consider the problem of optimization and learning for constrained and multi-objective Markov decision processes, for both discounted rewards and expected average…
math.OC2019★ 5 cited
Reinforcement Learning of Markov Decision Processes with Peak Constraints
Ather Gattami
In this paper, we consider reinforcement learning of Markov Decision Processes (MDP) with peak constraints, where an agent chooses a policy to optimize an objective and at the same…