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- articleJanuary 2019
Approximation hardness for a class of sparse optimization problems
In this paper, we consider three typical optimization problems with a convex loss function and a nonconvex sparse penalty or constraint. For the sparse penalized problem, we prove that finding an O(nc1dc2)-optimal solution to an n × d problem is ...
- ArticleDecember 2018
Near-optimal time and sample complexities for solving Markov decision processes with a generative model
NIPS'18: Proceedings of the 32nd International Conference on Neural Information Processing SystemsPages 5192–5202In this paper we consider the problem of computing an e-optimal policy of a discounted Markov Decision Process (DMDP) provided we can only access its transition function through a generative sampling model that given any state-action pair samples from ...
- research-articleJanuary 2018
Variance reduced value iteration and faster algorithms for solving markov decision processes
SODA '18: Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete AlgorithmsPages 770–787In this paper we provide faster algorithms for approximately solving discounted Markov Decision Processes in multiple parameter regimes. Given a discounted Markov Decision Process (DMDP) with |S| states, |A| actions, discount factor γ ∈ (0, 1), and ...
- ArticleAugust 2017
Strong NP-hardness for sparse optimization with concave penalty functions
ICML'17: Proceedings of the 34th International Conference on Machine Learning - Volume 70Pages 740–747Consider the regularized sparse minimization problem, which involves empirical sums of loss functions for n data points (each of dimension d) and a nonconvex sparsity penalty. We prove that finding an O(nc1 dc2)-optimal solution to the regularized ...
