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Yining Wang 0001

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4 papers

1 author row

ICLR Conference 2021 Conference Paper

Optimism in Reinforcement Learning with Generalized Linear Function Approximation

Yining Wang 0001
Ruosong Wang
Simon S. Du
Akshay Krishnamurthy

We design a new provably efficient algorithm for episodic reinforcement learning with generalized linear function approximation. We analyze the algorithm under a new expressivity assumption that we call ``optimistic closure,'' which is strictly weaker than assumptions from prior analyses for the linear setting. With optimistic closure, we prove that our algorithm enjoys a regret bound of $\widetilde{O}\left(H\sqrt{d^3 T}\right)$ where $H$ is the horizon, $d$ is the dimensionality of the state-action features and $T$ is the number of episodes. This is the first statistically and computationally efficient algorithm for reinforcement learning with generalized linear functions.

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ICML Conference 2017 Conference Paper

Near-Optimal Design of Experiments via Regret Minimization

Zeyuan Allen-Zhu
Yuanzhi Li
Aarti Singh
Yining Wang 0001

We consider computationally tractable methods for the experimental design problem, where k out of n design points of dimension p are selected so that certain optimality criteria are approximately satisfied. Our algorithm finds a $(1+\epsilon)$-approximate optimal design when k is a linear function of p; in contrast, existing results require k to be super-linear in p. Our algorithm also handles all popular optimality criteria, while existing ones only handle one or two such criteria. Numerical results on synthetic and real-world design problems verify the practical effectiveness of the proposed algorithm.

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ICML Conference 2015 Conference Paper

A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data

Yining Wang 0001
Yu-Xiang Wang 0003
Aarti Singh

Subspace clustering groups data into several lowrank subspaces. In this paper, we propose a theoretical framework to analyze a popular optimization-based algorithm, Sparse Subspace Clustering (SSC), when the data dimension is compressed via some random projection algorithms. We show SSC provably succeeds if the random projection is a subspace embedding, which includes random Gaussian projection, uniform row sampling, FJLT, sketching, etc. Our analysis applies to the most general deterministic setting and is able to handle both adversarial and stochastic noise. It also results in the first algorithm for privacy-preserved subspace clustering.

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ICML Conference 2015 Conference Paper

DP-space: Bayesian Nonparametric Subspace Clustering with Small-variance Asymptotics

Yining Wang 0001
Jun Zhu 0001

Subspace clustering separates data points approximately lying on union of affine subspaces into several clusters. This paper presents a novel nonparametric Bayesian subspace clustering model that infers both the number of subspaces and the dimension of each subspace from the observed data. Though the posterior inference is hard, our model leads to a very efficient deterministic algorithm, DP-space, which retains the nonparametric ability under a small-variance asymptotic analysis. DP-space monotonically minimizes an intuitive objective with an explicit tradeoff between data fitness and model complexity. Experimental results demonstrate that DP-space outperforms various competitors in terms of clustering accuracy and at the same time it is highly efficient.

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