102 citations · 295 across the 17 of their papers we have counts for
10 papers · 1 filter
Reinforcement Learning for Finite-Horizon Restless Multi-Armed Multi-Action Bandits
Guojun Xiong, Jian Li, Rahul Singh
We study a finite-horizon restless multi-armed bandit problem with multiple actions, dubbed R(MA)^2B. The state of each arm evolves according to a controlled Markov decision proces…
On the quantization of recurrent neural networks
Jian Li, Raziel Alvarez
Integer quantization of neural networks can be defined as the approximation of the high precision computation of the canonical neural network formulation, using reduced integer pre…
Gradient Descent Maximizes the Margin of Homogeneous Neural Networks
Kaifeng Lyu, Jian Li
In this paper, we study the implicit regularization of the gradient descent algorithm in homogeneous neural networks, including fully-connected and convolutional neural networks wi…
On Generalization Error Bounds of Noisy Gradient Methods for Non-Convex Learning
Jian Li, Xuanyuan Luo, Mingda Qiao
Generalization error (also known as the out-of-sample error) measures how well the hypothesis learned from training data generalizes to previously unseen data. Proving tight genera…
Generative Adversarial Mapping Networks
Jianbo Guo, Guangxiang Zhu, Jian Li
Generative Adversarial Networks (GANs) have shown impressive performance in generating photo-realistic images. They fit generative models by minimizing certain distance measure bet…
Practical Algorithms for Best-K Identification in Multi-Armed Bandits
Haotian Jiang, Jian Li, Mingda Qiao
In the Best- identification problem (Best--Arm), we are given stochastic bandit arms with unknown reward distributions. Our goal is to identify the arms with the larg…
SVM via Saddle Point Optimization: New Bounds and Distributed Algorithms
Yifei Jin, Lingxiao Huang, Jian Li
We study two important SVM variants: hard-margin SVM (for linearly separable cases) and -SVM (for linearly non-separable cases). We propose new algorithms from the perspective o…
Nearly Instance Optimal Sample Complexity Bounds for Top-k Arm Selection
Lijie Chen, Jian Li, Mingda Qiao
In the Best--Arm problem, we are given stochastic bandit arms, each associated with an unknown reward distribution. We are required to identify the arms with the largest…
Towards Instance Optimal Bounds for Best Arm Identification
Lijie Chen, Jian Li, Mingda Qiao
In the classical best arm identification (Best--Arm) problem, we are given stochastic bandit arms, each associated with a reward distribution with an unknown mean. We would…
On the Optimal Sample Complexity for Best Arm Identification
Lijie Chen, Jian Li
We study the best arm identification (BEST-1-ARM) problem, which is defined as follows. We are given stochastic bandit arms. The th arm has a reward distribution with…