activity
20122023
most citedMultiple Kernel Learning from Noisy Labels by Stochastic Programming

13 citations · 20 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG20231 cited

Efficient Algorithms for Generalized Linear Bandits with Heavy-tailed Rewards

Bo Xue, Yimu Wang, Yuanyu Wan +2

This paper investigates the problem of generalized linear bandits with heavy-tailed rewards, whose -th moment is bounded for some . Although there exist methods…

cs.CV2023

AANet: Aggregation and Alignment Network with Semi-hard Positive Sample Mining for Hierarchical Place Recognition

Feng Lu, Lijun Zhang, Shuting Dong +2

Visual place recognition (VPR) is one of the research hotspots in robotics, which uses visual information to locate robots. Recently, the hierarchical two-stage VPR methods have be…

cs.LG20232 cited

Non-stationary Projection-free Online Learning with Dynamic and Adaptive Regret Guarantees

Yibo Wang, Wenhao Yang, Wei Jiang +5

Projection-free online learning has drawn increasing interest due to its efficiency in solving high-dimensional problems with complicated constraints. However, most existing projec…

cs.LG20231 cited

Structured Pruning for Multi-Task Deep Neural Networks

Siddhant Garg, Lijun Zhang, Hui Guan

Although multi-task deep neural network (DNN) models have computation and storage benefits over individual single-task DNN models, they can be further optimized via model compressi…

cs.LG20192 cited

SAdam: A Variant of Adam for Strongly Convex Functions

Guanghui Wang, Shiyin Lu, Weiwei Tu +1

The Adam algorithm has become extremely popular for large-scale machine learning. Under convexity condition, it has been proved to enjoy a data-dependant regret bound…

cs.LG20161 cited

Efficient Non-oblivious Randomized Reduction for Risk Minimization with Improved Excess Risk Guarantee

Yi Xu, Haiqin Yang, Lijun Zhang +1

In this paper, we address learning problems for high dimensional data. Previously, oblivious random projection based approaches that project high dimensional features onto a random…