activity
20172022
most citedMathematical Models of Overparameterized Neural Networks

26 citations · 138 across the 23 of their papers we have counts for

collaborators

31 papers

cs.LG2022

IDEA: Interpretable Dynamic Ensemble Architecture for Time Series Prediction

Mengyue Zha, Kani Chen, Tong Zhang

We enhance the accuracy and generalization of univariate time series point prediction by an explainable ensemble on the fly. We propose an Interpretable Dynamic Ensemble Architectu…

cs.LG20211 cited

When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint

Yoav Freund, Yi-An Ma, Tong Zhang

There has been a surge of works bridging MCMC sampling and optimization, with a specific focus on translating non-asymptotic convergence guarantees for optimization problems into t…

cs.LG2021

Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning

Tong Zhang

Thompson Sampling has been widely used for contextual bandit problems due to the flexibility of its modeling power. However, a general theory for this class of methods in the frequ…

cs.LG20211 cited

Feature Correlation Aggregation: on the Path to Better Graph Neural Networks

Jieming Zhou, Tong Zhang, Pengfei Fang +2

Prior to the introduction of Graph Neural Networks (GNNs), modeling and analyzing irregular data, particularly graphs, was thought to be the Achilles' heel of deep learning. The co…

cs.CL202111 cited

KECRS: Towards Knowledge-Enriched Conversational Recommendation System

Tong Zhang, Yong Liu, Peixiang Zhong +3

The chit-chat-based conversational recommendation systems (CRS) provide item recommendations to users through natural language interactions. To better understand user's intentions,…

cs.LG2021

Effective Sparsification of Neural Networks with Global Sparsity Constraint

Xiao Zhou, Weizhong Zhang, Hang Xu +1

Weight pruning is an effective technique to reduce the model size and inference time for deep neural networks in real-world deployments. However, since magnitudes and relative impo…