4 citations · 11 across the 4 of their papers we have counts for
6 papers
Improving Uncertainty Calibration of Deep Neural Networks via Truth Discovery and Geometric Optimization
Chunwei Ma, Ziyun Huang, Jiayi Xian +2
Deep Neural Networks (DNNs), despite their tremendous success in recent years, could still cast doubts on their predictions due to the intrinsic uncertainty associated with their l…
Meta-Learning with Neural Tangent Kernels
Yufan Zhou, Zhenyi Wang, Jiayi Xian +2
Model Agnostic Meta-Learning (MAML) has emerged as a standard framework for meta-learning, where a meta-model is learned with the ability of fast adapting to new tasks. However, as…
Consistent -Median: Simpler, Better and Robust
Xiangyu Guo, Janardhan Kulkarni, Shi Li +1
In this paper we introduce and study the online consistent -clustering with outliers problem, generalizing the non-outlier version of the problem studied in [Lattanzi-Vassilvits…
Graph Neural Networks with Composite Kernels
Yufan Zhou, Jiayi Xian, Changyou Chen +1
Learning on graph structured data has drawn increasing interest in recent years. Frameworks like Graph Convolutional Networks (GCNs) have demonstrated their ability to capture stru…
The Power of Recourse: Better Algorithms for Facility Location in Online and Dynamic Models
Xiangyu Guo, Janardhan Kulkarni, Shi Li +1
In this paper we study the facility location problem in the online with recourse and dynamic algorithm models. In the online with recourse model, clients arrive one by one and our…
On Approximating Degree-Bounded Network Design Problems
Xiangyu Guo, Guy Kortsarz, Bundit Laekhanukit +3
Directed Steiner Tree (DST) is a central problem in combinatorial optimization and theoretical computer science: Given a directed graph with edge costs $c \in \mathbb{R}…