23 citations · 71 across the 20 of their papers we have counts for
7 papers · 1 filter
Learning to Solve Combinatorial Optimization under Positive Linear Constraints via Non-Autoregressive Neural Networks
Runzhong Wang, Yang Li, Junchi Yan +1
Combinatorial optimization (CO) is the fundamental problem at the intersection of computer science, applied mathematics, etc. The inherent hardness in CO problems brings up challen…
Poisson Process for Bayesian Optimization
Xiaoxing Wang, Jiaxing Li, Chao Xue +5
BayesianOptimization(BO) is a sample-efficient black-box optimizer, and extensive methods have been proposed to build the absolute function response of the black-box function throu…
EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning
Chao Chen, Haoyu Geng, Nianzu Yang +2
Dynamic graphs arise in various real-world applications, and it is often welcomed to model the dynamics directly in continuous time domain for its flexibility. This paper aims to d…
Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution
Chao Chen, Haoyu Geng, Gang Zeng +4
Inductive one-bit matrix completion is motivated by modern applications such as recommender systems, where new users would appear at test stage with the ratings consisting of only…
A K-variate Time Series Is Worth K Words: Evolution of the Vanilla Transformer Architecture for Long-term Multivariate Time Series Forecasting
Zanwei Zhou, Ruizhe Zhong, Chen Yang +3
Multivariate time series forecasting (MTSF) is a fundamental problem in numerous real-world applications. Recently, Transformer has become the de facto solution for MTSF, especiall…
DOTIN: Dropping Task-Irrelevant Nodes for GNNs
Shaofeng Zhang, Feng Zhu, Junchi Yan +2
Scalability is an important consideration for deep graph neural networks. Inspired by the conventional pooling layers in CNNs, many recent graph learning approaches have introduced…
ROME: Robustifying Memory-Efficient NAS via Topology Disentanglement and Gradient Accumulation
Xiaoxing Wang, Xiangxiang Chu, Yuda Fan +4
Albeit being a prevalent architecture searching approach, differentiable architecture search (DARTS) is largely hindered by its substantial memory cost since the entire supernet re…