321 citations · 1.8k across the 66 of their papers we have counts for
11 papers · 1 filter
DAAS: Differentiable Architecture and Augmentation Policy Search
Xiaoxing Wang, Xiangxiang Chu, Junchi Yan +1
Neural architecture search (NAS) has been an active direction of automatic machine learning (Auto-ML), aiming to explore efficient network structures. The searched architecture is…
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs
Runzhong Wang, Zhigang Hua, Gan Liu +6
Combinatorial Optimization (CO) has been a long-standing challenging research topic featured by its NP-hard nature. Traditionally such problems are approximately solved with heuris…
Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting
Longyuan Li, Junchi Yan, Xiaokang Yang +1
Probabilistic time series forecasting involves estimating the distribution of future based on its history, which is essential for risk management in downstream decision-making. We…
Combinatorial Learning of Graph Edit Distance via Dynamic Embedding
Runzhong Wang, Tianqi Zhang, Tianshu Yu +2
Graph Edit Distance (GED) is a popular similarity measurement for pairwise graphs and it also refers to the recovery of the edit path from the source graph to the target graph. Tra…
Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching
Runzhong Wang, Junchi Yan, Xiaokang Yang
Graph matching involves combinatorial optimization based on edge-to-edge affinity matrix, which can be generally formulated as Lawler's Quadratic Assignment Problem (QAP). This pap…
Reinforcement Learning with Policy Mixture Model for Temporal Point Processes Clustering
Weichang Wu, Junchi Yan, Xiaokang Yang +1
Temporal point process is an expressive tool for modeling event sequences over time. In this paper, we take a reinforcement learning view whereby the observed sequences are assumed…
Decoupled Learning for Factorial Marked Temporal Point Processes
Weichang Wu, Junchi Yan, Xiaokang Yang +1
This paper introduces the factorial marked temporal point process model and presents efficient learning methods. In conventional (multi-dimensional) marked temporal point process m…
Image Matching via Loopy RNN
Donghao Luo, Bingbing Ni, Yichao Yan +1
Most existing matching algorithms are one-off algorithms, i.e., they usually measure the distance between the two image feature representation vectors for only one time. In contras…
Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks
Shuai Xiao, Junchi Yan, Stephen M. Chu +2
Event sequence, asynchronously generated with random timestamp, is ubiquitous among applications. The precise and arbitrary timestamp can carry important clues about the underlying…
Wasserstein Learning of Deep Generative Point Process Models
Shuai Xiao, Mehrdad Farajtabar, Xiaojing Ye +3
Point processes are becoming very popular in modeling asynchronous sequential data due to their sound mathematical foundation and strength in modeling a variety of real-world pheno…
Joint Modeling of Event Sequence and Time Series with Attentional Twin Recurrent Neural Networks
Shuai Xiao, Junchi Yan, Mehrdad Farajtabar +3
A variety of real-world processes (over networks) produce sequences of data whose complex temporal dynamics need to be studied. More especially, the event timestamps can carry impo…