Search Results for author: Chaoqi Yang

Found 13 papers, 5 papers with code

GOCPT: Generalized Online Canonical Polyadic Tensor Factorization and Completion

1 code implementation8 May 2022 Chaoqi Yang, Cheng Qian, Jimeng Sun

Our variant GOCPTE shows up to 1:2% and 5:5% fitness improvement on two datasets with about 20% speedup compared to the best model.

Self-supervised EEG Representation Learning for Automatic Sleep Staging

1 code implementation27 Oct 2021 Chaoqi Yang, Danica Xiao, M. Brandon Westover, Jimeng Sun

Objective: In this paper, we aim to learn robust vector representations from massive unlabeled Electroencephalogram (EEG) signals, such that the learned representations (1) are expressive enough to replace the raw signals in the sleep staging task; and (2) provide better predictive performance than supervised models in scenarios of fewer labels and noisy samples.

EEG Representation Learning +2

Augmented Tensor Decomposition with Stochastic Optimization

no code implementations15 Jun 2021 Chaoqi Yang, Cheng Qian, Navjot Singh, Cao Xiao, M Brandon Westover, Edgar Solomonik, Jimeng Sun

Tensor decompositions are powerful tools for dimensionality reduction and feature interpretation of multidimensional data such as signals.

Data Augmentation Dimensionality Reduction +2

MTC: Multiresolution Tensor Completion from Partial and Coarse Observations

1 code implementation14 Jun 2021 Chaoqi Yang, Navjot Singh, Cao Xiao, Cheng Qian, Edgar Solomonik, Jimeng Sun

Our MTC model explores tensor mode properties and leverages the hierarchy of resolutions to recursively initialize an optimization setup, and optimizes on the coupled system using alternating least squares.

SafeDrug: Dual Molecular Graph Encoders for Recommending Effective and Safe Drug Combinations

no code implementations5 May 2021 Chaoqi Yang, Cao Xiao, Fenglong Ma, Lucas Glass, Jimeng Sun

On a benchmark dataset, our SafeDrug is relatively shown to reduce DDI by 19. 43% and improves 2. 88% on Jaccard similarity between recommended and actually prescribed drug combinations over previous approaches.

Change Matters: Medication Change Prediction with Recurrent Residual Networks

no code implementations5 May 2021 Chaoqi Yang, Cao Xiao, Lucas Glass, Jimeng Sun

Deep learning is revolutionizing predictive healthcare, including recommending medications to patients with complex health conditions.

Hypergraph Learning with Line Expansion

no code implementations11 May 2020 Chaoqi Yang, Ruijie Wang, Shuochao Yao, Tarek Abdelzaher

Previous hypergraph expansions are solely carried out on either vertex level or hyperedge level, thereby missing the symmetric nature of data co-occurrence, and resulting in information loss.

Graph Learning

Analyzing the Design Space of Re-opening Policies and COVID-19 Outcomes in the US

1 code implementation30 Apr 2020 Chaoqi Yang, Ruijie Wang, Fangwei Gao, Dachun Sun, Jiawei Tang, Tarek Abdelzaher

We further compare policies that rely on partial venue closure to policies that espouse wide-spread periodic testing instead (i. e., in lieu of social distancing).

Physics and Society Computers and Society Social and Information Networks

Revisiting Over-smoothing in Deep GCNs

no code implementations30 Mar 2020 Chaoqi Yang, Ruijie Wang, Shuochao Yao, Shengzhong Liu, Tarek Abdelzaher

Oversmoothing has been assumed to be the major cause of performance drop in deep graph convolutional networks (GCNs).

Node Classification

MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding

no code implementations18 Feb 2020 Haolin Zhou, Chaoqi Yang, Xiaofeng Gao, Qiong Chen, Gongshen Liu, Guihai Chen

Online Real-Time Bidding (RTB) is a complex auction game among which advertisers struggle to bid for ad impressions when a user request occurs.


Disentangling Overlapping Beliefs by Structured Matrix Factorization

no code implementations13 Feb 2020 Chaoqi Yang, Jinyang Li, Ruijie Wang, Shuochao Yao, Huajie Shao, Dongxin Liu, Shengzhong Liu, Tianshi Wang, Tarek F. Abdelzaher

This paper develops a new class of Non-negative Matrix Factorization (NMF) algorithms that allow identification of both agreement and disagreement points when beliefs of different communities partially overlap.

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