Search Results for author: Yizhou Sun

Found 97 papers, 39 papers with code

Differentiable Product Quantization for Learning Compact Embedding Layers

no code implementations ICML 2020 Ting Chen, Lala Li, Yizhou Sun

Embedding layers are commonly used to map discrete symbols into continuous embedding vectors that reflect their semantic meanings.


Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction

1 code implementation29 Sep 2023 Arjun Subramonian, Levent Sagun, Yizhou Sun

We further bridge GCN's preferential attachment bias with unfairness in link prediction and propose a new within-group fairness metric.

Unveiling Invariances via Neural Network Pruning

no code implementations15 Sep 2023 Derek Xu, Yizhou Sun, Wei Wang

Invariance describes transformations that do not alter data's underlying semantics.

Network Pruning

Weisfeiler and Lehman Go Measurement Modeling: Probing the Validity of the WL Test

1 code implementation11 Jul 2023 Arjun Subramonian, Adina Williams, Maximilian Nickel, Yizhou Sun, Levent Sagun

The expressive power of graph neural networks is usually measured by comparing how many pairs of graphs or nodes an architecture can possibly distinguish as non-isomorphic to those distinguishable by the $k$-dimensional Weisfeiler-Lehman ($k$-WL) test.

Generalizing Graph ODE for Learning Complex System Dynamics across Environments

no code implementations10 Jul 2023 Zijie Huang, Yizhou Sun, Wei Wang

In practice, however, we might observe multiple systems that are generated across different environments, which differ in latent exogenous factors such as temperature and gravity.

Contrastive Learning Physical Simulations

Inductive Meta-path Learning for Schema-complex Heterogeneous Information Networks

no code implementations8 Jul 2023 Shixuan Liu, Changjun Fan, Kewei Cheng, Yunfei Wang, Peng Cui, Yizhou Sun, Zhong Liu

Heterogeneous Information Networks (HINs) are information networks with multiple types of nodes and edges.

Meta-Path-based Probabilistic Soft Logic for Drug-Target Interaction Prediction

no code implementations25 Jun 2023 Shengming Zhang, Yizhou Sun

Drug-target interaction (DTI) prediction, which aims at predicting whether a drug will be bounded to a target, have received wide attention recently, with the goal to automate and accelerate the costly process of drug design.

CF-GODE: Continuous-Time Causal Inference for Multi-Agent Dynamical Systems

no code implementations20 Jun 2023 Song Jiang, Zijie Huang, Xiao Luo, Yizhou Sun

We model a multi-agent dynamical system as a graph and propose CounterFactual GraphODE (CF-GODE), a causal model that estimates continuous-time counterfactual outcomes in the presence of inter-dependencies between units.

Causal Inference

AVIS: Autonomous Visual Information Seeking with Large Language Model Agent

no code implementations13 Jun 2023 Ziniu Hu, Ahmet Iscen, Chen Sun, Kai-Wei Chang, Yizhou Sun, David A Ross, Cordelia Schmid, Alireza Fathi

Second, we use examples of user decision-making to provide our LLM-powered planner and reasoner with relevant contextual instances, enhancing their capacity to make informed decisions.

Decision Making Language Modelling +3

ProgSG: Cross-Modality Representation Learning for Programs in Electronic Design Automation

no code implementations18 May 2023 Yunsheng Bai, Atefeh Sohrabizadeh, Zongyue Qin, Ziniu Hu, Yizhou Sun, Jason Cong

In addition, these programs can be compiled and converted into a control data flow graph (CDFG), and the compiler also provides fine-grained alignment between the code tokens and the CDFG nodes.

Autonomous Driving Representation Learning

Bi-Level Attention Graph Neural Networks

1 code implementation23 Apr 2023 Roshni G. Iyer, Wei Wang, Yizhou Sun

Recent graph neural networks (GNNs) with the attention mechanism have historically been limited to small-scale homogeneous graphs (HoGs).

Graph Attention

Neural Compositional Rule Learning for Knowledge Graph Reasoning

1 code implementation7 Mar 2023 Kewei Cheng, Nesreen K. Ahmed, Yizhou Sun

NCRL detects the best compositional structure of a rule body, and breaks it into small compositions in order to infer the rule head.

Knowledge Graph Completion Systematic Generalization

Dissimilar Nodes Improve Graph Active Learning

1 code implementation5 Dec 2022 Zhicheng Ren, Yifu Yuan, Yuxin Wu, Xiaxuan Gao, Yewen Wang, Yizhou Sun

The existing Active Graph Embedding framework proposes to use centrality score, density score, and entropy score to evaluate the value of unlabeled nodes, and it has been shown to be capable of bringing some improvement to the node classification tasks of Graph Convolutional Networks.

Active Learning Graph Embedding +1

Prototypical Fine-tuning: Towards Robust Performance Under Varying Data Sizes

no code implementations24 Nov 2022 Yiqiao Jin, Xiting Wang, Yaru Hao, Yizhou Sun, Xing Xie

In this paper, we move towards combining large parametric models with non-parametric prototypical networks.

Learning Probabilities of Causation from Finite Population Data

no code implementations16 Oct 2022 Ang Li, Song Jiang, Yizhou Sun, Judea Pearl

This paper deals with the problem of learning the probabilities of causation of subpopulations given finite population data.

Unit Selection: Learning Benefit Function from Finite Population Data

no code implementations15 Oct 2022 Ang Li, Song Jiang, Yizhou Sun, Judea Pearl

In this paper, we present a machine learning framework that uses the bounds of the benefit function that are estimable from the finite population data to learn the bounds of the benefit function for each cell of characteristics.

Code Recommendation for Open Source Software Developers

1 code implementation15 Oct 2022 Yiqiao Jin, Yunsheng Bai, Yanqiao Zhu, Yizhou Sun, Wei Wang

In this paper, we formulate the novel problem of code recommendation, whose purpose is to predict the future contribution behaviors of developers given their interaction history, the semantic features of source code, and the hierarchical file structures of projects.

Graph Mining Recommendation Systems +1

Dual-Geometric Space Embedding Model for Two-View Knowledge Graphs

1 code implementation19 Sep 2022 Roshni G. Iyer, Yunsheng Bai, Wei Wang, Yizhou Sun

For works that seek to put both views of the KG together, the instance and ontology views are assumed to belong to the same geometric space, such as all nodes embedded in the same Euclidean space or non-Euclidean product space, an assumption no longer reasonable for two-view KGs where different portions of the graph exhibit different structures.

Knowledge Graphs Vocal Bursts Valence Prediction

Detecting Small Query Graphs in A Large Graph via Neural Subgraph Search

no code implementations21 Jul 2022 Yunsheng Bai, Derek Xu, Yizhou Sun, Wei Wang

In this paper, we propose NSUBS with two innovations to tackle the challenges: (1) A novel encoder-decoder neural network architecture to dynamically compute the matching information between the query and the target graphs at each search state; (2) A novel look-ahead loss function for training the policy network.

reinforcement-learning Reinforcement Learning (RL)

Schema-Guided Event Graph Completion

no code implementations6 Jun 2022 Hongwei Wang, Zixuan Zhang, Sha Li, Jiawei Han, Yizhou Sun, Hanghang Tong, Joseph P. Olive, Heng Ji

Existing link prediction or graph completion methods have difficulty dealing with event graphs because they are usually designed for a single large graph such as a social network or a knowledge graph, rather than multiple small dynamic event graphs.

Link Prediction

Improving Multi-Task Generalization via Regularizing Spurious Correlation

no code implementations19 May 2022 Ziniu Hu, Zhe Zhao, Xinyang Yi, Tiansheng Yao, Lichan Hong, Yizhou Sun, Ed H. Chi

First, the risk of having non-causal knowledge is higher, as the shared MTL model needs to encode all knowledge from different tasks, and causal knowledge for one task could be potentially spurious to the other.

Multi-Task Learning Representation Learning

Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment

1 code implementation ACL 2022 Zijie Huang, Zheng Li, Haoming Jiang, Tianyu Cao, Hanqing Lu, Bing Yin, Karthik Subbian, Yizhou Sun, Wei Wang

In this paper, we explore multilingual KG completion, which leverages limited seed alignment as a bridge, to embrace the collective knowledge from multiple languages.

Knowledge Graph Completion

Question-Answer Sentence Graph for Joint Modeling Answer Selection

no code implementations16 Feb 2022 Roshni G. Iyer, Thuy Vu, Alessandro Moschitti, Yizhou Sun

This research studies graph-based approaches for Answer Sentence Selection (AS2), an essential component for retrieval-based Question Answering (QA) systems.

Answer Selection Retrieval

Neural Capacitance: A New Perspective of Neural Network Selection via Edge Dynamics

no code implementations11 Jan 2022 Chunheng Jiang, Tejaswini Pedapati, Pin-Yu Chen, Yizhou Sun, Jianxi Gao

To this end, we construct a network mapping $\phi$, converting a neural network $G_A$ to a directed line graph $G_B$ that is defined on those edges in $G_A$.

Model Selection

Enabling Automated FPGA Accelerator Optimization Using Graph Neural Networks

no code implementations17 Nov 2021 Atefeh Sohrabizadeh, Yunsheng Bai, Yizhou Sun, Jason Cong

High-level synthesis (HLS) has freed the computer architects from developing their designs in a very low-level language and needing to exactly specify how the data should be transferred in register-level.

Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation

1 code implementation ICLR 2022 Shichang Zhang, Yozen Liu, Yizhou Sun, Neil Shah

Conversely, multi-layer perceptrons (MLPs) have no graph dependency and infer much faster than GNNs, even though they are less accurate than GNNs for node classification in general.

Knowledge Distillation Node Classification +2

Deep Learning of Potential Outcomes

no code implementations9 Oct 2021 Bernard Koch, Tim Sainburg, Pablo Geraldo, Song Jiang, Yizhou Sun, Jacob Gates Foster

This review systematizes the emerging literature for causal inference using deep neural networks under the potential outcomes framework.

Causal Inference

Towards Fine-Grained Reasoning for Fake News Detection

1 code implementation13 Sep 2021 Yiqiao Jin, Xiting Wang, Ruichao Yang, Yizhou Sun, Wei Wang, Hao Liao, Xing Xie

The detection of fake news often requires sophisticated reasoning skills, such as logically combining information by considering word-level subtle clues.

Fake News Detection

Coupled Graph ODE for Learning Interacting System Dynamics

1 code implementation ACM SIGKDD Conference on Knowledge Discovery & Data Mining 2021 Zijie Huang, Yizhou Sun, Wei Wang

On one hand, features of objects change over time, influenced by the linked objects in the interaction graph.

Fuzzy Logic Based Logical Query Answering on Knowledge Graphs

no code implementations5 Aug 2021 Xuelu Chen, Ziniu Hu, Yizhou Sun

Answering complex First-Order Logical (FOL) queries on large-scale incomplete knowledge graphs (KGs) is an important yet challenging task.

Knowledge Graphs Link Prediction

Clinical Named Entity Recognition using Contextualized Token Representations

no code implementations23 Jun 2021 Yichao Zhou, Chelsea Ju, J. Harry Caufield, Kevin Shih, Calvin Chen, Yizhou Sun, Kai-Wei Chang, Peipei Ping, Wei Wang

To facilitate various downstream applications using clinical case reports (CCRs), we pre-train two deep contextualized language models, Clinical Embeddings from Language Model (C-ELMo) and Clinical Contextual String Embeddings (C-Flair) using the clinical-related corpus from the PubMed Central.

Language Modelling named-entity-recognition +3

Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts

1 code implementation15 Mar 2021 Junheng Hao, Muhao Chen, Wenchao Yu, Yizhou Sun, Wei Wang

The cross-view association model is learned to bridge the embeddings of ontological concepts and their corresponding instance-view entities.

Entity Typing Knowledge Graphs +1

Bio-JOIE: Joint Representation Learning of Biological Knowledge Bases

1 code implementation7 Mar 2021 Junheng Hao, Chelsea Ju, Muhao Chen, Yizhou Sun, Carlo Zaniolo, Wei Wang

Leveraging a wide-range of biological knowledge, such as gene ontology and protein-protein interaction (PPI) networks from other closely related species presents a vital approach to infer the molecular impact of a new species.

Representation Learning Type prediction

CREATe: Clinical Report Extraction and Annotation Technology

no code implementations28 Feb 2021 Yichao Zhou, Wei-Ting Chen, BoWen Zhang, David Lee, J. Harry Caufield, Kai-Wei Chang, Yizhou Sun, Peipei Ping, Wei Wang

Clinical case reports are written descriptions of the unique aspects of a particular clinical case, playing an essential role in sharing clinical experiences about atypical disease phenotypes and new therapies.

Decoupled Greedy Learning of Graph Neural Networks

no code implementations1 Jan 2021 Yewen Wang, Jian Tang, Yizhou Sun, Guy Wolf

We empirically analyse our proposed DGL-GNN model, and demonstrate its effectiveness and superior efficiency through a range of experiments.

Learning to Search for Fast Maximum Common Subgraph Detection

no code implementations1 Jan 2021 Yunsheng Bai, Derek Qiang Xu, Yizhou Sun, Wei Wang

Detecting the Maximum Common Subgraph (MCS) between two input graphs is fundamental for applications in biomedical analysis, malware detection, cloud computing, etc.

Cloud Computing Graph Matching +2

Motif-Driven Contrastive Learning of Graph Representations

no code implementations23 Dec 2020 Shichang Zhang, Ziniu Hu, Arjun Subramonian, Yizhou Sun

Our framework MotIf-driven Contrastive leaRning Of Graph representations (MICRO-Graph) can: 1) use GNNs to extract motifs from large graph datasets; 2) leverage learned motifs to sample informative subgraphs for contrastive learning of GNN.

Clustering Contrastive Learning +1

Leveraging Meta-path Contexts for Classification in Heterogeneous Information Networks

no code implementations18 Dec 2020 Xiang Li, Danhao Ding, Ben Kao, Yizhou Sun, Nikos Mamoulis

A heterogeneous information network (HIN) has as vertices objects of different types and as edges the relations between objects, which are also of various types.

Classification General Classification +2

Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs

no code implementations12 Dec 2020 Jiarong Xu, Yizhou Sun, Xin Jiang, Yanhao Wang, Yang Yang, Chunping Wang, Jiangang Lu

To bridge the gap between theoretical graph attacks and real-world scenarios, in this work, we propose a novel and more realistic setting: strict black-box graph attack, in which the attacker has no knowledge about the victim model at all and is not allowed to send any queries.

Adversarial Attack Graph Classification +1

Unsupervised Adversarially-Robust Representation Learning on Graphs

no code implementations4 Dec 2020 Jiarong Xu, Yang Yang, Junru Chen, Chunping Wang, Xin Jiang, Jiangang Lu, Yizhou Sun

Additionally, we explore a provable connection between the robustness of the unsupervised graph encoder and that of models on downstream tasks.

Adversarial Robustness Community Detection +4

Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations

1 code implementation NeurIPS 2020 Zijie Huang, Yizhou Sun, Wei Wang

In this paper, we propose to learn system dynamics from irregularly-sampled partial observations with underlying graph structure for the first time.

Multivariate Time Series Classification with Hierarchical Variational Graph Pooling

no code implementations12 Oct 2020 Ziheng Duan, Haoyan Xu, Yueyang Wang, Yida Huang, Anni Ren, Zhongbin Xu, Yizhou Sun, Wei Wang

Then we combine GNNs and our proposed variational graph pooling layers for joint graph representation learning and graph coarsening, after which the graph is progressively coarsened to one node.

General Classification Graph Classification +5

Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer

1 code implementation Findings of the Association for Computational Linguistics 2020 Xuelu Chen, Muhao Chen, Changjun Fan, Ankith Uppunda, Yizhou Sun, Carlo Zaniolo

Predicting missing facts in a knowledge graph (KG) is a crucial task in knowledge base construction and reasoning, and it has been the subject of much research in recent works using KG embeddings.

Knowledge Graph Completion Self-Learning +1

GPT-GNN: Generative Pre-Training of Graph Neural Networks

3 code implementations27 Jun 2020 Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, Yizhou Sun

Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-structured data.

Graph Generation

Bi-Level Graph Neural Networks for Drug-Drug Interaction Prediction

no code implementations11 Jun 2020 Yunsheng Bai, Ken Gu, Yizhou Sun, Wei Wang

We introduce Bi-GNN for modeling biological link prediction tasks such as drug-drug interaction (DDI) and protein-protein interaction (PPI).

Link Prediction

TIMME: Twitter Ideology-detection via Multi-task Multi-relational Embedding

1 code implementation2 Jun 2020 Zhiping Xiao, Weiping Song, Haoyan Xu, Zhicheng Ren, Yizhou Sun

However, the incompleteness of the labels and the features in social network datasets is tricky, not to mention the enormous data size and the heterogeneousity.

Software Language Comprehension using a Program-Derived Semantics Graph

no code implementations NeurIPS Workshop CAP 2020 Roshni G. Iyer, Yizhou Sun, Wei Wang, Justin Gottschlich

To continue to advance this research, we present the program-derived semantics graph, a new graphical structure to capture semantics of code.

Heterogeneous Network Representation Learning: A Unified Framework with Survey and Benchmark

1 code implementation1 Apr 2020 Carl Yang, Yuxin Xiao, Yu Zhang, Yizhou Sun, Jiawei Han

Since there has already been a broad body of HNE algorithms, as the first contribution of this work, we provide a generic paradigm for the systematic categorization and analysis over the merits of various existing HNE algorithms.

Network Embedding

Heterogeneous Graph Transformer

4 code implementations3 Mar 2020 Ziniu Hu, Yuxiao Dong, Kuansan Wang, Yizhou Sun

Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data.

Graph Sampling Node Property Prediction

GLSearch: Maximum Common Subgraph Detection via Learning to Search

no code implementations8 Feb 2020 Yunsheng Bai, Derek Xu, Yizhou Sun, Wei Wang

However, MCS computation is NP-hard, and state-of-the-art MCS solvers rely on heuristic search algorithms which in practice cannot find good solution for large graph pairs given a limited computation budget.

Cloud Computing Graph Embedding +4

Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks

1 code implementation NeurIPS 2019 Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, Quanquan Gu

Original full-batch GCN training requires calculating the representation of all the nodes in the graph per GCN layer, which brings in high computation and memory costs.

Node Classification

Demystifying Graph Neural Network Via Graph Filter Assessment

no code implementations25 Sep 2019 Yewen Wang, Ziniu Hu, Yusong Ye, Yizhou Sun

However, there still lacks in-depth analysis on (1) Whether there exists a best filter that can perform best on all graph data; (2) Which graph properties will influence the optimal choice of graph filter; (3) How to design appropriate filter adaptive to the graph data.

Learning to Transfer via Modelling Multi-level Task Dependency

no code implementations25 Sep 2019 Haonan Wang, Zhenbang Wu, Ziniu Hu, Yizhou Sun

Besides, the understanding of relationships among tasks has been ignored by most of the current methods.

Multi-Task Learning

Learning Compact Embedding Layers via Differentiable Product Quantization

no code implementations25 Sep 2019 Ting Chen, Lala Li, Yizhou Sun

Embedding layers are commonly used to map discrete symbols into continuous embedding vectors that reflect their semantic meanings.



no code implementations25 Sep 2019 Kewei Cheng, Yikai Zhu, Ming Zhang, Yizhou Sun

Knowledge graph has gained increasing attention in recent years for its successful applications of numerous tasks.

Knowledge Graph Embedding

Neural Maximum Common Subgraph Detection with Guided Subgraph Extraction

no code implementations25 Sep 2019 Yunsheng Bai, Derek Xu, Ken Gu, Xueqing Wu, Agustin Marinovic, Christopher Ro, Yizhou Sun, Wei Wang

Maximum Common Subgraph (MCS) is defined as the largest subgraph that is commonly present in both graphs of a graph pair.

Chordal-GCN: Exploiting sparsity in training large-scale graph convolutional networks

no code implementations25 Sep 2019 Xin Jiang*, Kewei Cheng*, Song Jiang*, Yizhou Sun

Despite the impressive success of graph convolutional networks (GCNs) on numerous applications, training on large-scale sparse networks remains challenging.

Node Classification

Differentiable Product Quantization for End-to-End Embedding Compression

1 code implementation26 Aug 2019 Ting Chen, Lala Li, Yizhou Sun

Embedding layers are commonly used to map discrete symbols into continuous embedding vectors that reflect their semantic meanings.


Few-Shot Representation Learning for Out-Of-Vocabulary Words

1 code implementation ACL 2019 Ziniu Hu, Ting Chen, Kai-Wei Chang, Yizhou Sun

Existing approaches for learning word embeddings often assume there are sufficient occurrences for each word in the corpus, such that the representation of words can be accurately estimated from their contexts.

Learning Word Embeddings Meta-Learning +1

Pre-Training Graph Neural Networks for Generic Structural Feature Extraction

no code implementations31 May 2019 Ziniu Hu, Changjun Fan, Ting Chen, Kai-Wei Chang, Yizhou Sun

With the proposed pre-training procedure, the generic structural information is learned and preserved, thus the pre-trained GNN requires less amount of labeled data and fewer domain-specific features to achieve high performance on different downstream tasks.


Learning to Identify High Betweenness Centrality Nodes from Scratch: A Novel Graph Neural Network Approach

1 code implementation24 May 2019 Changjun Fan, Li Zeng, Yuhui Ding, Muhao Chen, Yizhou Sun, Zhong Liu

By training on small-scale networks, the learned model is capable of assigning relative BC scores to nodes for any unseen networks, and thus identifying the highly-ranked nodes.

Community Detection

Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification

1 code implementation11 May 2019 Ting Chen, Song Bian, Yizhou Sun

In this work, we propose a dissection of GNNs on graph classification into two parts: 1) the graph filtering, where graph-based neighbor aggregations are performed, and 2) the set function, where a set of hidden node features are composed for prediction.

General Classification Graph Classification

Learning Fair Representations via an Adversarial Framework

1 code implementation30 Apr 2019 Rui Feng, Yang Yang, Yuehan Lyu, Chenhao Tan, Yizhou Sun, Chunping Wang

Fairness has become a central issue for our research community as classification algorithms are adopted in societally critical domains such as recidivism prediction and loan approval.

Classification Fairness +1

Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity

1 code implementation1 Apr 2019 Yunsheng Bai, Hao Ding, Yang Qiao, Agustin Marinovic, Ken Gu, Ting Chen, Yizhou Sun, Wei Wang

We introduce a novel approach to graph-level representation learning, which is to embed an entire graph into a vector space where the embeddings of two graphs preserve their graph-graph proximity.

General Classification Graph Classification +3

Embedding Uncertain Knowledge Graphs

1 code implementation26 Nov 2018 Xuelu Chen, Muhao Chen, Weijia Shi, Yizhou Sun, Carlo Zaniolo

However, there are many KGs that model uncertain knowledge, which typically model the inherent uncertainty of relations facts with a confidence score, and embedding such uncertain knowledge represents an unresolved challenge.

Binary Classification General Classification +2

Convolutional Set Matching for Graph Similarity

1 code implementation23 Oct 2018 Yunsheng Bai, Hao Ding, Yizhou Sun, Wei Wang

We introduce GSimCNN (Graph Similarity Computation via Convolutional Neural Networks) for predicting the similarity score between two graphs.

Graph Similarity set matching

Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching

1 code implementation10 Sep 2018 Yunsheng Bai, Hao Ding, Yizhou Sun, Wei Wang

Since computing the exact distance/similarity between two graphs is typically NP-hard, a series of approximate methods have been proposed with a trade-off between accuracy and speed.

Clustering Combinatorial Optimization +5

The Art of Drafting: A Team-Oriented Hero Recommendation System for Multiplayer Online Battle Arena Games

no code implementations26 Jun 2018 Zhengxing Chen, Truong-Huy D Nguyen, Yuyu Xu, Chris Amato, Seth Cooper, Yizhou Sun, Magy Seif El-Nasr

The selection of heroes, also known as pick or draft, takes place before the match starts and alternates between the two teams until each player has selected one hero.

Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations

1 code implementation ICML 2018 Ting Chen, Martin Renqiang Min, Yizhou Sun

Conventional embedding methods directly associate each symbol with a continuous embedding vector, which is equivalent to applying a linear transformation based on a "one-hot" encoding of the discrete symbols.

HeteroMed: Heterogeneous Information Network for Medical Diagnosis

no code implementations22 Apr 2018 Anahita Hosseini, Ting Chen, Wenjun Wu, Yizhou Sun, Majid Sarrafzadeh

To the best of our knowledge, this is the first study to use Heterogeneous Information Network for modeling clinical data and disease diagnosis.

Medical Diagnosis

Learning K-way D-dimensional Discrete Code For Compact Embedding Representations

no code implementations8 Nov 2017 Ting Chen, Martin Renqiang Min, Yizhou Sun

Conventional embedding methods directly associate each symbol with a continuous embedding vector, which is equivalent to applying linear transformation based on "one-hot" encoding of the discrete symbols.

Language Modelling

On Sampling Strategies for Neural Network-based Collaborative Filtering

no code implementations23 Jun 2017 Ting Chen, Yizhou Sun, Yue Shi, Liangjie Hong

In this paper, we propose a general neural network-based recommendation framework, which subsumes several existing state-of-the-art recommendation algorithms, and address the efficiency issue by investigating sampling strategies in the stochastic gradient descent training for the framework.

Collaborative Filtering

Joint Text Embedding for Personalized Content-based Recommendation

no code implementations4 Jun 2017 Ting Chen, Liangjie Hong, Yue Shi, Yizhou Sun

While latent factors of items can be learned effectively from user interaction data, in many cases, such data is not available, especially for newly emerged items.

News Recommendation Recommendation Systems

Ideology Detection for Twitter Users with Heterogeneous Types of Links

no code implementations24 Dec 2016 Yupeng Gu, Ting Chen, Yizhou Sun, Bingyu Wang

The problem of ideology detection is to study the latent (political) placement for people, which is traditionally studied on politicians according to their voting behaviors.

Social and Information Networks

Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author Identification

1 code implementation8 Dec 2016 Ting Chen, Yizhou Sun

To address the challenges, we propose a task-guided and path-augmented heterogeneous network embedding model.

Feature Engineering Network Embedding

Entity Embedding-based Anomaly Detection for Heterogeneous Categorical Events

no code implementations26 Aug 2016 Ting Chen, Lu-An Tang, Yizhou Sun, Zhengzhang Chen, Kai Zhang

Anomaly detection plays an important role in modern data-driven security applications, such as detecting suspicious access to a socket from a process.

Anomaly Detection

Graph-based Consensus Maximization among Multiple Supervised and Unsupervised Models

no code implementations NeurIPS 2009 Jing Gao, Feng Liang, Wei Fan, Yizhou Sun, Jiawei Han

First, we can boost the diversity of classification ensemble by incorporating multiple clustering outputs, each of which provides grouping constraints for the joint label predictions of a set of related objects.

Clustering General Classification

Cannot find the paper you are looking for? You can Submit a new open access paper.