Search Results for author: Xia Hu

Found 168 papers, 62 papers with code

LoRA-as-an-Attack! Piercing LLM Safety Under The Share-and-Play Scenario

no code implementations29 Feb 2024 Hongyi Liu, Zirui Liu, Ruixiang Tang, Jiayi Yuan, Shaochen Zhong, Yu-Neng Chuang, Li Li, Rui Chen, Xia Hu

Our aim is to raise awareness of the potential risks under the emerging share-and-play scenario, so as to proactively prevent potential consequences caused by LoRA-as-an-Attack.

Learning to Compress Prompt in Natural Language Formats

no code implementations28 Feb 2024 Yu-Neng Chuang, Tianwei Xing, Chia-Yuan Chang, Zirui Liu, Xun Chen, Xia Hu

In this work, we propose a Natural Language Prompt Encapsulation (Nano-Capsulator) framework compressing original prompts into NL formatted Capsule Prompt while maintaining the prompt utility and transferability.

Large Language Models As Faithful Explainers

no code implementations7 Feb 2024 Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang, Ruixiang Tang, Fan Yang, Mengnan Du, Xuanting Cai, Xia Hu

In this work, we introduce a generative explanation framework, xLLM, to improve the faithfulness of the explanations provided in natural language formats for LLMs.

Decision Making

LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning

2 code implementations2 Jan 2024 Hongye Jin, Xiaotian Han, Jingfeng Yang, Zhimeng Jiang, Zirui Liu, Chia-Yuan Chang, Huiyuan Chen, Xia Hu

To achieve this goal, we propose SelfExtend to extend the context window of LLMs by constructing bi-level attention information: the grouped attention and the neighbor attention.

Towards Mitigating Dimensional Collapse of Representations in Collaborative Filtering

no code implementations29 Dec 2023 Huiyuan Chen, Vivian Lai, Hongye Jin, Zhimeng Jiang, Mahashweta Das, Xia Hu

Here we propose a non-contrastive learning objective, named nCL, which explicitly mitigates dimensional collapse of representations in collaborative filtering.

Collaborative Filtering Contrastive Learning +1

LETA: Learning Transferable Attribution for Generic Vision Explainer

no code implementations23 Dec 2023 Guanchu Wang, Yu-Neng Chuang, Fan Yang, Mengnan Du, Chia-Yuan Chang, Shaochen Zhong, Zirui Liu, Zhaozhuo Xu, Kaixiong Zhou, Xuanting Cai, Xia Hu

To address this problem, we develop a pre-trained, DNN-based, generic explainer on large-scale image datasets, and leverage its transferability to explain various vision models for downstream tasks.

Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution

1 code implementation19 Nov 2023 Yuting Sun, Guansong Pang, Guanhua Ye, Tong Chen, Xia Hu, Hongzhi Yin

The ongoing challenges in time series anomaly detection (TSAD), notably the scarcity of anomaly labels and the variability in anomaly lengths and shapes, have led to the need for a more efficient solution.

Anomaly Detection Contrastive Learning +3

Assessing Privacy Risks in Language Models: A Case Study on Summarization Tasks

no code implementations20 Oct 2023 Ruixiang Tang, Gord Lueck, Rodolfo Quispe, Huseyin A Inan, Janardhan Kulkarni, Xia Hu

Large language models have revolutionized the field of NLP by achieving state-of-the-art performance on various tasks.

text similarity

GrowLength: Accelerating LLMs Pretraining by Progressively Growing Training Length

no code implementations1 Oct 2023 Hongye Jin, Xiaotian Han, Jingfeng Yang, Zhimeng Jiang, Chia-Yuan Chang, Xia Hu

Our method progressively increases the training length throughout the pretraining phase, thereby mitigating computational costs and enhancing efficiency.

On the Equivalence of Graph Convolution and Mixup

no code implementations29 Sep 2023 Xiaotian Han, Hanqing Zeng, Yu Chen, Shaoliang Nie, Jingzhou Liu, Kanika Narang, Zahra Shakeri, Karthik Abinav Sankararaman, Song Jiang, Madian Khabsa, Qifan Wang, Xia Hu

We establish this equivalence mathematically by demonstrating that graph convolution networks (GCN) and simplified graph convolution (SGC) can be expressed as a form of Mixup.

Data Augmentation

Towards Data-centric Graph Machine Learning: Review and Outlook

1 code implementation20 Sep 2023 Xin Zheng, Yixin Liu, Zhifeng Bao, Meng Fang, Xia Hu, Alan Wee-Chung Liew, Shirui Pan

Data-centric AI, with its primary focus on the collection, management, and utilization of data to drive AI models and applications, has attracted increasing attention in recent years.

Management Navigate

DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research

1 code implementation4 Sep 2023 Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang, Kwei-Herng Lai, Daochen Zha, Ruixiang Tang, Fan Yang, Alfredo Costilla Reyes, Kaixiong Zhou, Xiaoqian Jiang, Xia Hu

The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are used to describe similar research.

named-entity-recognition Named Entity Recognition +5

Hessian-aware Quantized Node Embeddings for Recommendation

no code implementations2 Sep 2023 Huiyuan Chen, Kaixiong Zhou, Kwei-Herng Lai, Chin-Chia Michael Yeh, Yan Zheng, Xia Hu, Hao Yang

To address the gradient mismatch problem in STE, we further consider the quantized errors and its second-order derivatives for better stability.

Recommendation Systems Retrieval

Tackling Diverse Minorities in Imbalanced Classification

no code implementations28 Aug 2023 Kwei-Herng Lai, Daochen Zha, Huiyuan Chen, Mangesh Bendre, Yuzhong Chen, Mahashweta Das, Hao Yang, Xia Hu

Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers.

Anomaly Detection Classification +2

Collaborative Graph Neural Networks for Attributed Network Embedding

1 code implementation22 Jul 2023 Qiaoyu Tan, Xin Zhang, Xiao Huang, Hao Chen, Jundong Li, Xia Hu

Graph neural networks (GNNs) have shown prominent performance on attributed network embedding.

Attribute Network Embedding

Towards Assumption-free Bias Mitigation

no code implementations9 Jul 2023 Chia-Yuan Chang, Yu-Neng Chuang, Kwei-Herng Lai, Xiaotian Han, Xia Hu, Na Zou

Those studies face challenges, either in inaccurate predictions of sensitive attributes or the need to mitigate unequal distribution of manually defined non-sensitive attributes related to bias.

valid

FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods

1 code implementation15 Jun 2023 Xiaotian Han, Jianfeng Chi, Yu Chen, Qifan Wang, Han Zhao, Na Zou, Xia Hu

This paper introduces the Fair Fairness Benchmark (\textsf{FFB}), a benchmarking framework for in-processing group fairness methods.

Benchmarking Fairness

Efficient GNN Explanation via Learning Removal-based Attribution

no code implementations9 Jun 2023 Yao Rong, Guanchu Wang, Qizhang Feng, Ninghao Liu, Zirui Liu, Enkelejda Kasneci, Xia Hu

A strategy of subgraph sampling is designed in LARA to improve the scalability of the training process.

Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model

1 code implementation NeurIPS 2023 Zirui Liu, Guanchu Wang, Shaochen Zhong, Zhaozhuo Xu, Daochen Zha, Ruixiang Tang, Zhimeng Jiang, Kaixiong Zhou, Vipin Chaudhary, Shuai Xu, Xia Hu

While the model parameters do contribute to memory usage, the primary memory bottleneck during training arises from storing feature maps, also known as activations, as they are crucial for gradient calculation.

Language Modelling Stochastic Optimization

Editable Graph Neural Network for Node Classifications

no code implementations24 May 2023 Zirui Liu, Zhimeng Jiang, Shaochen Zhong, Kaixiong Zhou, Li Li, Rui Chen, Soo-Hyun Choi, Xia Hu

However, model editing for graph neural networks (GNNs) is rarely explored, despite GNNs' widespread applicability.

Fake News Detection Model Editing

Multi-factor Sequential Re-ranking with Perception-Aware Diversification

no code implementations21 May 2023 Yue Xu, Hao Chen, Zefan Wang, Jianwen Yin, Qijie Shen, Dimin Wang, Feiran Huang, Lixiang Lai, Tao Zhuang, Junfeng Ge, Xia Hu

Feed recommendation systems, which recommend a sequence of items for users to browse and interact with, have gained significant popularity in practical applications.

Graph Clustering Recommendation Systems +1

Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond

1 code implementation26 Apr 2023 Jingfeng Yang, Hongye Jin, Ruixiang Tang, Xiaotian Han, Qizhang Feng, Haoming Jiang, Bing Yin, Xia Hu

This paper presents a comprehensive and practical guide for practitioners and end-users working with Large Language Models (LLMs) in their downstream natural language processing (NLP) tasks.

Language Modelling Natural Language Understanding +1

Interactive System-wise Anomaly Detection

no code implementations21 Apr 2023 Guanchu Wang, Ninghao Liu, Daochen Zha, Xia Hu

Anomaly detection, where data instances are discovered containing feature patterns different from the majority, plays a fundamental role in various applications.

Anomaly Detection Data Poisoning +1

Context-aware Domain Adaptation for Time Series Anomaly Detection

no code implementations15 Apr 2023 Kwei-Herng Lai, Lan Wang, Huiyuan Chen, Kaixiong Zhou, Fei Wang, Hao Yang, Xia Hu

We formulate context sampling into the Markov decision process and exploit deep reinforcement learning to optimize the time series domain adaptation process via context sampling and design a tailored reward function to generate domain-invariant features that better align two domains for anomaly detection.

Anomaly Detection Domain Adaptation +3

Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching

no code implementations24 Mar 2023 Jiayi Yuan, Ruixiang Tang, Xiaoqian Jiang, Xia Hu

The process of matching patients with suitable clinical trials is essential for advancing medical research and providing optimal care.

Data Augmentation Text Generation

SPeC: A Soft Prompt-Based Calibration on Performance Variability of Large Language Model in Clinical Notes Summarization

no code implementations23 Mar 2023 Yu-Neng Chuang, Ruixiang Tang, Xiaoqian Jiang, Xia Hu

Electronic health records (EHRs) store an extensive array of patient information, encompassing medical histories, diagnoses, treatments, and test outcomes.

Language Modelling Large Language Model

Did You Train on My Dataset? Towards Public Dataset Protection with Clean-Label Backdoor Watermarking

1 code implementation20 Mar 2023 Ruixiang Tang, Qizhang Feng, Ninghao Liu, Fan Yang, Xia Hu

To overcome this challenge, we introduce a clean-label backdoor watermarking framework that uses imperceptible perturbations to replace mislabeled samples.

Anomaly Detection

PheME: A deep ensemble framework for improving phenotype prediction from multi-modal data

no code implementations19 Mar 2023 Shenghan Zhang, Haoxuan Li, Ruixiang Tang, Sirui Ding, Laila Rasmy, Degui Zhi, Na Zou, Xia Hu

In this work, we present PheME, an Ensemble framework using Multi-modality data of structured EHRs and unstructured clinical notes for accurate Phenotype prediction.

Ensemble Learning

Data-centric Artificial Intelligence: A Survey

10 code implementations17 Mar 2023 Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai, Fan Yang, Zhimeng Jiang, Shaochen Zhong, Xia Hu

Artificial Intelligence (AI) is making a profound impact in almost every domain.

Does Synthetic Data Generation of LLMs Help Clinical Text Mining?

no code implementations8 Mar 2023 Ruixiang Tang, Xiaotian Han, Xiaoqian Jiang, Xia Hu

Our method has resulted in significant improvements in the performance of downstream tasks, improving the F1-score from 23. 37% to 63. 99% for the named entity recognition task and from 75. 86% to 83. 59% for the relation extraction task.

Code Generation named-entity-recognition +5

Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach

1 code implementation NeurIPS 2023 Zhimeng Jiang, Xiaotian Han, Hongye Jin, Guanchu Wang, Rui Chen, Na Zou, Xia Hu

Motivated by these sufficient conditions, we propose robust fairness regularization (RFR) by considering the worst case within the model weight perturbation ball for each sensitive attribute group.

Attribute Fairness

CoRTX: Contrastive Framework for Real-time Explanation

1 code implementation5 Mar 2023 Yu-Neng Chuang, Guanchu Wang, Fan Yang, Quan Zhou, Pushkar Tripathi, Xuanting Cai, Xia Hu

In this work, we propose a COntrastive Real-Time eXplanation (CoRTX) framework to learn the explanation-oriented representation and relieve the intensive dependence of explainer training on explanation labels.

Contrastive Learning

Towards Personalized Preprocessing Pipeline Search

no code implementations28 Feb 2023 Diego Martinez, Daochen Zha, Qiaoyu Tan, Xia Hu

However, the existing systems often have a very small search space for feature preprocessing with the same preprocessing pipeline applied to all the numerical features.

AutoML Clustering +1

Fairly Predicting Graft Failure in Liver Transplant for Organ Assigning

no code implementations18 Feb 2023 Sirui Ding, Ruixiang Tang, Daochen Zha, Na Zou, Kai Zhang, Xiaoqian Jiang, Xia Hu

To tackle this problem, this work proposes a fair machine learning framework targeting graft failure prediction in liver transplant.

Fairness Knowledge Distillation

Efficient XAI Techniques: A Taxonomic Survey

no code implementations7 Feb 2023 Yu-Neng Chuang, Guanchu Wang, Fan Yang, Zirui Liu, Xuanting Cai, Mengnan Du, Xia Hu

Finally, we summarize the challenges of deploying XAI acceleration methods to real-world scenarios, overcoming the trade-off between faithfulness and efficiency, and the selection of different acceleration methods.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI)

The Science of Detecting LLM-Generated Texts

no code implementations4 Feb 2023 Ruixiang Tang, Yu-Neng Chuang, Xia Hu

The emergence of large language models (LLMs) has resulted in the production of LLM-generated texts that is highly sophisticated and almost indistinguishable from texts written by humans.

LLM-generated Text Detection Misinformation +2

Retiring $Δ$DP: New Distribution-Level Metrics for Demographic Parity

1 code implementation31 Jan 2023 Xiaotian Han, Zhimeng Jiang, Hongye Jin, Zirui Liu, Na Zou, Qifan Wang, Xia Hu

Unfortunately, in this paper, we reveal that the fairness metric $\Delta DP$ can not precisely measure the violation of demographic parity, because it inherently has the following drawbacks: i) zero-value $\Delta DP$ does not guarantee zero violation of demographic parity, ii) $\Delta DP$ values can vary with different classification thresholds.

Fairness

Data-centric AI: Perspectives and Challenges

1 code implementation12 Jan 2023 Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai, Fan Yang, Xia Hu

The role of data in building AI systems has recently been significantly magnified by the emerging concept of data-centric AI (DCAI), which advocates a fundamental shift from model advancements to ensuring data quality and reliability.

Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection

1 code implementation23 Dec 2022 Qiaoyu Tan, Xin Zhang, Ninghao Liu, Daochen Zha, Li Li, Rui Chen, Soo-Hyun Choi, Xia Hu

To bridge the gap, we introduce a Personalized Subgraph Selector (PS2) as a plug-and-play framework to automatically, personally, and inductively identify optimal subgraphs for different edges when performing GNNLP.

Link Prediction

TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems

no code implementations8 Dec 2022 Huiyuan Chen, Xiaoting Li, Kaixiong Zhou, Xia Hu, Chin-Chia Michael Yeh, Yan Zheng, Hao Yang

We found that our TinyKG with INT2 quantization aggressively reduces the memory footprint of activation maps with $7 \times$, only with $2\%$ loss in accuracy, allowing us to deploy KGNNs on memory-constrained devices.

Knowledge Graphs Quantization +1

Adaptive Risk-Aware Bidding with Budget Constraint in Display Advertising

1 code implementation6 Dec 2022 Zhimeng Jiang, Kaixiong Zhou, Mi Zhang, Rui Chen, Xia Hu, Soo-Hyun Choi

In this work, we explicitly factor in the uncertainty of estimated ad impression values and model the risk preference of a DSP under a specific state and market environment via a sequential decision process.

reinforcement-learning Reinforcement Learning (RL)

Mitigating Relational Bias on Knowledge Graphs

no code implementations26 Nov 2022 Yu-Neng Chuang, Kwei-Herng Lai, Ruixiang Tang, Mengnan Du, Chia-Yuan Chang, Na Zou, Xia Hu

Knowledge graph data are prevalent in real-world applications, and knowledge graph neural networks (KGNNs) are essential techniques for knowledge graph representation learning.

Graph Representation Learning Knowledge Graphs +1

QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks

no code implementations9 Nov 2022 Kaixiong Zhou, Zhenyu Zhang, Shengyuan Chen, Tianlong Chen, Xiao Huang, Zhangyang Wang, Xia Hu

Quantum neural networks (QNNs), an interdisciplinary field of quantum computing and machine learning, have attracted tremendous research interests due to the specific quantum advantages.

RSC: Accelerating Graph Neural Networks Training via Randomized Sparse Computations

no code implementations19 Oct 2022 Zirui Liu, Shengyuan Chen, Kaixiong Zhou, Daochen Zha, Xiao Huang, Xia Hu

To this end, we propose Randomized Sparse Computation, which for the first time demonstrate the potential of training GNNs with approximated operations.

DreamShard: Generalizable Embedding Table Placement for Recommender Systems

1 code implementation5 Oct 2022 Daochen Zha, Louis Feng, Qiaoyu Tan, Zirui Liu, Kwei-Herng Lai, Bhargav Bhushanam, Yuandong Tian, Arun Kejariwal, Xia Hu

Although prior work has explored learning-based approaches for the device placement of computational graphs, embedding table placement remains to be a challenging problem because of 1) the operation fusion of embedding tables, and 2) the generalizability requirement on unseen placement tasks with different numbers of tables and/or devices.

Recommendation Systems Reinforcement Learning (RL)

Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning

2 code implementations26 Aug 2022 Daochen Zha, Kwei-Herng Lai, Qiaoyu Tan, Sirui Ding, Na Zou, Xia Hu

Motivated by this, we investigate developing a learning-based over-sampling algorithm to optimize the classification performance, which is a challenging task because of the huge and hierarchical decision space.

Hierarchical Reinforcement Learning reinforcement-learning +1

AutoShard: Automated Embedding Table Sharding for Recommender Systems

1 code implementation12 Aug 2022 Daochen Zha, Louis Feng, Bhargav Bhushanam, Dhruv Choudhary, Jade Nie, Yuandong Tian, Jay Chae, Yinbin Ma, Arun Kejariwal, Xia Hu

This is a significant design challenge of distributed systems named embedding table sharding, i. e., how we should partition the embedding tables to balance the costs across devices, which is a non-trivial task because 1) it is hard to efficiently and precisely measure the cost, and 2) the partition problem is known to be NP-hard.

Recommendation Systems

DIVISION: Memory Efficient Training via Dual Activation Precision

1 code implementation5 Aug 2022 Guanchu Wang, Zirui Liu, Zhimeng Jiang, Ninghao Liu, Na Zou, Xia Hu

Activation compressed training provides a solution towards reducing the memory cost of training deep neural networks~(DNNs).

Quantization

Differentially Private Counterfactuals via Functional Mechanism

no code implementations4 Aug 2022 Fan Yang, Qizhang Feng, Kaixiong Zhou, Jiahao Chen, Xia Hu

Counterfactual, serving as one emerging type of model explanation, has attracted tons of attentions recently from both industry and academia.

counterfactual valid

Mitigating Algorithmic Bias with Limited Annotations

1 code implementation20 Jul 2022 Guanchu Wang, Mengnan Du, Ninghao Liu, Na Zou, Xia Hu

Existing work on fairness modeling commonly assumes that sensitive attributes for all instances are fully available, which may not be true in many real-world applications due to the high cost of acquiring sensitive information.

Fairness

Fair Machine Learning in Healthcare: A Review

no code implementations29 Jun 2022 Qizhang Feng, Mengnan Du, Na Zou, Xia Hu

The digitization of healthcare data coupled with advances in computational capabilities has propelled the adoption of machine learning (ML) in healthcare.

BIG-bench Machine Learning Fairness

Accelerating Shapley Explanation via Contributive Cooperator Selection

1 code implementation17 Jun 2022 Guanchu Wang, Yu-Neng Chuang, Mengnan Du, Fan Yang, Quan Zhou, Pushkar Tripathi, Xuanting Cai, Xia Hu

Even though Shapley value provides an effective explanation for a DNN model prediction, the computation relies on the enumeration of all possible input feature coalitions, which leads to the exponentially growing complexity.

Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture

no code implementations27 May 2022 Yicheng Wang, Xiaotian Han, Chia-Yuan Chang, Daochen Zha, Ulisses Braga-Neto, Xia Hu

Physics-informed neural networks (PINNs) are revolutionizing science and engineering practice by bringing together the power of deep learning to bear on scientific computation.

Hyperparameter Optimization Neural Architecture Search

G-Mixup: Graph Data Augmentation for Graph Classification

1 code implementation15 Feb 2022 Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Xia Hu

To this end, we propose $\mathcal{G}$-Mixup to augment graphs for graph classification by interpolating the generator (i. e., graphon) of different classes of graphs.

Data Augmentation Graph Classification

Geometric Graph Representation Learning via Maximizing Rate Reduction

no code implementations13 Feb 2022 Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Qingquan Song, Jundong Li, Xia Hu

Learning discriminative node representations benefits various downstream tasks in graph analysis such as community detection and node classification.

Community Detection Contrastive Learning +2

FMP: Toward Fair Graph Message Passing against Topology Bias

no code implementations8 Feb 2022 Zhimeng Jiang, Xiaotian Han, Chao Fan, Zirui Liu, Na Zou, Ali Mostafavi, Xia Hu

Despite recent advances in achieving fair representations and predictions through regularization, adversarial debiasing, and contrastive learning in graph neural networks (GNNs), the working mechanism (i. e., message passing) behind GNNs inducing unfairness issue remains unknown.

Contrastive Learning Fairness +1

Deconfounding to Explanation Evaluation in Graph Neural Networks

no code implementations21 Jan 2022 Ying-Xin Wu, Xiang Wang, An Zhang, Xia Hu, Fuli Feng, Xiangnan He, Tat-Seng Chua

In this work, we propose Deconfounded Subgraph Evaluation (DSE) which assesses the causal effect of an explanatory subgraph on the model prediction.

MGAE: Masked Autoencoders for Self-Supervised Learning on Graphs

1 code implementation7 Jan 2022 Qiaoyu Tan, Ninghao Liu, Xiao Huang, Rui Chen, Soo-Hyun Choi, Xia Hu

We introduce a novel masked graph autoencoder (MGAE) framework to perform effective learning on graph structure data.

Decoder Link Prediction +2

Towards Similarity-Aware Time-Series Classification

1 code implementation5 Jan 2022 Daochen Zha, Kwei-Herng Lai, Kaixiong Zhou, Xia Hu

Prior work has approached TSC from two major directions: (1) similarity-based methods that classify time-series based on the nearest neighbors, and (2) deep learning models that directly learn the representations for classification in a data-driven manner.

Classification Dynamic Time Warping +5

Defense Against Explanation Manipulation

no code implementations8 Nov 2021 Ruixiang Tang, Ninghao Liu, Fan Yang, Na Zou, Xia Hu

Explainable machine learning attracts increasing attention as it improves transparency of models, which is helpful for machine learning to be trusted in real applications.

Adversarial Attack BIG-bench Machine Learning

CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks

no code implementations28 Oct 2021 Haotian Xue, Kaixiong Zhou, Tianlong Chen, Kai Guo, Xia Hu, Yi Chang, Xin Wang

In this paper, we investigate GNNs from the lens of weight and feature loss landscapes, i. e., the loss changes with respect to model weights and node features, respectively.

Robustness Challenges in Model Distillation and Pruning for Natural Language Understanding

no code implementations16 Oct 2021 Mengnan Du, Subhabrata Mukherjee, Yu Cheng, Milad Shokouhi, Xia Hu, Ahmed Hassan Awadallah

Recent work has focused on compressing pre-trained language models (PLMs) like BERT where the major focus has been to improve the in-distribution performance for downstream tasks.

Knowledge Distillation Model Compression +1

G-Mixup: Graph Augmentation for Graph Classification

no code implementations29 Sep 2021 Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Xia Hu

To this end, we propose $\mathcal{G}$-Mixup to augment graphs for graph classification by interpolating the generator (i. e., graphon) of different classes of graphs.

Graph Classification

AutoCoG: A Unified Data-Modal Co-Search Framework for Graph Neural Networks

no code implementations29 Sep 2021 Duc N.M Hoang, Kaixiong Zhou, Tianlong Chen, Xia Hu, Zhangyang Wang

Despite the preliminary success, we argue that for GNNs, NAS has to be customized further, due to the topological complicacy of GNN input data (graph) as well as the notorious training instability.

Data Augmentation Language Modelling +1

EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression

no code implementations ICLR 2022 Zirui Liu, Kaixiong Zhou, Fan Yang, Li Li, Rui Chen, Xia Hu

Based on the implementation, we propose a memory-efficient framework called ``EXACT'', which for the first time demonstrate the potential and evaluate the feasibility of training GNNs with compressed activations.

Graph Learning

An Information Fusion Approach to Learning with Instance-Dependent Label Noise

no code implementations ICLR 2022 Zhimeng Jiang, Kaixiong Zhou, Zirui Liu, Li Li, Rui Chen, Soo-Hyun Choi, Xia Hu

Instance-dependent label noise (IDN) widely exists in real-world datasets and usually misleads the training of deep neural networks.

Generalized Demographic Parity for Group Fairness

1 code implementation ICLR 2022 Zhimeng Jiang, Xiaotian Han, Chao Fan, Fan Yang, Ali Mostafavi, Xia Hu

We show the understanding of GDP from the probability perspective and theoretically reveal the connection between GDP regularizer and adversarial debiasing.

Attribute Fairness

Was my Model Stolen? Feature Sharing for Robust and Transferable Watermarks

no code implementations29 Sep 2021 Ruixiang Tang, Hongye Jin, Curtis Wigington, Mengnan Du, Rajiv Jain, Xia Hu

The main idea is to insert a watermark which is only known to defender into the protected model and the watermark will then be transferred into all stolen models.

Model extraction

Orthogonal Graph Neural Networks

1 code implementation23 Sep 2021 Kai Guo, Kaixiong Zhou, Xia Hu, Yu Li, Yi Chang, Xin Wang

Graph neural networks (GNNs) have received tremendous attention due to their superiority in learning node representations.

Attribute Graph Classification

Adaptive Label Smoothing To Regularize Large-Scale Graph Training

no code implementations30 Aug 2021 Kaixiong Zhou, Ninghao Liu, Fan Yang, Zirui Liu, Rui Chen, Li Li, Soo-Hyun Choi, Xia Hu

Graph neural networks (GNNs), which learn the node representations by recursively aggregating information from its neighbors, have become a predominant computational tool in many domains.

Node Clustering

Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study

1 code implementation24 Aug 2021 Tianlong Chen, Kaixiong Zhou, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang

In view of those, we present the first fair and reproducible benchmark dedicated to assessing the "tricks" of training deep GNNs.

Dirichlet Energy Constrained Learning for Deep Graph Neural Networks

1 code implementation NeurIPS 2021 Kaixiong Zhou, Xiao Huang, Daochen Zha, Rui Chen, Li Li, Soo-Hyun Choi, Xia Hu

To this end, we analyze the bottleneck of deep GNNs by leveraging the Dirichlet energy of node embeddings, and propose a generalizable principle to guide the training of deep GNNs.

Generating the Graph Gestalt: Kernel-Regularized Graph Representation Learning

no code implementations29 Jun 2021 Kiarash Zahirnia, Ankita Sakhuja, Oliver Schulte, Parmis Nadaf, Ke Li, Xia Hu

Our experiments demonstrate a significant improvement in the realism of the generated graph structures, typically by 1-2 orders of magnitude of graph structure metrics, compared to leading graph VAEand GAN models.

Graph Representation Learning

Fairness via Representation Neutralization

no code implementations NeurIPS 2021 Mengnan Du, Subhabrata Mukherjee, Guanchu Wang, Ruixiang Tang, Ahmed Hassan Awadallah, Xia Hu

This process not only requires a lot of instance-level annotations for sensitive attributes, it also does not guarantee that all fairness sensitive information has been removed from the encoder.

Attribute Classification +1

Model-Based Counterfactual Synthesizer for Interpretation

no code implementations16 Jun 2021 Fan Yang, Sahan Suresh Alva, Jiahao Chen, Xia Hu

To address these limitations, we propose a Model-based Counterfactual Synthesizer (MCS) framework for interpreting machine learning models.

counterfactual Inductive Bias

DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning

1 code implementation11 Jun 2021 Daochen Zha, Jingru Xie, Wenye Ma, Sheng Zhang, Xiangru Lian, Xia Hu, Ji Liu

Games are abstractions of the real world, where artificial agents learn to compete and cooperate with other agents.

Game of Poker Multi-agent Reinforcement Learning +2

Simplifying Deep Reinforcement Learning via Self-Supervision

1 code implementation10 Jun 2021 Daochen Zha, Kwei-Herng Lai, Kaixiong Zhou, Xia Hu

Supervised regression to demonstrations has been demonstrated to be a stable way to train deep policy networks.

regression reinforcement-learning +1

A General Taylor Framework for Unifying and Revisiting Attribution Methods

no code implementations28 May 2021 Huiqi Deng, Na Zou, Mengnan Du, Weifu Chen, Guocan Feng, Xia Hu

However, the attribution problem has not been well-defined, which lacks a unified guideline to the contribution assignment process.

Benchmarking Decision Making

Learning Disentangled Representations for Time Series

no code implementations17 May 2021 Yuening Li, Zhengzhang Chen, Daochen Zha, Mengnan Du, Denghui Zhang, Haifeng Chen, Xia Hu

Motivated by the success of disentangled representation learning in computer vision, we study the possibility of learning semantic-rich time-series representations, which remains unexplored due to three main challenges: 1) sequential data structure introduces complex temporal correlations and makes the latent representations hard to interpret, 2) sequential models suffer from KL vanishing problem, and 3) interpretable semantic concepts for time-series often rely on multiple factors instead of individuals.

Disentanglement Time Series +1

Mutual Information Preserving Back-propagation: Learn to Invert for Faithful Attribution

no code implementations14 Apr 2021 Huiqi Deng, Na Zou, Weifu Chen, Guocan Feng, Mengnan Du, Xia Hu

The basic idea is to learn a source signal by back-propagation such that the mutual information between input and output should be as much as possible preserved in the mutual information between input and the source signal.

Decision Making

DivAug: Plug-in Automated Data Augmentation with Explicit Diversity Maximization

1 code implementation ICCV 2021 Zirui Liu, Haifeng Jin, Ting-Hsiang Wang, Kaixiong Zhou, Xia Hu

We validate in experiments that the relative gain from automated data augmentation in test accuracy is highly correlated to Variance Diversity.

Data Augmentation

Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU Models

no code implementations NAACL 2021 Mengnan Du, Varun Manjunatha, Rajiv Jain, Ruchi Deshpande, Franck Dernoncourt, Jiuxiang Gu, Tong Sun, Xia Hu

These two observations are further employed to formulate a measurement which can quantify the shortcut degree of each training sample.

Sparse-Interest Network for Sequential Recommendation

1 code implementation18 Feb 2021 Qiaoyu Tan, Jianwei Zhang, Jiangchao Yao, Ninghao Liu, Jingren Zhou, Hongxia Yang, Xia Hu

Our sparse-interest module can adaptively infer a sparse set of concepts for each user from the large concept pool and output multiple embeddings accordingly.

Sequential Recommendation

Dynamic Memory based Attention Network for Sequential Recommendation

1 code implementation18 Feb 2021 Qiaoyu Tan, Jianwei Zhang, Ninghao Liu, Xiao Huang, Hongxia Yang, Jingren Zhou, Xia Hu

It segments the overall long behavior sequence into a series of sub-sequences, then trains the model and maintains a set of memory blocks to preserve long-term interests of users.

Sequential Recommendation

Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments

3 code implementations ICLR 2021 Daochen Zha, Wenye Ma, Lei Yuan, Xia Hu, Ji Liu

Unfortunately, methods based on intrinsic rewards often fall short in procedurally-generated environments, where a different environment is generated in each episode so that the agent is not likely to visit the same state more than once.

Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation

no code implementations18 Jan 2021 Fan Yang, Ninghao Liu, Mengnan Du, Xia Hu

With the wide use of deep neural networks (DNN), model interpretability has become a critical concern, since explainable decisions are preferred in high-stake scenarios.

Attribute

Efficient Differentiable Neural Architecture Search with Model Parallelism

no code implementations1 Jan 2021 Yi-Wei Chen, Qingquan Song, Xia Hu

Differentiable NAS with supernets that encompass all potential architectures in a large graph cuts down search overhead to few GPU days or less.

Neural Architecture Search

Detecting Interactions from Neural Networks via Topological Analysis

no code implementations NeurIPS 2020 Zirui Liu, Qingquan Song, Kaixiong Zhou, Ting-Hsiang Wang, Ying Shan, Xia Hu

Motivated by the observation, in this paper, we propose to investigate the interaction detection problem from a novel topological perspective by analyzing the connectivity in neural networks.

Deep Serial Number: Computational Watermarking for DNN Intellectual Property Protection

no code implementations17 Nov 2020 Ruixiang Tang, Mengnan Du, Xia Hu

In this paper, we present DSN (Deep Serial Number), a simple yet effective watermarking algorithm designed specifically for deep neural networks (DNNs).

Knowledge Distillation valid

Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection

no code implementations29 Oct 2020 Imtiaz Ahmed, Travis Galoppo, Xia Hu, Yu Ding

In order to make dimensionality reduction effective for high-dimensional data embedding nonlinear low-dimensional manifold, it is understood that some sort of geodesic distance metric should be used to discriminate the data samples.

Clustering Dimensionality Reduction +1

Towards Interaction Detection Using Topological Analysis on Neural Networks

no code implementations25 Oct 2020 Zirui Liu, Qingquan Song, Kaixiong Zhou, Ting Hsiang Wang, Ying Shan, Xia Hu

Detecting statistical interactions between input features is a crucial and challenging task.

Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning

1 code implementation16 Sep 2020 Daochen Zha, Kwei-Herng Lai, Mingyang Wan, Xia Hu

Specifically, existing strategies have been focused on making the top instances more likely to be anomalous based on the feedback.

Anomaly Detection reinforcement-learning +2

Are Interpretations Fairly Evaluated? A Definition Driven Pipeline for Post-Hoc Interpretability

no code implementations16 Sep 2020 Ninghao Liu, Yunsong Meng, Xia Hu, Tie Wang, Bo Long

Recent years have witnessed an increasing number of interpretation methods being developed for improving transparency of NLP models.

A Unified Taylor Framework for Revisiting Attribution Methods

no code implementations21 Aug 2020 Huiqi Deng, Na Zou, Mengnan Du, Weifu Chen, Guocan Feng, Xia Hu

Attribution methods have been developed to understand the decision-making process of machine learning models, especially deep neural networks, by assigning importance scores to individual features.

Benchmarking Decision Making

Explainable Recommender Systems via Resolving Learning Representations

no code implementations21 Aug 2020 Ninghao Liu, Yong Ge, Li Li, Xia Hu, Rui Chen, Soo-Hyun Choi

Different from previous work, in our model, factor discovery and representation learning are simultaneously conducted, and we are able to handle extra attribute information and knowledge.

Attribute Explainable Recommendation +2

Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction

no code implementations29 Jun 2020 Qingquan Song, Dehua Cheng, Hanning Zhou, Jiyan Yang, Yuandong Tian, Xia Hu

Click-Through Rate (CTR) prediction is one of the most important machine learning tasks in recommender systems, driving personalized experience for billions of consumers.

Click-Through Rate Prediction Learning-To-Rank +2

AutoRec: An Automated Recommender System

1 code implementation26 Jun 2020 Ting-Hsiang Wang, Qingquan Song, Xiaotian Han, Zirui Liu, Haifeng Jin, Xia Hu

To address the need, we present AutoRec, an open-source automated machine learning (AutoML) platform extended from the TensorFlow ecosystem and, to our knowledge, the first framework to leverage AutoML for model search and hyperparameter tuning in deep recommendation models.

AutoML Click-Through Rate Prediction +1

Policy-GNN: Aggregation Optimization for Graph Neural Networks

1 code implementation26 Jun 2020 Kwei-Herng Lai, Daochen Zha, Kaixiong Zhou, Xia Hu

It is a challenging task to develop an effective aggregation strategy for each node, given complex graphs and sparse features.

Node Classification Reinforcement Learning (RL)

AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning

no code implementations19 Jun 2020 Yuening Li, Zhengzhang Chen, Daochen Zha, Kaixiong Zhou, Haifeng Jin, Haifeng Chen, Xia Hu

Outlier detection is an important data mining task with numerous practical applications such as intrusion detection, credit card fraud detection, and video surveillance.

Fraud Detection Image Classification +7

Measuring Model Complexity of Neural Networks with Curve Activation Functions

no code implementations16 Jun 2020 Xia Hu, Weiqing Liu, Jiang Bian, Jian Pei

Our results demonstrate that the occurrence of overfitting is positively correlated with the increase of model complexity during training.

Mitigating Gender Bias in Captioning Systems

1 code implementation15 Jun 2020 Ruixiang Tang, Mengnan Du, Yuening Li, Zirui Liu, Na Zou, Xia Hu

Image captioning has made substantial progress with huge supporting image collections sourced from the web.

Benchmarking Gender Prediction +1

An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks

1 code implementation15 Jun 2020 Ruixiang Tang, Mengnan Du, Ninghao Liu, Fan Yang, Xia Hu

In this paper, we investigate a specific security problem called trojan attack, which aims to attack deployed DNN systems relying on the hidden trigger patterns inserted by malicious hackers.

Towards Deeper Graph Neural Networks with Differentiable Group Normalization

1 code implementation NeurIPS 2020 Kaixiong Zhou, Xiao Huang, Yuening Li, Daochen Zha, Rui Chen, Xia Hu

Graph neural networks (GNNs), which learn the representation of a node by aggregating its neighbors, have become an effective computational tool in downstream applications.

Dual Policy Distillation

1 code implementation7 Jun 2020 Kwei-Herng Lai, Daochen Zha, Yuening Li, Xia Hu

In this work, we introduce dual policy distillation(DPD), a student-student framework in which two learners operate on the same environment to explore different perspectives of the environment and extract knowledge from each other to enhance their learning.

Continuous Control reinforcement-learning +1

XGNN: Towards Model-Level Explanations of Graph Neural Networks

no code implementations3 Jun 2020 Hao Yuan, Jiliang Tang, Xia Hu, Shuiwang Ji

Furthermore, our experimental results indicate that the generated graphs can provide guidance on how to improve the trained GNNs.

Graph Generation valid

iCapsNets: Towards Interpretable Capsule Networks for Text Classification

no code implementations16 May 2020 Zhengyang Wang, Xia Hu, Shuiwang Ji

On the other hand, iCapsNets explore a novel way to explain the model's general behavior, achieving global interpretability.

General Classification text-classification +1

Adversarial Attacks and Defenses: An Interpretation Perspective

no code implementations23 Apr 2020 Ninghao Liu, Mengnan Du, Ruocheng Guo, Huan Liu, Xia Hu

In this paper, we review recent work on adversarial attacks and defenses, particularly from the perspective of machine learning interpretation.

Adversarial Attack Adversarial Defense +2

Learning to Hash with Graph Neural Networks for Recommender Systems

no code implementations4 Mar 2020 Qiaoyu Tan, Ninghao Liu, Xing Zhao, Hongxia Yang, Jingren Zhou, Xia Hu

In this work, we investigate the problem of hashing with graph neural networks (GNNs) for high quality retrieval, and propose a simple yet effective discrete representation learning framework to jointly learn continuous and discrete codes.

Deep Hashing Graph Representation Learning +1

Multi-Channel Graph Convolutional Networks

no code implementations17 Dec 2019 Kaixiong Zhou, Qingquan Song, Xiao Huang, Daochen Zha, Na Zou, Xia Hu

To further improve the graph representation learning ability, hierarchical GNN has been explored.

Clustering Graph Classification +1

XDeep: An Interpretation Tool for Deep Neural Networks

1 code implementation4 Nov 2019 Fan Yang, Zijian Zhang, Haofan Wang, Yuening Li, Xia Hu

XDeep is an open-source Python package developed to interpret deep models for both practitioners and researchers.

RLCard: A Toolkit for Reinforcement Learning in Card Games

8 code implementations10 Oct 2019 Daochen Zha, Kwei-Herng Lai, Yuanpu Cao, Songyi Huang, Ruzhe Wei, Junyu Guo, Xia Hu

The goal of RLCard is to bridge reinforcement learning and imperfect information games, and push forward the research of reinforcement learning in domains with multiple agents, large state and action space, and sparse reward.

Board Games Game of Poker +3

PyODDS: An End-to-End Outlier Detection System

1 code implementation7 Oct 2019 Yuening Li, Daochen Zha, Na Zou, Xia Hu

PyODDS is an end-to end Python system for outlier detection with database support.

BIG-bench Machine Learning Outlier Detection

Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks

9 code implementations3 Oct 2019 Haofan Wang, Zifan Wang, Mengnan Du, Fan Yang, Zijian Zhang, Sirui Ding, Piotr Mardziel, Xia Hu

Recently, increasing attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network makes specific decisions.

Adversarial Attack Decision Making +1

Contextual Local Explanation for Black Box Classifiers

no code implementations2 Oct 2019 Zijian Zhang, Fan Yang, Haofan Wang, Xia Hu

We introduce a new model-agnostic explanation technique which explains the prediction of any classifier called CLE.

General Classification Image Classification

Sub-Architecture Ensemble Pruning in Neural Architecture Search

1 code implementation1 Oct 2019 Yijun Bian, Qingquan Song, Mengnan Du, Jun Yao, Huanhuan Chen, Xia Hu

Neural architecture search (NAS) is gaining more and more attention in recent years due to its flexibility and remarkable capability to reduce the burden of neural network design.

Ensemble Learning Ensemble Pruning +1

Distribution-Guided Local Explanation for Black-Box Classifiers

no code implementations25 Sep 2019 Weijie Fu, Meng Wang, Mengnan Du, Ninghao Liu, Shijie Hao, Xia Hu

Existing local explanation methods provide an explanation for each decision of black-box classifiers, in the form of relevance scores of features according to their contributions.

Towards Generalizable Deepfake Detection with Locality-aware AutoEncoder

no code implementations13 Sep 2019 Mengnan Du, Shiva Pentyala, Yuening Li, Xia Hu

The analysis further shows that LAE outperforms the state-of-the-arts by 6. 52%, 12. 03%, and 3. 08% respectively on three deepfake detection tasks in terms of generalization accuracy on previously unseen manipulations.

Active Learning DeepFake Detection +2

Fairness in Deep Learning: A Computational Perspective

no code implementations23 Aug 2019 Mengnan Du, Fan Yang, Na Zou, Xia Hu

Deep learning is increasingly being used in high-stake decision making applications that affect individual lives.

Decision Making Fairness

Learning Credible Deep Neural Networks with Rationale Regularization

no code implementations13 Aug 2019 Mengnan Du, Ninghao Liu, Fan Yang, Xia Hu

Recent explainability related studies have shown that state-of-the-art DNNs do not always adopt correct evidences to make decisions.

text-classification Text Classification

Deep Structured Cross-Modal Anomaly Detection

no code implementations11 Aug 2019 Yuening Li, Ninghao Liu, Jundong Li, Mengnan Du, Xia Hu

To this end, we propose a novel deep structured anomaly detection framework to identify the cross-modal anomalies embedded in the data.

Anomaly Detection

Techniques for Automated Machine Learning

no code implementations21 Jul 2019 Yi-Wei Chen, Qingquan Song, Xia Hu

Automated machine learning (AutoML) aims to find optimal machine learning solutions automatically given a machine learning problem.

Automated Feature Engineering Bayesian Optimization +4

Evaluating Explanation Without Ground Truth in Interpretable Machine Learning

no code implementations16 Jul 2019 Fan Yang, Mengnan Du, Xia Hu

Interpretable Machine Learning (IML) has become increasingly important in many real-world applications, such as autonomous cars and medical diagnosis, where explanations are significantly preferred to help people better understand how machine learning systems work and further enhance their trust towards systems.

BIG-bench Machine Learning Interpretable Machine Learning +1

XFake: Explainable Fake News Detector with Visualizations

no code implementations8 Jul 2019 Fan Yang, Shiva K. Pentyala, Sina Mohseni, Mengnan Du, Hao Yuan, Rhema Linder, Eric D. Ragan, Shuiwang Ji, Xia Hu

In this demo paper, we present the XFake system, an explainable fake news detector that assists end-users to identify news credibility.

Attribute

Experience Replay Optimization

no code implementations19 Jun 2019 Daochen Zha, Kwei-Herng Lai, Kaixiong Zhou, Xia Hu

Experience replay enables reinforcement learning agents to memorize and reuse past experiences, just as humans replay memories for the situation at hand.

Continuous Control reinforcement-learning +1

Exact and Consistent Interpretation of Piecewise Linear Models Hidden behind APIs: A Closed Form Solution

1 code implementation17 Jun 2019 Zicun Cong, Lingyang Chu, Lanjun Wang, Xia Hu, Jian Pei

More and more AI services are provided through APIs on cloud where predictive models are hidden behind APIs.

Coupled Variational Recurrent Collaborative Filtering

1 code implementation11 Jun 2019 Qingquan Song, Shiyu Chang, Xia Hu

To bridge the gap, in this paper, we propose a Coupled Variational Recurrent Collaborative Filtering (CVRCF) framework based on the idea of Deep Bayesian Learning to handle the streaming recommendation problem.

Collaborative Filtering Recommendation Systems +1

Deep Bayesian Optimization on Attributed Graphs

3 code implementations31 May 2019 Jiaxu Cui, Bo Yang, Xia Hu

Attributed graphs, which contain rich contextual features beyond just network structure, are ubiquitous and have been observed to benefit various network analytics applications.

Bayesian Optimization Gaussian Processes +1

Is a Single Vector Enough? Exploring Node Polysemy for Network Embedding

1 code implementation25 May 2019 Ninghao Liu, Qiaoyu Tan, Yuening Li, Hongxia Yang, Jingren Zhou, Xia Hu

Network embedding models are powerful tools in mapping nodes in a network into continuous vector-space representations in order to facilitate subsequent tasks such as classification and link prediction.

General Classification Language Modelling +3

Deep Representation Learning for Social Network Analysis

no code implementations18 Apr 2019 Qiaoyu Tan, Ninghao Liu, Xia Hu

First, we introduce the basic models for learning node representations in homogeneous networks.

Anomaly Detection Attribute +3

On Attribution of Recurrent Neural Network Predictions via Additive Decomposition

no code implementations27 Mar 2019 Mengnan Du, Ninghao Liu, Fan Yang, Shuiwang Ji, Xia Hu

REAT decomposes the final prediction of a RNN into additive contribution of each word in the input text.

Decision Making

Multi-Label Adversarial Perturbations

no code implementations2 Jan 2019 Qingquan Song, Haifeng Jin, Xiao Huang, Xia Hu

Experiments on real-world multi-label image classification and ranking problems demonstrate the effectiveness of our proposed frameworks and provide insights of the vulnerability of multi-label deep learning models under diverse targeted attacking strategies.

General Classification Multi-class Classification +3

Techniques for Interpretable Machine Learning

no code implementations31 Jul 2018 Mengnan Du, Ninghao Liu, Xia Hu

Interpretable machine learning tackles the important problem that humans cannot understand the behaviors of complex machine learning models and how these models arrive at a particular decision.

BIG-bench Machine Learning Interpretable Machine Learning

Auto-Keras: An Efficient Neural Architecture Search System

14 code implementations27 Jun 2018 Haifeng Jin, Qingquan Song, Xia Hu

In this paper, we propose a novel framework enabling Bayesian optimization to guide the network morphism for efficient neural architecture search.

Bayesian Optimization Neural Architecture Search

r-Instance Learning for Missing People Tweets Identification

no code implementations28 May 2018 Yang Yang, Haoyan Liu, Xia Hu, Jiawei Zhang, Xiao-Ming Zhang, Zhoujun Li, Philip S. Yu

The number of missing people (i. e., people who get lost) greatly increases in recent years.

Towards Explanation of DNN-based Prediction with Guided Feature Inversion

no code implementations19 Mar 2018 Mengnan Du, Ninghao Liu, Qingquan Song, Xia Hu

While deep neural networks (DNN) have become an effective computational tool, the prediction results are often criticized by the lack of interpretability, which is essential in many real-world applications such as health informatics.

Decision Making

Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form Solution

no code implementations17 Feb 2018 Lingyang Chu, Xia Hu, Juhua Hu, Lanjun Wang, Jian Pei

Strong intelligent machines powered by deep neural networks are increasingly deployed as black boxes to make decisions in risk-sensitive domains, such as finance and medical.

Contextual Outlier Interpretation

no code implementations28 Nov 2017 Ninghao Liu, Donghwa Shin, Xia Hu

Outlier detection plays an essential role in many data-driven applications to identify isolated instances that are different from the majority.

feature selection Outlier Interpretation

Tensor Completion Algorithms in Big Data Analytics

no code implementations28 Nov 2017 Qingquan Song, Hancheng Ge, James Caverlee, Xia Hu

Tensor completion is a problem of filling the missing or unobserved entries of partially observed tensors.

Deep Style Match for Complementary Recommendation

no code implementations26 Aug 2017 Kui Zhao, Xia Hu, Jiajun Bu, Can Wang

In order to answer these kinds of questions, we attempt to model human sense of style compatibility in this paper.

Common Sense Reasoning Feature Engineering

Neural Collaborative Filtering

43 code implementations WWW 2017 Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua

When it comes to model the key factor in collaborative filtering -- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items.

Collaborative Filtering Recommendation Systems

Attributed Network Embedding for Learning in a Dynamic Environment

no code implementations6 Jun 2017 Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, Huan Liu

To our best knowledge, we are the first to tackle this problem with the following two challenges: (1) the inherently correlated network and node attributes could be noisy and incomplete, it necessitates a robust consensus representation to capture their individual properties and correlations; (2) the embedding learning needs to be performed in an online fashion to adapt to the changes accordingly.

Attribute Clustering +3