Search Results for author: Kun Zhang

Found 233 papers, 85 papers with code

Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Bases

no code implementations COLING 2022 Kun Zhang, Yunqi Qiu, Yuanzhuo Wang, Long Bai, Wei Li, Xuhui Jiang, HuaWei Shen, Xueqi Cheng

Complex question generation over knowledge bases (KB) aims to generate natural language questions involving multiple KB relations or functional constraints.

Contrastive Learning Decoder +3

Label-Noise Robust Domain Adaptation

no code implementations ICML 2020 Xiyu Yu, Tongliang Liu, Mingming Gong, Kun Zhang, Kayhan Batmanghelich, DaCheng Tao

Domain adaptation aims to correct the classifiers when faced with distribution shift between source (training) and target (test) domains.

Denoising Domain Adaptation

LTF: A Label Transformation Framework for Correcting Label Shift

no code implementations ICML 2020 Jiaxian Guo, Mingming Gong, Tongliang Liu, Kun Zhang, DaCheng Tao

Distribution shift is a major obstacle to the deployment of current deep learning models on real-world problems.

Optimal Kernel Choice for Score Function-based Causal Discovery

no code implementations14 Jul 2024 Wenjie Wang, Biwei Huang, Feng Liu, Xinge You, Tongliang Liu, Kun Zhang, Mingming Gong

In this paper, we propose a kernel selection method within the generalized score function that automatically selects the optimal kernel that best fits the data.

Causal Discovery

Empowering Graph Invariance Learning with Deep Spurious Infomax

1 code implementation13 Jul 2024 Tianjun Yao, Yongqiang Chen, Zhenhao Chen, Kai Hu, Zhiqiang Shen, Kun Zhang

To bridge this gap, we introduce a novel graph invariance learning paradigm, which induces a robust and general inductive bias.

Inductive Bias

Cloud Atlas: Efficient Fault Localization for Cloud Systems using Language Models and Causal Insight

no code implementations11 Jul 2024 Zhiqiang Xie, Yujia Zheng, Lizi Ottens, Kun Zhang, Christos Kozyrakis, Jonathan Mace

We evaluate Atlas across a range of fault localization scenarios and demonstrate that Atlas is capable of generating causal graphs in a scalable and generalizable manner, with performance that far surpasses that of data-driven algorithms and is commensurate to the ground-truth baseline.

Causal Discovery Fault localization

Detecting and Identifying Selection Structure in Sequential Data

no code implementations29 Jun 2024 Yujia Zheng, Zeyu Tang, Yiwen Qiu, Bernhard Schölkopf, Kun Zhang

Therefore, rather than merely viewing it as a bias, we explore the causal structure of selection in sequential data to delve deeper into the complete causal process.

Inductive Bias

MuGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph Classification

1 code implementation28 Jun 2024 Tianjun Yao, Jiaqi Sun, Defu Cao, Kun Zhang, Guangyi Chen

To tackle the second challenge, MuGSI proposes to incorporate a node feature augmentation component, thereby enhancing the expressiveness of the student MLPs and making them more capable learners.

Graph Classification Knowledge Distillation +1

Learning Discrete Latent Variable Structures with Tensor Rank Conditions

no code implementations11 Jun 2024 Zhengming Chen, Ruichu Cai, Feng Xie, Jie Qiao, Anpeng Wu, Zijian Li, Zhifeng Hao, Kun Zhang

Unobserved discrete data are ubiquitous in many scientific disciplines, and how to learn the causal structure of these latent variables is crucial for uncovering data patterns.

Causal Discovery

Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges

no code implementations10 Jun 2024 Usman Gohar, Zeyu Tang, Jialu Wang, Kun Zhang, Peter L. Spirtes, Yang Liu, Lu Cheng

The widespread integration of Machine Learning systems in daily life, particularly in high-stakes domains, has raised concerns about the fairness implications.

Decision Making Fairness

Path-Specific Causal Reasoning for Fairness-aware Cognitive Diagnosis

1 code implementation5 Jun 2024 Dacao Zhang, Kun Zhang, Le Wu, Mi Tian, Richang Hong, Meng Wang

Then, we design a novel attribute-oriented predictor to decouple the sensitive attributes, in which fairness-related sensitive features will be eliminated and other useful information will be retained.

Attribute cognitive diagnosis +1

On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data

no code implementations4 Jun 2024 Shunxing Fan, Mingming Gong, Kun Zhang

Although we expect such instantaneous dependence has consistency with the true causal relation in certain sense to make the discovery results meaningful, it remains unclear what type of consistency we need and when will such consistency be satisfied.

Causal Discovery

Learning Discrete Concepts in Latent Hierarchical Models

no code implementations1 Jun 2024 Lingjing Kong, Guangyi Chen, Biwei Huang, Eric P. Xing, Yuejie Chi, Kun Zhang

Learning concepts from natural high-dimensional data (e. g., images) holds potential in building human-aligned and interpretable machine learning models.

Interpretable Machine Learning

On the Identification of Temporally Causal Representation with Instantaneous Dependence

no code implementations24 May 2024 Zijian Li, Yifan Shen, Kaitao Zheng, Ruichu Cai, Xiangchen Song, Mingming Gong, Zhengmao Zhu, Guangyi Chen, Kun Zhang

To fill this gap, we propose an \textbf{ID}entification framework for instantane\textbf{O}us \textbf{L}atent dynamics (\textbf{IDOL}) by imposing a sparse influence constraint that the latent causal processes have sparse time-delayed and instantaneous relations.

Motion Forecasting Representation Learning +2

An Efficient Finite Difference Approximation via a Double Sample-Recycling Approach

no code implementations9 May 2024 Guo Liang, Guangwu Liu, Kun Zhang

Secondly, recycling these pilot samples again and generating new samples at the estimated perturbation, lead to an efficient finite difference estimator.

A Conditional Independence Test in the Presence of Discretization

1 code implementation26 Apr 2024 Boyang Sun, Yu Yao, Huangyuan Hao, Yumou Qiu, Kun Zhang

Applying existing test methods to the observations of $X_1$, $\tilde{X}_2$ and $X_3$ can lead to a false conclusion about the underlying conditional independence of variables $X_1$, $X_2$ and $X_3$.

Causal Discovery

Counterfactual Reasoning Using Predicted Latent Personality Dimensions for Optimizing Persuasion Outcome

no code implementations21 Apr 2024 Donghuo Zeng, Roberto S. Legaspi, Yuewen Sun, Xinshuai Dong, Kazushi Ikeda, Peter Spirtes, Kun Zhang

In this paper, we present a novel approach that tracks a user's latent personality dimensions (LPDs) during ongoing persuasion conversation and generates tailored counterfactual utterances based on these LPDs to optimize the overall persuasion outcome.

counterfactual Counterfactual Reasoning +1

Masked Multi-Domain Network: Multi-Type and Multi-Scenario Conversion Rate Prediction with a Single Model

no code implementations26 Mar 2024 Wentao Ouyang, Xiuwu Zhang, Chaofeng Guo, Shukui Ren, Yupei Sui, Kun Zhang, Jinmei Luo, Yunfeng Chen, Dongbo Xu, Xiangzheng Liu, Yanlong Du

A desired model for this problem should satisfy the following requirements: 1) Accuracy: the model should achieve fine-grained accuracy with respect to any conversion type in any display scenario.

Identifiable Latent Neural Causal Models

no code implementations23 Mar 2024 Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton Van Den Hengel, Kun Zhang, Javen Qinfeng Shi

This work establishes a {sufficient} and {necessary} condition characterizing the types of distribution shifts for identifiability in the context of latent additive noise models.

Representation Learning

Local Causal Discovery with Linear non-Gaussian Cyclic Models

1 code implementation21 Mar 2024 Haoyue Dai, Ignavier Ng, Yujia Zheng, Zhengqing Gao, Kun Zhang

Local causal discovery is of great practical significance, as there are often situations where the discovery of the global causal structure is unnecessary, and the interest lies solely on a single target variable.

Causal Discovery

Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View

1 code implementation21 Mar 2024 Haoyue Dai, Ignavier Ng, Gongxu Luo, Peter Spirtes, Petar Stojanov, Kun Zhang

This particular test-wise deletion procedure, in which we perform CI tests on the samples without zeros for the conditioned variables, can be seamlessly integrated with existing structure learning approaches including constraint-based and greedy score-based methods, thus giving rise to a principled framework for GRNI in the presence of dropouts.

Imputation

Counterfactual Generation with Identifiability Guarantees

1 code implementation NeurIPS 2023 Hanqi Yan, Lingjing Kong, Lin Gui, Yuejie Chi, Eric Xing, Yulan He, Kun Zhang

In this work, we tackle the domain-varying dependence between the content and the style variables inherent in the counterfactual generation task.

counterfactual Style Transfer +1

Federated Causal Discovery from Heterogeneous Data

1 code implementation20 Feb 2024 Loka Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen, Tongliang Liu, Bin Gu, Kun Zhang

This discrepancy has motivated the development of federated causal discovery (FCD) approaches.

Causal Discovery

Confidence Matters: Revisiting Intrinsic Self-Correction Capabilities of Large Language Models

1 code implementation19 Feb 2024 Loka Li, Zhenhao Chen, Guangyi Chen, Yixuan Zhang, Yusheng Su, Eric Xing, Kun Zhang

We have experimentally observed that LLMs possess the capability to understand the "confidence" in their own responses.

Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering

1 code implementation18 Feb 2024 Peijie Sun, Le Wu, Kun Zhang, Xiangzhi Chen, Meng Wang

Using the graph-based collaborative filtering model as our backbone and following the same data augmentation methods as the existing contrastive learning model SGL, we effectively enhance the performance of the recommendation model.

Collaborative Filtering Contrastive Learning +2

Improving Cognitive Diagnosis Models with Adaptive Relational Graph Neural Networks

no code implementations15 Feb 2024 Pengyang Shao, Chen Gao, Lei Chen, Yonghui Yang, Kun Zhang, Meng Wang

Typically, these CD algorithms assist students by inferring their abilities (i. e., their proficiency levels on various knowledge concepts).

cognitive diagnosis Graph Neural Network

Revealing Multimodal Contrastive Representation Learning through Latent Partial Causal Models

no code implementations9 Feb 2024 Yuhang Liu, Zhen Zhang, Dong Gong, Biwei Huang, Mingming Gong, Anton Van Den Hengel, Kun Zhang, Javen Qinfeng Shi

Multimodal contrastive representation learning methods have proven successful across a range of domains, partly due to their ability to generate meaningful shared representations of complex phenomena.

Representation Learning

Causal Representation Learning from Multiple Distributions: A General Setting

no code implementations7 Feb 2024 Kun Zhang, Shaoan Xie, Ignavier Ng, Yujia Zheng

We show that under the sparsity constraint on the recovered graph over the latent variables and suitable sufficient change conditions on the causal influences, interestingly, one can recover the moralized graph of the underlying directed acyclic graph, and the recovered latent variables and their relations are related to the underlying causal model in a specific, nontrivial way.

Representation Learning

Discovery of the Hidden World with Large Language Models

no code implementations6 Feb 2024 Chenxi Liu, Yongqiang Chen, Tongliang Liu, Mingming Gong, James Cheng, Bo Han, Kun Zhang

The rise of large language models (LLMs) that are trained to learn rich knowledge from the massive observations of the world, provides a new opportunity to assist with discovering high-level hidden variables from the raw observational data.

Causal Discovery

Natural Counterfactuals With Necessary Backtracking

no code implementations2 Feb 2024 Guang-Yuan Hao, Jiji Zhang, Biwei Huang, Hao Wang, Kun Zhang

Counterfactual reasoning is pivotal in human cognition and especially important for providing explanations and making decisions.

counterfactual Counterfactual Reasoning

Calibration-then-Calculation: A Variance Reduced Metric Framework in Deep Click-Through Rate Prediction Models

no code implementations30 Jan 2024 Yewen Fan, Nian Si, Xiangchen Song, Kun Zhang

In this paper, we introduce a novel metric framework, the Calibrated Loss Metric, designed to address this issue by reducing the variance present in its conventional counterpart.

Click-Through Rate Prediction

CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process

1 code implementation25 Jan 2024 Guangyi Chen, Yifan Shen, Zhenhao Chen, Xiangchen Song, Yuewen Sun, Weiran Yao, Xiao Liu, Kun Zhang

Identifying the underlying time-delayed latent causal processes in sequential data is vital for grasping temporal dynamics and making downstream reasoning.

Consistency Guided Knowledge Retrieval and Denoising in LLMs for Zero-shot Document-level Relation Triplet Extraction

1 code implementation24 Jan 2024 Qi Sun, Kun Huang, Xiaocui Yang, Rong Tong, Kun Zhang, Soujanya Poria

In this paper, we propose a Zero-shot Document-level Relation Triplet Extraction (ZeroDocRTE) framework, which generates labeled data by retrieval and denoising knowledge from LLMs, called GenRDK.

Denoising Relation +2

HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization

no code implementations18 Jan 2024 Guanglin Zhou, Zhongyi Han, Shiming Chen, Biwei Huang, Liming Zhu, Tongliang Liu, Lina Yao, Kun Zhang

Domain Generalization (DG) endeavors to create machine learning models that excel in unseen scenarios by learning invariant features.

Contrastive Learning Domain Generalization

Functional Linear Non-Gaussian Acyclic Model for Causal Discovery

no code implementations17 Jan 2024 Tian-Le Yang, Kuang-Yao Lee, Kun Zhang, Joe Suzuki

To expand this concept, we extend the notion of variables to encompass vectors and even functions, leading to the Functional Linear Non-Gaussian Acyclic Model (Func-LiNGAM).

Causal Discovery EEG

On the Three Demons in Causality in Finance: Time Resolution, Nonstationarity, and Latent Factors

no code implementations28 Dec 2023 Xinshuai Dong, Haoyue Dai, Yewen Fan, Songyao Jin, Sathyamoorthy Rajendran, Kun Zhang

Financial data is generally time series in essence and thus suffers from three fundamental issues: the mismatch in time resolution, the time-varying property of the distribution - nonstationarity, and causal factors that are important but unknown/unobserved.

Time Series

Learning Socio-Temporal Graphs for Multi-Agent Trajectory Prediction

no code implementations22 Dec 2023 Yuke Li, Lixiong Chen, Guangyi Chen, Ching-Yao Chan, Kun Zhang, Stefano Anzellotti, Donglai Wei

In order to predict a pedestrian's trajectory in a crowd accurately, one has to take into account her/his underlying socio-temporal interactions with other pedestrians consistently.

Trajectory Prediction

Identification of Causal Structure with Latent Variables Based on Higher Order Cumulants

no code implementations19 Dec 2023 Wei Chen, Zhiyi Huang, Ruichu Cai, Zhifeng Hao, Kun Zhang

Despite the emergence of numerous methods aimed at addressing this challenge, they are not fully identified to the structure that two observed variables are influenced by one latent variable and there might be a directed edge in between.

Causal Discovery

A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables

no code implementations18 Dec 2023 Xinshuai Dong, Biwei Huang, Ignavier Ng, Xiangchen Song, Yujia Zheng, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang

Most existing causal discovery methods rely on the assumption of no latent confounders, limiting their applicability in solving real-life problems.

Causal Discovery

How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation

1 code implementation12 Dec 2023 Zhongyi Han, Guanglin Zhou, Rundong He, Jindong Wang, Tailin Wu, Yilong Yin, Salman Khan, Lina Yao, Tongliang Liu, Kun Zhang

We further investigate its adaptability to controlled data perturbations and examine the efficacy of in-context learning as a tool to enhance its adaptation.

Anomaly Detection Autonomous Driving +6

DreamInpainter: Text-Guided Subject-Driven Image Inpainting with Diffusion Models

no code implementations5 Dec 2023 Shaoan Xie, Yang Zhao, Zhisheng Xiao, Kelvin C. K. Chan, Yandong Li, Yanwu Xu, Kun Zhang, Tingbo Hou

Our extensive experiments demonstrate the superior performance of our method in terms of visual quality, identity preservation, and text control, showcasing its effectiveness in the context of text-guided subject-driven image inpainting.

Image Inpainting

Identifying Semantic Component for Robust Molecular Property Prediction

1 code implementation8 Nov 2023 Zijian Li, Zunhong Xu, Ruichu Cai, Zhenhui Yang, Yuguang Yan, Zhifeng Hao, Guangyi Chen, Kun Zhang

Specifically, we first formulate the data generation process from the atom level to the molecular level, where the latent space is split into SI substructures, SR substructures, and SR atom variables.

Molecular Property Prediction Property Prediction

Procedural Fairness Through Decoupling Objectionable Data Generating Components

1 code implementation5 Nov 2023 Zeyu Tang, Jialu Wang, Yang Liu, Peter Spirtes, Kun Zhang

We reveal and address the frequently overlooked yet important issue of disguised procedural unfairness, namely, the potentially inadvertent alterations on the behavior of neutral (i. e., not problematic) aspects of data generating process, and/or the lack of procedural assurance of the greatest benefit of the least advantaged individuals.

Decision Making Fairness

Temporally Disentangled Representation Learning under Unknown Nonstationarity

1 code implementation NeurIPS 2023 Xiangchen Song, Weiran Yao, Yewen Fan, Xinshuai Dong, Guangyi Chen, Juan Carlos Niebles, Eric Xing, Kun Zhang

In unsupervised causal representation learning for sequential data with time-delayed latent causal influences, strong identifiability results for the disentanglement of causally-related latent variables have been established in stationary settings by leveraging temporal structure.

Disentanglement

Identifiable Latent Polynomial Causal Models Through the Lens of Change

no code implementations24 Oct 2023 Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton Van Den Hengel, Kun Zhang, Javen Qinfeng Shi

However, this progress rests on the assumption that the causal relationships among latent causal variables adhere strictly to linear Gaussian models.

Representation Learning

Subspace Identification for Multi-Source Domain Adaptation

1 code implementation NeurIPS 2023 Zijian Li, Ruichu Cai, Guangyi Chen, Boyang Sun, Zhifeng Hao, Kun Zhang

To mitigate the need for these strict assumptions, we propose a subspace identification theory that guarantees the disentanglement of domain-invariant and domain-specific variables under less restrictive constraints regarding domain numbers and transformation properties, thereby facilitating domain adaptation by minimizing the impact of domain shifts on invariant variables.

Disentanglement Domain Adaptation +1

Towards Realistic Zero-Shot Classification via Self Structural Semantic Alignment

1 code implementation24 Aug 2023 Sheng Zhang, Muzammal Naseer, Guangyi Chen, Zhiqiang Shen, Salman Khan, Kun Zhang, Fahad Khan

To address this challenge, we propose the Self Structural Semantic Alignment (S^3A) framework, which extracts the structural semantic information from unlabeled data while simultaneously self-learning.

Self-Learning Zero-Shot Learning

Tem-adapter: Adapting Image-Text Pretraining for Video Question Answer

1 code implementation ICCV 2023 Guangyi Chen, Xiao Liu, Guangrun Wang, Kun Zhang, Philip H. S. Torr, Xiao-Ping Zhang, Yansong Tang

To bridge these gaps, in this paper, we propose Tem-Adapter, which enables the learning of temporal dynamics and complex semantics by a visual Temporal Aligner and a textual Semantic Aligner.

Decoder Question Answering +1

Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables

no code implementations13 Aug 2023 Feng Xie, Biwei Huang, Zhengming Chen, Ruichu Cai, Clark Glymour, Zhi Geng, Kun Zhang

To this end, we propose a Generalized Independent Noise (GIN) condition for linear non-Gaussian acyclic causal models that incorporate latent variables, which establishes the independence between a linear combination of certain measured variables and some other measured variables.

Causal-learn: Causal Discovery in Python

1 code implementation31 Jul 2023 Yujia Zheng, Biwei Huang, Wei Chen, Joseph Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang

Causal discovery aims at revealing causal relations from observational data, which is a fundamental task in science and engineering.

Causal Discovery

Generative Contrastive Graph Learning for Recommendation

1 code implementation11 Jul 2023 Yonghui Yang, Zhengwei Wu, Le Wu, Kun Zhang, Richang Hong, Zhiqiang Zhang, Jun Zhou, Meng Wang

Second, feature augmentation imposes the same scale noise augmentation on each node, which neglects the unique characteristics of nodes on the graph.

Collaborative Filtering Contrastive Learning +3

A Model Fusion Distributed Kalman Filter For Non-Gaussian Observation Noise

no code implementations20 Jun 2023 Xuemei Mao, Gang Wang, Bei Peng, Jiacheng He, Kun Zhang, Song Gao

A DKF, called model fusion DKF (MFDKF) is proposed against the non-Gaussain noise.

Description-Enhanced Label Embedding Contrastive Learning for Text Classification

1 code implementation15 Jun 2023 Kun Zhang, Le Wu, Guangyi Lv, Enhong Chen, Shulan Ruan, Jing Liu, Zhiqiang Zhang, Jun Zhou, Meng Wang

Then, we propose a novel Relation of Relation Learning Network (R2-Net) for text classification, in which text classification and R2 classification are treated as optimization targets.

Contrastive Learning Relation +3

Evolving Semantic Prototype Improves Generative Zero-Shot Learning

no code implementations12 Jun 2023 Shiming Chen, Wenjin Hou, Ziming Hong, Xiaohan Ding, Yibing Song, Xinge You, Tongliang Liu, Kun Zhang

After alignment, synthesized sample features from unseen classes are closer to the real sample features and benefit DSP to improve existing generative ZSL methods by 8. 5\%, 8. 0\%, and 9. 7\% on the standard CUB, SUN AWA2 datasets, the significant performance improvement indicates that evolving semantic prototype explores a virgin field in ZSL.

Zero-Shot Learning

Partial Identifiability for Domain Adaptation

no code implementations10 Jun 2023 Lingjing Kong, Shaoan Xie, Weiran Yao, Yujia Zheng, Guangyi Chen, Petar Stojanov, Victor Akinwande, Kun Zhang

In general, without further assumptions, the joint distribution of the features and the label is not identifiable in the target domain.

Unsupervised Domain Adaptation

Advancing Counterfactual Inference through Nonlinear Quantile Regression

no code implementations9 Jun 2023 Shaoan Xie, Biwei Huang, Bin Gu, Tongliang Liu, Kun Zhang

Traditional counterfactual inference, under Pearls' counterfactual framework, typically depends on having access to or estimating a structural causal model.

counterfactual Counterfactual Inference +2

Causal Discovery with Latent Confounders Based on Higher-Order Cumulants

no code implementations31 May 2023 Ruichu Cai, Zhiyi Huang, Wei Chen, Zhifeng Hao, Kun Zhang

In light of the power of the closed-form solution to OICA corresponding to the One-Latent-Component structure, we formulate a way to estimate the mixing matrix using the higher-order cumulants, and further propose the testable One-Latent-Component condition to identify the latent variables and determine causal orders.

Causal Discovery

Understanding Breast Cancer Survival: Using Causality and Language Models on Multi-omics Data

no code implementations28 May 2023 Mugariya Farooq, Shahad Hardan, Aigerim Zhumbhayeva, Yujia Zheng, Preslav Nakov, Kun Zhang

The need for more usable and explainable machine learning models in healthcare increases the importance of developing and utilizing causal discovery algorithms, which aim to discover causal relations by analyzing observational data.

Causal Discovery

Voices of Her: Analyzing Gender Differences in the AI Publication World

1 code implementation24 May 2023 Yiwen Ding, Jiarui Liu, Zhiheng Lyu, Kun Zhang, Bernhard Schoelkopf, Zhijing Jin, Rada Mihalcea

While several previous studies have analyzed gender bias in research, we are still missing a comprehensive analysis of gender differences in the AI community, covering diverse topics and different development trends.

Diversity

Generalized Precision Matrix for Scalable Estimation of Nonparametric Markov Networks

no code implementations19 May 2023 Yujia Zheng, Ignavier Ng, Yewen Fan, Kun Zhang

A Markov network characterizes the conditional independence structure, or Markov property, among a set of random variables.

Uncertainty Guided Label Denoising for Document-level Distant Relation Extraction

1 code implementation18 May 2023 Qi Sun, Kun Huang, Xiaocui Yang, Pengfei Hong, Kun Zhang, Soujanya Poria

Therefore, how to select effective pseudo labels to denoise DS data is still a challenge in document-level distant relation extraction.

Denoising Document-level Relation Extraction +1

Feature Expansion for Graph Neural Networks

1 code implementation10 May 2023 Jiaqi Sun, Lin Zhang, Guangyi Chen, Kun Zhang, Peng Xu, Yujiu Yang

Graph neural networks aim to learn representations for graph-structured data and show impressive performance, particularly in node classification.

Node Classification Representation Learning

Explainable Recommender with Geometric Information Bottleneck

no code implementations9 May 2023 Hanqi Yan, Lin Gui, Menghan Wang, Kun Zhang, Yulan He

Explainable recommender systems can explain their recommendation decisions, enhancing user trust in the systems.

Explanation Generation Recommendation Systems

Unsupervised Sampling Promoting for Stochastic Human Trajectory Prediction

1 code implementation CVPR 2023 Guangyi Chen, Zhenhao Chen, Shunxing Fan, Kun Zhang

Specifically, we model the trajectory sampling as a Gaussian process and construct an acquisition function to measure the potential sampling value.

Bayesian Optimization Trajectory Prediction

Scalable Causal Discovery with Score Matching

no code implementations6 Apr 2023 Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello

This paper demonstrates how to discover the whole causal graph from the second derivative of the log-likelihood in observational non-linear additive Gaussian noise models.

Causal Discovery

Causal Discovery with Score Matching on Additive Models with Arbitrary Noise

no code implementations6 Apr 2023 Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello

Causal discovery methods are intrinsically constrained by the set of assumptions needed to ensure structure identifiability.

Additive models Causal Discovery

Structure Learning with Continuous Optimization: A Sober Look and Beyond

no code implementations4 Apr 2023 Ignavier Ng, Biwei Huang, Kun Zhang

This paper investigates in which cases continuous optimization for directed acyclic graph (DAG) structure learning can and cannot perform well and why this happens, and suggests possible directions to make the search procedure more reliable.

Redeeming Falsifiability?

no code implementations28 Mar 2023 Mark Whitmeyer, Kun Zhang

We revisit Popper's falsifiability criterion.

HiNet: Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information Extraction

1 code implementation10 Mar 2023 Jie zhou, Xianshuai Cao, Wenhao Li, Lin Bo, Kun Zhang, Chuan Luo, Qian Yu

Multi-scenario & multi-task learning has been widely applied to many recommendation systems in industrial applications, wherein an effective and practical approach is to carry out multi-scenario transfer learning on the basis of the Mixture-of-Expert (MoE) architecture.

Multi-Task Learning Recommendation Systems

Beware of Instantaneous Dependence in Reinforcement Learning

no code implementations9 Mar 2023 Zhengmao Zhu, YuRen Liu, Honglong Tian, Yang Yu, Kun Zhang

Playing an important role in Model-Based Reinforcement Learning (MBRL), environment models aim to predict future states based on the past.

Model-based Reinforcement Learning reinforcement-learning +1

Measuring the Privacy Leakage via Graph Reconstruction Attacks on Simplicial Neural Networks (Student Abstract)

no code implementations8 Feb 2023 Huixin Zhan, Kun Zhang, Keyi Lu, Victor S. Sheng

In this paper, we measure the privacy leakage via studying whether graph representations can be inverted to recover the graph used to generate them via graph reconstruction attack (GRA).

Decoder Graph Attention +3

Emerging Synergies in Causality and Deep Generative Models: A Survey

no code implementations29 Jan 2023 Guanglin Zhou, Shaoan Xie, GuangYuan Hao, Shiming Chen, Biwei Huang, Xiwei Xu, Chen Wang, Liming Zhu, Lina Yao, Kun Zhang

In the field of artificial intelligence (AI), the quest to understand and model data-generating processes (DGPs) is of paramount importance.

Causal Identification Fairness +1

Increasing Fairness via Combination with Learning Guarantees

no code implementations25 Jan 2023 Yijun Bian, Kun Zhang, Anqi Qiu, Nanguang Chen

Furthermore, we investigate the properties of the proposed measure and propose first- and second-order oracle bounds to show that fairness can be boosted via ensemble combination with theoretical learning guarantees.

Ensemble Learning Fairness +1

Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors

1 code implementation21 Jan 2023 Zeyu Tang, Yatong Chen, Yang Liu, Kun Zhang

The pursuit of long-term fairness involves the interplay between decision-making and the underlying data generating process.

Decision Making Fairness

Improving Uncertainty Quantification of Variance Networks by Tree-Structured Learning

1 code implementation24 Dec 2022 Wenxuan Ma, Xing Yan, Kun Zhang

A tree is built upon giving the training data, whose leaf nodes represent different regions where region-specific neural networks are trained to predict both the mean and the variance for quantifying uncertainty.

Uncertainty Quantification

SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model

no code implementations CVPR 2023 Shaoan Xie, Zhifei Zhang, Zhe Lin, Tobias Hinz, Kun Zhang

By contrast, multi-modal inpainting provides more flexible and useful controls on the inpainted content, \eg, a text prompt can be used to describe an object with richer attributes, and a mask can be used to constrain the shape of the inpainted object rather than being only considered as a missing area.

Image Inpainting Object +1

Causal Discovery in Linear Latent Variable Models Subject to Measurement Error

1 code implementation8 Nov 2022 Yuqin Yang, AmirEmad Ghassami, Mohamed Nafea, Negar Kiyavash, Kun Zhang, Ilya Shpitser

We demonstrate a somewhat surprising connection between this problem and causal discovery in the presence of unobserved parentless causes, in the sense that there is a mapping, given by the mixing matrix, between the underlying models to be inferred in these problems.

Causal Discovery

Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks

1 code implementation1 Nov 2022 Yue Yu, Xuan Kan, Hejie Cui, ran Xu, Yujia Zheng, Xiangchen Song, Yanqiao Zhu, Kun Zhang, Razieh Nabi, Ying Guo, Chao Zhang, Carl Yang

To better adapt GNNs for fMRI analysis, we propose TBDS, an end-to-end framework based on \underline{T}ask-aware \underline{B}rain connectivity \underline{D}AG (short for Directed Acyclic Graph) \underline{S}tructure generation for fMRI analysis.

Time Series Time Series Analysis

Temporally Disentangled Representation Learning

no code implementations24 Oct 2022 Weiran Yao, Guangyi Chen, Kun Zhang

In this work, we establish the identifiability theories of nonparametric latent causal processes from their nonlinear mixtures under fixed temporal causal influences and analyze how distribution changes can further benefit the disentanglement.

Disentanglement

Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models

no code implementations20 Oct 2022 Haoyue Dai, Peter Spirtes, Kun Zhang

Causal discovery under measurement error aims to recover the causal graph among unobserved target variables from observations made with measurement error.

Causal Discovery

Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations

no code implementations12 Oct 2022 Yuanyuan Wang, Wei Huang, Mingming Gong, Xi Geng, Tongliang Liu, Kun Zhang, DaCheng Tao

This paper derives a sufficient condition for the identifiability of homogeneous linear ODE systems from a sequence of equally-spaced error-free observations sampled from a single trajectory.

PLOT: Prompt Learning with Optimal Transport for Vision-Language Models

1 code implementation3 Oct 2022 Guangyi Chen, Weiran Yao, Xiangchen Song, Xinyue Li, Yongming Rao, Kun Zhang

To solve this problem, we propose to apply optimal transport to match the vision and text modalities.

Latent Hierarchical Causal Structure Discovery with Rank Constraints

no code implementations1 Oct 2022 Biwei Huang, Charles Jia Han Low, Feng Xie, Clark Glymour, Kun Zhang

Most causal discovery procedures assume that there are no latent confounders in the system, which is often violated in real-world problems.

Causal Discovery

Truncated Matrix Power Iteration for Differentiable DAG Learning

1 code implementation30 Aug 2022 Zhen Zhang, Ignavier Ng, Dong Gong, Yuhang Liu, Ehsan M Abbasnejad, Mingming Gong, Kun Zhang, Javen Qinfeng Shi

Recovering underlying Directed Acyclic Graph (DAG) structures from observational data is highly challenging due to the combinatorial nature of the DAG-constrained optimization problem.

Identifiable Latent Causal Content for Domain Adaptation under Latent Covariate Shift

no code implementations30 Aug 2022 Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton Van Den Hengel, Kun Zhang, Javen Qinfeng Shi

Within this new paradigm, we present an intricate causal generative model by introducing latent noises across domains, along with a latent content variable and a latent style variable to achieve more nuanced rendering of observational data.

Domain Adaptation

Identifying Weight-Variant Latent Causal Models

no code implementations30 Aug 2022 Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton Van Den Hengel, Kun Zhang, Javen Qinfeng Shi

The task of causal representation learning aims to uncover latent higher-level causal representations that affect lower-level observations.

Representation Learning

Costly Evidence and Discretionary Disclosure

no code implementations9 Aug 2022 Mark Whitmeyer, Kun Zhang

When acquisition is covert, the receiver does not.

A Causal Approach to Detecting Multivariate Time-series Anomalies and Root Causes

no code implementations30 Jun 2022 Wenzhuo Yang, Kun Zhang, Steven C. H. Hoi

In light of the modularity property of causal systems (the causal processes to generate different variables are irrelevant modules), the original problem is divided into a series of separate, simpler, and low-dimensional anomaly detection problems so that where an anomaly happens (root causes) can be directly identified.

Anomaly Detection Time Series +1

Withholding Verifiable Information

no code implementations20 Jun 2022 Kun Zhang

These results help in characterizing the sender's preferred equilibria and her equilibrium payoff set in a class of verifiable disclosure games.

On the Identifiability of Nonlinear ICA: Sparsity and Beyond

no code implementations15 Jun 2022 Yujia Zheng, Ignavier Ng, Kun Zhang

We show that under specific instantiations of such constraints, the independent latent sources can be identified from their nonlinear mixtures up to a permutation and a component-wise transformation, thus achieving nontrivial identifiability of nonlinear ICA without auxiliary variables.

Inductive Bias

Causal Balancing for Domain Generalization

1 code implementation10 Jun 2022 Xinyi Wang, Michael Saxon, Jiachen Li, Hongyang Zhang, Kun Zhang, William Yang Wang

While machine learning models rapidly advance the state-of-the-art on various real-world tasks, out-of-domain (OOD) generalization remains a challenging problem given the vulnerability of these models to spurious correlations.

Domain Generalization

What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective

no code implementations8 Jun 2022 Zeyu Tang, Jiji Zhang, Kun Zhang

In this paper, we review and reflect on various fairness notions previously proposed in machine learning literature, and make an attempt to draw connections to arguments in moral and political philosophy, especially theories of justice.

BIG-bench Machine Learning Fairness +1

Offline Reinforcement Learning with Causal Structured World Models

no code implementations3 Jun 2022 Zheng-Mao Zhu, Xiong-Hui Chen, Hong-Long Tian, Kun Zhang, Yang Yu

Model-based methods have recently shown promising for offline reinforcement learning (RL), aiming to learn good policies from historical data without interacting with the environment.

Model-based Reinforcement Learning Offline RL +2

Counterfactual Fairness with Partially Known Causal Graph

no code implementations27 May 2022 Aoqi Zuo, Susan Wei, Tongliang Liu, Bo Han, Kun Zhang, Mingming Gong

Interestingly, we find that counterfactual fairness can be achieved as if the true causal graph were fully known, when specific background knowledge is provided: the sensitive attributes do not have ancestors in the causal graph.

BIG-bench Machine Learning Causal Inference +2

MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models

1 code implementation27 May 2022 Erdun Gao, Ignavier Ng, Mingming Gong, Li Shen, Wei Huang, Tongliang Liu, Kun Zhang, Howard Bondell

In this paper, we develop a general method, which we call MissDAG, to perform causal discovery from data with incomplete observations.

Causal Discovery Imputation +1

Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems

1 code implementation19 May 2022 Yewen Fan, Nian Si, Kun Zhang

Calibration is defined as the ratio of the average predicted click rate to the true click rate.

Recommendation Systems

Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification

no code implementations18 May 2022 Kai Zhang, Qi Liu, Zhenya Huang, Mingyue Cheng, Kun Zhang, Mengdi Zhang, Wei Wu, Enhong Chen

Existing studies in this task attach more attention to the sequence modeling of sentences while largely ignoring the rich domain-invariant semantics embedded in graph structures (i. e., the part-of-speech tags and dependency relations).

Classification Graph Attention +4

A Review-aware Graph Contrastive Learning Framework for Recommendation

1 code implementation26 Apr 2022 Jie Shuai, Kun Zhang, Le Wu, Peijie Sun, Richang Hong, Meng Wang, Yong Li

Second, while most current models suffer from limited user behaviors, can we exploit the unique self-supervised signals in the review-aware graph to guide two recommendation components better?

Contrastive Learning Recommendation Systems +1

Factored Adaptation for Non-Stationary Reinforcement Learning

no code implementations30 Mar 2022 Fan Feng, Biwei Huang, Kun Zhang, Sara Magliacane

Dealing with non-stationarity in environments (e. g., in the transition dynamics) and objectives (e. g., in the reward functions) is a challenging problem that is crucial in real-world applications of reinforcement learning (RL).

reinforcement-learning Reinforcement Learning (RL)

Sample Recycling for Nested Simulation with Application in Portfolio Risk Measurement

no code implementations29 Mar 2022 Kun Zhang, Ben Mingbin Feng, Guangwu Liu, Shiyu Wang

The resulting sample of conditional expectations is then used to estimate different risk measures of interest.

valid

Attainability and Optimality: The Equalized Odds Fairness Revisited

no code implementations24 Feb 2022 Zeyu Tang, Kun Zhang

In particular, for prediction performed by a deterministic function of input features, we give conditions under which Equalized Odds can hold true; if the stochastic prediction is acceptable, we show that under mild assumptions, fair predictors can always be derived.

Fairness

Conditional Contrastive Learning with Kernel

1 code implementation ICLR 2022 Yao-Hung Hubert Tsai, Tianqin Li, Martin Q. Ma, Han Zhao, Kun Zhang, Louis-Philippe Morency, Ruslan Salakhutdinov

Conditional contrastive learning frameworks consider the conditional sampling procedure that constructs positive or negative data pairs conditioned on specific variables.

Contrastive Learning

Buying Opinions

no code implementations10 Feb 2022 Mark Whitmeyer, Kun Zhang

A principal hires an agent to acquire soft information about an unknown state.

Learning Latent Causal Dynamics

no code implementations10 Feb 2022 Weiran Yao, Guangyi Chen, Kun Zhang

Specifically, the framework factorizes unknown distribution shifts into transition distribution changes caused by fixed dynamics and time-varying latent causal relations, and by global changes in observation.

Time Series Time Series Analysis

Identifiability of Label Noise Transition Matrix

no code implementations4 Feb 2022 Yang Liu, Hao Cheng, Kun Zhang

When label noise transition depends on each instance, the problem of identifying the instance-dependent noise transition matrix becomes substantially more challenging.

Learning with noisy labels

Optimal transport for causal discovery

no code implementations ICLR 2022 Ruibo Tu, Kun Zhang, Hedvig Kjellström, Cheng Zhang

With this criterion, we propose a novel optimal transport-based algorithm for ANMs which is robust to the choice of models and extend it to post-nonlinear models.

Causal Discovery

Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions

1 code implementation NeurIPS 2021 Ignavier Ng, Yujia Zheng, Jiji Zhang, Kun Zhang

Many of the causal discovery methods rely on the faithfulness assumption to guarantee asymptotic correctness.

Causal Discovery

Perlin Noise Improve Adversarial Robustness

no code implementations26 Dec 2021 Chengjun Tang, Kun Zhang, Chunfang Xing, Yong Ding, Zengmin Xu

Combined with the defensive idea of adversarial training, we use Perlin noise to train the neural network to obtain a model that can defend against procedural noise adversarial examples.

Adversarial Robustness

Domain Adaptation with Invariant Representation Learning: What Transformations to Learn?

1 code implementation NeurIPS 2021 Petar Stojanov, Zijian Li, Mingming Gong, Ruichu Cai, Jaime Carbonell, Kun Zhang

We provide reasoning why when the supports of the source and target data from overlap, any map of $X$ that is fixed across domains may not be suitable for domain adaptation via invariant features.

Representation Learning Transfer Learning +1

Transferable Time-Series Forecasting under Causal Conditional Shift

1 code implementation5 Nov 2021 Zijian Li, Ruichu Cai, Tom Z. J Fu, Zhifeng Hao, Kun Zhang

In order to address these challenges, we analyze variational conditional dependencies in time-series data and find that the causal structures are usually stable among domains, and further raise the causal conditional shift assumption.

Domain Adaptation Semi-supervised Domain Adaptation +2

Nash equilibrium of multi-agent graphical game with a privacy information encrypted learning algorithm

no code implementations29 Oct 2021 Kun Zhang, Ji-Feng Zhang, Rong Su, Huaguang Zhang

With the secure hierarchical structure, the relationship between the secure consensus problem and global Nash equilibrium is discussed under potential packet loss attacks, and the necessary and sufficient condition for the existence of global Nash equilibrium is provided regarding the soft-constrained graphical game.

Quantization

Towards Federated Bayesian Network Structure Learning with Continuous Optimization

2 code implementations18 Oct 2021 Ignavier Ng, Kun Zhang

Traditionally, Bayesian network structure learning is often carried out at a central site, in which all data is gathered.

Federated Learning

Action-Sufficient State Representation Learning for Control with Structural Constraints

no code implementations12 Oct 2021 Biwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang

Perceived signals in real-world scenarios are usually high-dimensional and noisy, and finding and using their representation that contains essential and sufficient information required by downstream decision-making tasks will help improve computational efficiency and generalization ability in the tasks.

Computational Efficiency Decision Making +1

Learning Temporally Causal Latent Processes from General Temporal Data

2 code implementations11 Oct 2021 Weiran Yao, Yuewen Sun, Alex Ho, Changyin Sun, Kun Zhang

In this work, we consider both a nonparametric, nonstationary setting and a parametric setting for the latent processes and propose two provable conditions under which temporally causal latent processes can be identified from their nonlinear mixtures.

Causal Discovery Representation Learning +1

Learning Temporally Latent Causal Processes from General Temporal Data

2 code implementations ICLR 2022 Weiran Yao, Yuewen Sun, Alex Ho, Changyin Sun, Kun Zhang

Our goal is to find time-delayed latent causal variables and identify their relations from temporal measured variables.

Causal Discovery Disentanglement +1

Unaligned Image-to-Image Translation by Learning to Reweight

1 code implementation ICCV 2021 Shaoan Xie, Mingming Gong, Yanwu Xu, Kun Zhang

An essential yet restrictive assumption for unsupervised image translation is that the two domains are aligned, e. g., for the selfie2anime task, the anime (selfie) domain must contain only anime (selfie) face images that can be translated to some images in the other domain.

Translation Unsupervised Image-To-Image Translation

A Fast PC Algorithm with Reversed-order Pruning and A Parallelization Strategy

no code implementations10 Sep 2021 Kai Zhang, Chao Tian, Kun Zhang, Todd Johnson, Xiaoqian Jiang

The PC algorithm is the state-of-the-art algorithm for causal structure discovery on observational data.

Generalized Minimum Error Entropy for Adaptive Filtering

1 code implementation8 Sep 2021 Jiacheng He, Gang Wang, Bei Peng, Zhenyu Feng, Kun Zhang

In our study, a novel concept, called generalized error entropy, utilizing the generalized Gaussian density (GGD) function as the kernel function is proposed.

Instance-dependent Label-noise Learning under a Structural Causal Model

2 code implementations NeurIPS 2021 Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, Kun Zhang

In particular, we show that properly modeling the instances will contribute to the identifiability of the label noise transition matrix and thus lead to a better classifier.

DAE-GAN: Dynamic Aspect-aware GAN for Text-to-Image Synthesis

1 code implementation ICCV 2021 Shulan Ruan, Yong Zhang, Kun Zhang, Yanbo Fan, Fan Tang, Qi Liu, Enhong Chen

Text-to-image synthesis refers to generating an image from a given text description, the key goal of which lies in photo realism and semantic consistency.

Image Generation Sentence +2

Outdoor Position Recovery from HeterogeneousTelco Cellular Data

no code implementations24 Aug 2021 Yige Zhang, Weixiong Rao, Kun Zhang, Lei Chen

The HMM approaches typically assume stable mobility patterns of the underlying mobile devices.

Multi-Task Learning Position +1

LadRa-Net: Locally-Aware Dynamic Re-read Attention Net for Sentence Semantic Matching

no code implementations6 Aug 2021 Kun Zhang, Guangyi Lv, Le Wu, Enhong Chen, Qi Liu, Meng Wang

In order to overcome this problem and boost the performance of attention mechanism, we propose a novel dynamic re-read attention, which can pay close attention to one small region of sentences at each step and re-read the important parts for better sentence representations.

Language Modelling Natural Language Inference +2

Multi-Channel Auto-Encoders and a Novel Dataset for Learning Domain Invariant Representations of Histopathology Images

no code implementations15 Jul 2021 Andrew Moyes, Richard Gault, Kun Zhang, Ji Ming, Danny Crookes, Jing Wang

Experimental results show that the MCAE model produces feature representations that are less sensitive to inter-domain variations than the comparative StaNoSA method when tested on the novel synthetic data.

Model Transferability With Responsive Decision Subjects

1 code implementation13 Jul 2021 Yatong Chen, Zeyu Tang, Kun Zhang, Yang Liu

We provide both upper bounds for the performance gap due to the induced domain shift, as well as lower bounds for the trade-offs that a classifier has to suffer on either the source training distribution or the induced target distribution.

BIG-bench Machine Learning Domain Adaptation

AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning

1 code implementation ICLR 2022 Biwei Huang, Fan Feng, Chaochao Lu, Sara Magliacane, Kun Zhang

We show that by explicitly leveraging this compact representation to encode changes, we can efficiently adapt the policy to the target domain, in which only a few samples are needed and further policy optimization is avoided.

Atari Games reinforcement-learning +2

Region-Aware Network: Model Human's Top-Down Visual Perception Mechanism for Crowd Counting

no code implementations23 Jun 2021 Yuehai Chen, Jing Yang, Dong Zhang, Kun Zhang, Badong Chen, Shaoyi Du

More specifically, we scan the whole input images and its priority maps in the form of column vector to obtain a relevance matrix estimating their similarity.

Crowd Counting

AKG: Automatic Kernel Generation for Neural Processing Units using Polyhedral Transformations

1 code implementation Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation 2021 Jie Zhao, Bojie Li, Wang Nie, Zhen Geng, Renwei Zhang, Xiong Gao, Bin Cheng, Chen Wu, Yun Cheng, Zheng Li, Peng Di, Kun Zhang, Xuefeng Jin

Existing tensor compilers have proven their effectiveness in deploying deep neural networks on general-purpose hardware like CPU and GPU, but optimizing for neural processing units (NPUs) is still challenging due to the heterogeneous compute units and complicated memory hierarchy.

Code Generation Management +1

Graph Domain Adaptation: A Generative View

no code implementations14 Jun 2021 Ruichu Cai, Fengzhu Wu, Zijian Li, Pengfei Wei, Lingling Yi, Kun Zhang

Based on this assumption, we propose a disentanglement-based unsupervised domain adaptation method for the graph-structured data, which applies variational graph auto-encoders to recover these latent variables and disentangles them via three supervised learning modules.

Disentanglement Graph Classification +2

DGA-Net Dynamic Gaussian Attention Network for Sentence Semantic Matching

no code implementations9 Jun 2021 Kun Zhang, Guangyi Lv, Meng Wang, Enhong Chen

Then, we develop a Dynamic Gaussian Attention (DGA) to dynamically capture the important parts and corresponding local contexts from a detailed perspective.

Language Modelling Relation +2

Progressive Open-Domain Response Generation with Multiple Controllable Attributes

no code implementations7 Jun 2021 Haiqin Yang, Xiaoyuan Yao, Yiqun Duan, Jianping Shen, Jie Zhong, Kun Zhang

More specifically, PHED deploys Conditional Variational AutoEncoder (CVAE) on Transformer to include one aspect of attributes at one stage.

Attribute Decoder +2

Graph-based Exercise- and Knowledge-Aware Learning Network for Student Performance Prediction

no code implementations1 Jun 2021 Mengfan Liu, Pengyang Shao, Kun Zhang

Predicting student performance is a fundamental task in Intelligent Tutoring Systems (ITSs), by which we can learn about students' knowledge level and provide personalized teaching strategies for them.

Collaborative Filtering

Privileged Graph Distillation for Cold Start Recommendation

no code implementations31 May 2021 Shuai Wang, Kun Zhang, Le Wu, Haiping Ma, Richang Hong, Meng Wang

The teacher model is composed of a heterogeneous graph structure for warm users and items with privileged CF links.

Attribute Collaborative Filtering +1

Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation

1 code implementation16 May 2021 Lei Chen, Le Wu, Kun Zhang, Richang Hong, Meng Wang

Despite the performance gain of these implicit feedback based models, the recommendation results are still far from satisfactory due to the sparsity of the observed item set for each user.

Collaborative Filtering

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

1 code implementation27 Apr 2021 Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, Meng Wang

Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks.

Collaborative Filtering Sequential Recommendation

FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders

no code implementations26 Mar 2021 Wei Chen, Kun Zhang, Ruichu Cai, Biwei Huang, Joseph Ramsey, Zhifeng Hao, Clark Glymour

The first step of our method uses the FCI procedure, which allows confounders and is able to produce asymptotically correct results.

Causal Discovery

Probing black hole microstructure with the kinetic turnover of phase transition

no code implementations18 Feb 2021 Ran Li, Kun Zhang, Jin Wang

By treating black hole as the macroscopic stable state on the free energy landscape, we propose that the stochastic dynamics of the black hole phase transition can be effectively described by the Langevin equation or equivalently by the Fokker-Planck equation in phase space.

General Relativity and Quantum Cosmology

Towards advancing the earthquake forecasting by machine learning of satellite data

no code implementations31 Jan 2021 Pan Xiong, Lei Tong, Kun Zhang, Xuhui Shen, Roberto Battiston, Dimitar Ouzounov, Roberto Iuppa, Danny Crookes, Cheng Long, Huiyu Zhou

Amongst the available technologies for earthquake research, remote sensing has been commonly used due to its unique features such as fast imaging and wide image-acquisition range.

BIG-bench Machine Learning

Field-free spin-orbit torque-induced switching of perpendicular magnetization in a ferrimagnetic layer with vertical composition gradient

no code implementations21 Jan 2021 Zhenyi Zheng, Yue Zhang, Victor Lopez-Dominguez, Luis Sánchez-Tejerina, Jiacheng Shi, Xueqiang Feng, Lei Chen, Zilu Wang, Zhizhong Zhang, Kun Zhang, Bin Hong, Yong Xu, Youguang Zhang, Mario Carpentieri, Albert Fert, Giovanni Finocchio, Weisheng Zhao, Pedram Khalili Amiri

Existing methods to do so involve the application of an in-plane bias magnetic field, or incorporation of in-plane structural asymmetry in the device, both of which can be difficult to implement in practical applications.

Mesoscale and Nanoscale Physics

Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction

no code implementations20 Jan 2021 Gang Qu, Li Xiao, Wenxing Hu, Kun Zhang, Vince D. Calhoun, Yu-Ping Wang

Methods: To take advantage of complementary information from multi-modal fMRI, we propose an interpretable multi-modal graph convolutional network (MGCN) model, incorporating the fMRI time series and the functional connectivity (FC) between each pair of brain regions.

Graph Embedding Time Series Analysis

Localized Meta-Learning: A PAC-Bayes Analysis for Meta-Learning Beyond Global Prior

no code implementations1 Jan 2021 Chenghao Liu, Tao Lu, Doyen Sahoo, Yuan Fang, Kun Zhang, Steven Hoi

Meta-learning methods learn the meta-knowledge among various training tasks and aim to promote the learning of new tasks under the task similarity assumption.

Meta-Learning

Minimal Geometry-Distortion Constraint for Unsupervised Image-to-Image Translation

no code implementations1 Jan 2021 Jiaxian Guo, Jiachen Li, Mingming Gong, Huan Fu, Kun Zhang, DaCheng Tao

Unsupervised image-to-image (I2I) translation, which aims to learn a domain mapping function without paired data, is very challenging because the function is highly under-constrained.

Translation Unsupervised Image-To-Image Translation

Score-based Causal Discovery from Heterogeneous Data

no code implementations1 Jan 2021 Chenwei Ding, Biwei Huang, Mingming Gong, Kun Zhang, Tongliang Liu, DaCheng Tao

Most algorithms in causal discovery consider a single domain with a fixed distribution.

Causal Discovery

Attainability and Optimality: The Equalized-Odds Fairness Revisited

no code implementations1 Jan 2021 Zeyu Tang, Kun Zhang

In this paper, focusing on the Equalized Odds notion of fairness, we consider the attainability of this criterion, and furthermore, if attainable, the optimality of the prediction performance under various settings.

Fairness

Learning Disentangled Semantic Representation for Domain Adaptation

1 code implementation22 Dec 2020 Ruichu Cai, Zijian Li, Pengfei Wei, Jie Qiao, Kun Zhang, Zhifeng Hao

Different from previous efforts on the entangled feature space, we aim to extract the domain invariant semantic information in the latent disentangled semantic representation (DSR) of the data.

Domain Adaptation