Search Results for author: Chen Ma

Found 50 papers, 22 papers with code

Rankability-enhanced Revenue Uplift Modeling Framework for Online Marketing

no code implementations24 May 2024 Bowei He, Yunpeng Weng, Xing Tang, Ziqiang Cui, Zexu Sun, Liang Chen, Xiuqiang He, Chen Ma

Uplift modeling has been widely employed in online marketing by predicting the response difference between the treatment and control groups, so as to identify the sensitive individuals toward interventions like coupons or discounts.

Diffusion-based Contrastive Learning for Sequential Recommendation

no code implementations15 May 2024 Ziqiang Cui, Haolun Wu, Bowei He, Ji Cheng, Chen Ma

Given a user sequence, our method selects certain positions and employs a context-aware diffusion model to generate alternative items for these positions with the guidance of context information.

Contrastive Learning Sequential Recommendation

An Empty Room is All We Want: Automatic Defurnishing of Indoor Panoramas

no code implementations6 May 2024 Mira Slavcheva, Dave Gausebeck, Kevin Chen, David Buchhofer, Azwad Sabik, Chen Ma, Sachal Dhillon, Olaf Brandt, Alan Dolhasz

We propose a pipeline that leverages Stable Diffusion to improve inpainting results in the context of defurnishing -- the removal of furniture items from indoor panorama images.

Room Layout Estimation

A Survey on the Memory Mechanism of Large Language Model based Agents

1 code implementation21 Apr 2024 Zeyu Zhang, Xiaohe Bo, Chen Ma, Rui Li, Xu Chen, Quanyu Dai, Jieming Zhu, Zhenhua Dong, Ji-Rong Wen

Compared with original LLMs, LLM-based agents are featured in their self-evolving capability, which is the basis for solving real-world problems that need long-term and complex agent-environment interactions.

Language Modelling Large Language Model

Treatment-Aware Hyperbolic Representation Learning for Causal Effect Estimation with Social Networks

1 code implementation12 Jan 2024 Ziqiang Cui, Xing Tang, Yang Qiao, Bowei He, Liang Chen, Xiuqiang He, Chen Ma

Firstly, TAHyper employs the hyperbolic space to encode the social networks, thereby effectively reducing the distortion of confounder representation caused by Euclidean embeddings.

Representation Learning

Less or More From Teacher: Exploiting Trilateral Geometry For Knowledge Distillation

no code implementations22 Dec 2023 Chengming Hu, Haolun Wu, Xuan Li, Chen Ma, Xi Chen, Jun Yan, Boyu Wang, Xue Liu

A simple neural network then learns the implicit mapping from the intra- and inter-sample relations to an adaptive, sample-wise knowledge fusion ratio in a bilevel-optimization manner.

Bilevel Optimization Click-Through Rate Prediction +2

SlowTrack: Increasing the Latency of Camera-based Perception in Autonomous Driving Using Adversarial Examples

no code implementations15 Dec 2023 Chen Ma, Ningfei Wang, Qi Alfred Chen, Chao Shen

Our evaluation results show that the system-level effects can be significantly improved, i. e., the vehicle crash rate of SlowTrack is around 95% on average while existing works only have around 30%.

Autonomous Driving object-detection +1

Large Language Models as Topological Structure Enhancers for Text-Attributed Graphs

no code implementations24 Nov 2023 Shengyin Sun, Yuxiang Ren, Chen Ma, Xuecang Zhang

The latest advancements in large language models (LLMs) have revolutionized the field of natural language processing (NLP).

Graph Learning Information Retrieval +6

Towards Hybrid-grained Feature Interaction Selection for Deep Sparse Network

1 code implementation NeurIPS 2023 Fuyuan Lyu, Xing Tang, Dugang Liu, Chen Ma, Weihong Luo, Liang Chen, Xiuqiang He, Xue Liu

In this work, we introduce a hybrid-grained feature interaction selection approach that targets both feature field and feature value for deep sparse networks.

Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitization

no code implementations7 Oct 2023 Zexu Sun, Bowei He, Ming Ma, Jiakai Tang, Yuchen Wang, Chen Ma, Dugang Liu

Specifically, our RUAD can more effectively alleviate the feature sensitivity of the uplift model through two customized modules, including a feature selection module with joint multi-label modeling to identify a key subset from the input features and an adversarial feature desensitization module using adversarial training and soft interpolation operations to enhance the robustness of the model against this selected subset of features.

feature selection Marketing

A Survey on Large Language Model based Autonomous Agents

2 code implementations22 Aug 2023 Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, ZhiYuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, Wayne Xin Zhao, Zhewei Wei, Ji-Rong Wen

In this paper, we present a comprehensive survey of these studies, delivering a systematic review of the field of LLM-based autonomous agents from a holistic perspective.

Language Modelling Large Language Model

Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System

no code implementations15 Aug 2023 Bowei He, Xu He, Renrui Zhang, Yingxue Zhang, Ruiming Tang, Chen Ma

The high-throughput data requires the model to be updated in a timely manner for capturing the user interest dynamics, which leads to the emergence of streaming recommender systems.

Recommendation Systems

Mutually Guided Few-shot Learning for Relational Triple Extraction

1 code implementation23 Jun 2023 Chengmei Yang, Shuai Jiang, Bowei He, Chen Ma, Lianghua He

Specifically, our method consists of an entity-guided relation proto-decoder to classify the relations firstly and a relation-guided entity proto-decoder to extract entities based on the classified relations.

Cross-Domain Few-Shot Decoder +3

Sim2Rec: A Simulator-based Decision-making Approach to Optimize Real-World Long-term User Engagement in Sequential Recommender Systems

1 code implementation3 May 2023 Xiong-Hui Chen, Bowei He, Yang Yu, Qingyang Li, Zhiwei Qin, Wenjie Shang, Jieping Ye, Chen Ma

However, building a user simulator with no reality-gap, i. e., can predict user's feedback exactly, is unrealistic because the users' reaction patterns are complex and historical logs for each user are limited, which might mislead the simulator-based recommendation policy.

Decision Making Recommendation Systems +1

Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems

no code implementations2 May 2023 Yuening Wang, Yingxue Zhang, Antonios Valkanas, Ruiming Tang, Chen Ma, Jianye Hao, Mark Coates

In contrast, for users who have static preferences, model performance can benefit greatly from preserving as much of the user's long-term preferences as possible.

Incremental Learning Knowledge Distillation +1

Dynamically Expandable Graph Convolution for Streaming Recommendation

1 code implementation21 Mar 2023 Bowei He, Xu He, Yingxue Zhang, Ruiming Tang, Chen Ma

Personalized recommender systems have been widely studied and deployed to reduce information overload and satisfy users' diverse needs.

Graph Learning Recommendation Systems

Result Diversification in Search and Recommendation: A Survey

1 code implementation29 Dec 2022 Haolun Wu, Yansen Zhang, Chen Ma, Fuyuan Lyu, Bowei He, Bhaskar Mitra, Xue Liu

Diversifying return results is an important research topic in retrieval systems in order to satisfy both the various interests of customers and the equal market exposure of providers.


Hyperspherical Quantization: Toward Smaller and More Accurate Models

no code implementations24 Dec 2022 Dan Liu, Xi Chen, Chen Ma, Xue Liu

Model quantization enables the deployment of deep neural networks under resource-constrained devices.


Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation

no code implementations11 Nov 2022 Haolun Wu, Yingxue Zhang, Chen Ma, Wei Guo, Ruiming Tang, Xue Liu, Mark Coates

To offer accurate and diverse recommendation services, recent methods use auxiliary information to foster the learning process of user and item representations.

Decision Making Recommendation Systems +2

Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation

1 code implementation2 Aug 2022 Haolun Wu, Chen Ma, Yingxue Zhang, Xue Liu, Ruiming Tang, Mark Coates

In order to effectively utilize such information, most research adopts the pairwise ranking method on constructed training triplets (user, positive item, negative item) and aims to distinguish between positive items and negative items for each user.

Bilevel Optimization

Gradient-based Bi-level Optimization for Deep Learning: A Survey

no code implementations24 Jul 2022 Can Chen, Xi Chen, Chen Ma, Zixuan Liu, Xue Liu

In this survey, we first give a formal definition of the gradient-based bi-level optimization.

Hyperparameter Optimization

Unbiased Implicit Feedback via Bi-level Optimization

no code implementations31 May 2022 Can Chen, Chen Ma, Xi Chen, Sirui Song, Hao liu, Xue Liu

Recent works reveal a huge gap between the implicit feedback and user-item relevance due to the fact that implicit feedback is also closely related to the item exposure.

Recommendation Systems

Joint Multisided Exposure Fairness for Recommendation

1 code implementation29 Apr 2022 Haolun Wu, Bhaskar Mitra, Chen Ma, Fernando Diaz, Xue Liu

Prior research on exposure fairness in the context of recommender systems has focused mostly on disparities in the exposure of individual or groups of items to individual users of the system.

Exposure Fairness Information Retrieval +2

Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks

1 code implementation NeurIPS 2021 Chen Ma, Xiangyu Guo, Li Chen, Jun-Hai Yong, Yisen Wang

In this paper, we propose a novel geometric-based approach called Tangent Attack (TA), which identifies an optimal tangent point of a virtual hemisphere located on the decision boundary to reduce the distortion of the attack.

Hard-label Attack

Pruning Ternary Quantization

no code implementations23 Jul 2021 Dan Liu, Xi Chen, Jie Fu, Chen Ma, Xue Liu

To simultaneously optimize bit-width, model size, and accuracy, we propose pruning ternary quantization (PTQ): a simple, effective, symmetric ternary quantization method.

Image Classification Model Compression +3

Multi-FR: A Multi-objective Optimization Framework for Multi-stakeholder Fairness-aware Recommendation

no code implementations6 May 2021 Haolun Wu, Chen Ma, Bhaskar Mitra, Fernando Diaz, Xue Liu

To address these limitations, we propose a multi-objective optimization framework for fairness-aware recommendation, Multi-FR, that adaptively balances accuracy and fairness for various stakeholders with Pareto optimality guarantee.

Fairness Recommendation Systems

TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion

1 code implementation17 Apr 2021 Jiapeng Wu, Yishi Xu, Yingxue Zhang, Chen Ma, Mark Coates, Jackie Chi Kit Cheung

The model has to adapt to changes in the TKG for efficient training and inference while preserving its performance on historical knowledge.

Decision Making Information Retrieval +4

Knowledge-Enhanced Top-K Recommendation in Poincaré Ball

no code implementations13 Jan 2021 Chen Ma, Liheng Ma, Yingxue Zhang, Haolun Wu, Xue Liu, Mark Coates

To effectively make use of the knowledge graph, we propose a recommendation model in the hyperbolic space, which facilitates the learning of the hierarchical structure of knowledge graphs.

Knowledge Graphs Recommendation Systems

Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation

no code implementations13 Jan 2021 Chen Ma, Liheng Ma, Yingxue Zhang, Ruiming Tang, Xue Liu, Mark Coates

Personalized recommender systems are playing an increasingly important role as more content and services become available and users struggle to identify what might interest them.

Metric Learning Recommendation Systems

Switching Transferable Gradient Directions for Query-Efficient Black-Box Adversarial Attacks

no code implementations15 Sep 2020 Chen Ma, Shuyu Cheng, Li Chen, Jun Zhu, Junhai Yong

In each iteration, SWITCH first tries to update the current sample along the direction of $\hat{\mathbf{g}}$, but considers switching to its opposite direction $-\hat{\mathbf{g}}$ if our algorithm detects that it does not increase the value of the attack objective function.

Adversarial Attack

Simulating Unknown Target Models for Query-Efficient Black-box Attacks

1 code implementation CVPR 2021 Chen Ma, Li Chen, Jun-Hai Yong

The meta-gradients of this loss are then computed and accumulated from multiple tasks to update the Simulator and subsequently improve generalization.

Knowledge Distillation Meta-Learning

Feature Statistics Guided Efficient Filter Pruning

no code implementations21 May 2020 Hang Li, Chen Ma, Wei Xu, Xue Liu

Building compact convolutional neural networks (CNNs) with reliable performance is a critical but challenging task, especially when deploying them in real-world applications.

Universal Successor Features for Transfer Reinforcement Learning

no code implementations ICLR 2019 Chen Ma, Dylan R. Ashley, Junfeng Wen, Yoshua Bengio

Transfer in Reinforcement Learning (RL) refers to the idea of applying knowledge gained from previous tasks to solving related tasks.

reinforcement-learning Reinforcement Learning (RL) +1

Multi-Graph Convolution Collaborative Filtering

no code implementations1 Jan 2020 Jianing Sun, Yingxue Zhang, Chen Ma, Mark Coates, Huifeng Guo, Ruiming Tang, Xiuqiang He

In this work, we develop a graph convolution-based recommendation framework, named Multi-Graph Convolution Collaborative Filtering (Multi-GCCF), which explicitly incorporates multiple graphs in the embedding learning process.

Collaborative Filtering

Memory Augmented Graph Neural Networks for Sequential Recommendation

1 code implementation26 Dec 2019 Chen Ma, Liheng Ma, Yingxue Zhang, Jianing Sun, Xue Liu, Mark Coates

In addition to the modeling of user interests, we employ a bilinear function to capture the co-occurrence patterns of related items.

Sequential Recommendation

Learning to Combat Compounding-Error in Model-Based Reinforcement Learning

no code implementations24 Dec 2019 Chenjun Xiao, Yifan Wu, Chen Ma, Dale Schuurmans, Martin Müller

Despite its potential to improve sample complexity versus model-free approaches, model-based reinforcement learning can fail catastrophically if the model is inaccurate.

Model-based Reinforcement Learning reinforcement-learning +1

The Hitchhiker's Guide to LDA

2 code implementations7 Aug 2019 Chen Ma

Latent Dirichlet Allocation (LDA) model is a famous model in the topic model field, it has been studied for years due to its extensive application value in industry and academia.

MetaAdvDet: Towards Robust Detection of Evolving Adversarial Attacks

1 code implementation6 Aug 2019 Chen Ma, Chenxu Zhao, Hailin Shi, Li Chen, Junhai Yong, Dan Zeng

To solve such few-shot problem with the evolving attack, we propose a meta-learning based robust detection method to detect new adversarial attacks with limited examples.

Adversarial Attack Detection Meta-Learning

Hierarchical Gating Networks for Sequential Recommendation

2 code implementations21 Jun 2019 Chen Ma, Peng Kang, Xue Liu

However, with the tremendous increase of users and items, sequential recommender systems still face several challenging problems: (1) the hardness of modeling the long-term user interests from sparse implicit feedback; (2) the difficulty of capturing the short-term user interests given several items the user just accessed.

 Ranked #1 on Recommendation Systems on Amazon-CDs (Recall@10 metric)

Sequential Recommendation

AU R-CNN: Encoding Expert Prior Knowledge into R-CNN for Action Unit Detection

2 code implementations14 Dec 2018 Chen Ma, Li Chen, Junhai Yong

(2) We integrate various dynamic models (including convolutional long short-term memory, two stream network, conditional random field, and temporal action localization network) into AU R-CNN and then investigate and analyze the reason behind the performance of dynamic models.

Action Unit Detection Temporal Action Localization

Gated Attentive-Autoencoder for Content-Aware Recommendation

1 code implementation7 Dec 2018 Chen Ma, Peng Kang, Bin Wu, Qinglong Wang, Xue Liu

In particular, a word-level and a neighbor-level attention module are integrated with the autoencoder.

Product Recommendation Recommendation Systems

Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence

1 code implementation27 Sep 2018 Chen Ma, Yingxue Zhang, Qinglong Wang, Xue Liu

To incorporate the geographical context information, we propose a neighbor-aware decoder to make users' reachability higher on the similar and nearby neighbors of checked-in POIs, which is achieved by the inner product of POI embeddings together with the radial basis function (RBF) kernel.

Decoder Recommendation Systems

Universal Successor Representations for Transfer Reinforcement Learning

no code implementations11 Apr 2018 Chen Ma, Junfeng Wen, Yoshua Bengio

The objective of transfer reinforcement learning is to generalize from a set of previous tasks to unseen new tasks.

reinforcement-learning Reinforcement Learning (RL) +1

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