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Junyuan Xie

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13 papers

2 author rows

AAAI Conference 2023 Conference Paper

DPAUC: Differentially Private AUC Computation in Federated Learning

Jiankai Sun
Xin Yang
Yuanshun Yao
Junyuan Xie
Di Wu
Chong Wang

Federated learning (FL) has gained significant attention recently as a privacy-enhancing tool to jointly train a machine learning model by multiple participants. The prior work on FL has mostly studied how to protect label privacy during model training. However, model evaluation in FL might also lead to the potential leakage of private label information. In this work, we propose an evaluation algorithm that can accurately compute the widely used AUC (area under the curve) metric when using the label differential privacy (DP) in FL. Through extensive experiments, we show our algorithms can compute accurate AUCs compared to the ground truth. The code is available at https://github.com/bytedance/fedlearner/tree/master/example/privacy/DPAUC

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ICLR Conference 2022 Conference Paper

Label Leakage and Protection in Two-party Split Learning

Oscar Li
Jiankai Sun
Xin Yang 0017
Weihao Gao
Hongyi Zhang
Junyuan Xie
Virginia Smith
Chong Wang 0002

Two-party split learning is a popular technique for learning a model across feature-partitioned data. In this work, we explore whether it is possible for one party to steal the private label information from the other party during split training, and whether there are methods that can protect against such attacks. Specifically, we first formulate a realistic threat model and propose a privacy loss metric to quantify label leakage in split learning. We then show that there exist two simple yet effective methods within the threat model that can allow one party to accurately recover private ground-truth labels owned by the other party. To combat these attacks, we propose several random perturbation techniques, including $\texttt{Marvell}$, an approach that strategically finds the structure of the noise perturbation by minimizing the amount of label leakage (measured through our quantification metric) of a worst-case adversary. We empirically demonstrate the effectiveness of our protection techniques against the identified attacks, and show that $\texttt{Marvell}$ in particular has improved privacy-utility tradeoffs relative to baseline approaches.

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JMLR Journal 2020 Journal Article

GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing

Jian Guo
He He
Tong He
Leonard Lausen
Mu Li
Haibin Lin
Xingjian Shi
Chenguang Wang

We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-the-art pre-trained models, training scripts, and training logs, to facilitate rapid prototyping and promote reproducible research. We also provide modular APIs with flexible building blocks to enable efficient customization. Leveraging the MXNet ecosystem, the deep learning models in GluonCV and GluonNLP can be deployed onto a variety of platforms with different programming languages. The Apache 2.0 license has been adopted by GluonCV and GluonNLP to allow for software distribution, modification, and usage. [abs] [ pdf ][ bib ] &copy JMLR 2020. ( edit, beta )

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AAMAS Conference 2019 Conference Paper

Multi-unit Budget Feasible Mechanisms for Cellular Traffic Offloading

Jun Wu
Yuan Zhang
Yu Qiao
Lei Zhang
Chongjun Wang
Junyuan Xie

Cellular traffic offloading is nowadays an important problem in mobile networking. Since the offloading resource owners (agents) are self-interested and have private costs, it is highly challenging to design procurement mechanisms that motivate agents to reveal their true costs and achieve guaranteed performance under the constraint of a strict budget. In this paper, we model cellular traffic offloading as a multi-unit budget feasible procurement auction design problem with diminishing return valuations. We design a novel greedy-based randomized mechanism, and prove it is budget-feasible, truthful, individually rational and a (3 + 2 ln 𝑁)-approximation, where 𝑁 is the total number of available resource units. We also propose a deterministic mechanism which achieves (2 + ln 𝑁 + √︀ 2 + 3 ln 𝑁 + ln2 𝑁) - approximation. We prove no budget-feasible and truthful mechanism can do better than ln 𝑁-approximation in our setting, thus our mechanism approaches the optimal to a constant factor. In addition to solving the cellular traffic offloading problem, our work successfully extends solvable valuation class of greedy-based multi-unit budget-feasible mechanism with performance guarantees from the concave-additive valuations to more general local diminishing return valuations.

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UAI Conference 2018 Conference Paper

GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs

Jiani Zhang 0001
Xingjian Shi
Junyuan Xie
Hao Ma 0001
Irwin King
Dit-Yan Yeung

We propose a new network architecture, Gated Attention Networks (GaAN), for learning on graphs. Unlike the traditional multi-head attention mechanism, which equally consumes all attention heads, GaAN uses a convolutional sub-network to control each attention head’s importance. We demonstrate the effectiveness of GaAN on the inductive node classification problem on large graphs. Moreover, with GaAN as a building block, we construct the Graph Gated Recurrent Unit (GGRU) to address the traffic speed forecasting problem. Extensive experiments on three realworld datasets show that our GaAN framework achieves state-of-the-art results on both tasks.

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AAAI Conference 2018 Conference Paper

Multi-Entity Aspect-Based Sentiment Analysis With Context, Entity and Aspect Memory

Jun Yang
Runqi Yang
Chongjun Wang
Junyuan Xie

Inspired by recent works in Aspect-Based Sentiment Analysis(ABSA) on product reviews and faced with more complex posts on social media platforms mentioning multiple entities as well as multiple aspects, we deﬁne a novel task called Multi-Entity Aspect-Based Sentiment Analysis (ME-ABSA). This task aims at ﬁne-grained sentiment analysis of (entity, aspect) combinations, making the well-studied ABSA task a special case of it. To address the task, we propose an innovative method that models Context memory, Entity memory and Aspect memory, called CEA method. Our experimental results show that our CEA method achieves a signiﬁcant gain over several baselines, including the state-of-the-art method for the ABSA task, and their enhanced versions, on datasets for ME-ABSA and ABSA tasks. The in-depth analysis illustrates the signiﬁcant advantage of the CEA method over baseline methods for several hard-to-predict post types. Furthermore, we show that the CEA method is capable of generalizing to new (entity, aspect) combinations with little loss of accuracy. This observation indicates that data annotation in real applications can be largely simpliﬁed.

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AAMAS Conference 2017 Conference Paper

Mechanism Design for Social Law Synthesis under Incomplete Information

Jun Wu
Lei Zhang
Chongjun Wang
Junyuan Xie

For the social law synthesis problem, when the agents are rational in the sense of game theory and hold some information we need as private information, it naturally evolves into a setting that is perfectly addressed by the framework of algorithmic mechanism design. In this strategic setting, we are not only required to ﬁnd out the feasible social law for the objective, but also required to formulate the right payment to the agents to induce incentive compatibility and individual rationality. We design a mechanism for this setting, prove that it satisﬁes all the required formal properties, and characterize the conditions for the existence of feasible mechanisms. Moreover, we show that the upper-bound of the total payment of the proposed mechanism is high.

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AAMAS Conference 2017 Conference Paper

Synthesizing Optimal Social Laws for Strategical Agents via Bayesian Mechanism Design

Jun Wu
Lei Zhang
Chongjun Wang
Junyuan Xie

When rational behavior of the agents and private information are considered, the optimal social law synthesizing problem naturally evolves into a setting which can be handled by the framework of algorithmic mechanism design. We focus on the Bayesian case in this paper, that is, the probability distribution of each agent’s cost is known. It is easy to see that in this case our problem closely relates to path/spanning-tree auctions and Myerson’s optimal auction mechanism, but the optimization objective is new, that is, we focus on profit maximization instead of payment maximization. By studying this problem: we further extend the logic-based framework of social law optimization problem to the strategic case, and show that it becomes a new problem of algorithmic mechanism design; we find out a mechanism that is incentive compatible, individually rational and maximizes the expected profit for all input cost profiles; however, we can show that this mechanism is computational intractable; so, we finally find out a tractable constant-factor approximation mechanism. CCS Concepts •Computing methodologies → Multi-agent systems;

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ECAI Conference 2016 Conference Paper

False-Name-Proof Mechanisms for Path Auctions in Social Networks

Lei Zhang 0086
Haibin Chen
Jun Wu 0015
Chongjun Wang
Junyuan Xie

We study path auction mechanisms for buying path between two given nodes in a social network, where edges are owned by strategic agents. The well known VCG mechanism is the unique solution that guarantees both truthfulness and efficiency. However, in social network environments, the mechanism is vulnerable to false-name manipulations where agents can profit from placing multiple bids under fictitious names. Moreover, the VCG mechanism often leads to high overpayment. In this paper, we present core-selecting path mechanisms that are robust against false-name bids and address the overpayment problem. Specifically, we provide a new formulation for the core, which greatly reduces the number of core constraints. Based on the new formulation, we present a Vickery-nearest pricing rule, which finds the core payment profile that minimizes the L∞ distance to the VCG payment profile. We prove that the Vickery-nearest core payments can be computed in polynomial time by solving linear programs. Our experiment results on real network datasets and reported cost dataset show that our Vickery-nearest core-selecting path mechanism can reduce VCG's overpayment by about 20%.

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ICML Conference 2016 Conference Paper

Unsupervised Deep Embedding for Clustering Analysis

Junyuan Xie
Ross B. Girshick
Ali Farhadi

Clustering is central to many data-driven application domains and has been studied extensively in terms of distance functions and grouping algorithms. Relatively little work has focused on learning representations for clustering. In this paper, we propose Deep Embedded Clustering (DEC), a method that simultaneously learns feature representations and cluster assignments using deep neural networks. DEC learns a mapping from the data space to a lower-dimensional feature space in which it iteratively optimizes a clustering objective. Our experimental evaluations on image and text corpora show significant improvement over state-of-the-art methods.

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AAMAS Conference 2013 Conference Paper

On the Complexity of Undominated Core and Farsighted Solution Concepts in Coalitional Games

Yusen Zhan
Jun Wu
Chongjun Wang
Meilin Liu
Junyuan Xie

In this paper, we study the computational complexity of solution concepts in the context of coalitional games. Firstly, we distinguish two diﬀerent kinds of core, the undominated core and excess core, and investigate the diﬀerence and relationship between them. Secondly, we thoroughly investigate the computational complexity of undominated core and three farsighted solution concepts—farsighted core, farsighted stable set and largest consistent set.

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NeurIPS Conference 2012 Conference Paper

Image Denoising and Inpainting with Deep Neural Networks

Junyuan Xie
Linli Xu
Enhong Chen

We present a novel approach to low-level vision problems that combines sparse coding and deep networks pre-trained with denoising auto-encoder (DA). We propose an alternative training scheme that successfully adapts DA, originally designed for unsupervised feature learning, to the tasks of image denoising and blind inpainting. Our method achieves state-of-the-art performance in the image denoising task. More importantly, in blind image inpainting task, the proposed method provides solutions to some complex problems that have not been tackled before. Specifically, we can automatically remove complex patterns like superimposed text from an image, rather than simple patterns like pixels missing at random. Moreover, the proposed method does not need the information regarding the region that requires inpainting to be given a priori. Experimental results demonstrate the effectiveness of the proposed method in the tasks of image denoising and blind inpainting. We also show that our new training scheme for DA is more effective and can improve the performance of unsupervised feature learning.

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AAMAS Conference 2011 Conference Paper

), which can selectively restrict the joint behavior of a coalition, in this paper. We extend the semantics of atl and propose Coordinated atl ( co-atl ) to support the formalizing of cns. We soundly and completely characterize the limitation of the normative power of a coalition by identifying two fragments of col-atl language corresponding to two types of system properties that are unchangeable by restricting the joint behavior of such a coalition. Then, we prove that the effectiveness checking, feasibility and synthesis problems of cns are ptime -complete, cp -complete and fnp -complete, respectively. Moreover, we define two concepts of optimality for cns, that is, minimality and compactness, and prove that both minimality checking and compactness checking are conp -complete while the problem of checking whether a coalition is a minimal controllable coalition is dp -complete. The relation between ns and cns is discussed, and it turns out that css intrinsically consists of a proper subset of cns s and some basic problems related to cns are no more complex than that of ns. A Framework for Coalitional Normative Systems

Jun Wu
Chongjun Wang
Junyuan Xie

We propose coalitional normative system ( cns

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