Search Results for author: Ruoming Jin

Found 21 papers, 4 papers with code

Scalable Differential Privacy with Certified Robustness in Adversarial Learning

1 code implementation ICML 2020 Hai Phan, My T. Thai, Han Hu, Ruoming Jin, Tong Sun, Dejing Dou

In this paper, we aim to develop a scalable algorithm to preserve differential privacy (DP) in adversarial learning for deep neural networks (DNNs), with certified robustness to adversarial examples.

AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices

no code implementations18 Dec 2023 Ji Liu, Tianshi Che, Yang Zhou, Ruoming Jin, Huaiyu Dai, Dejing Dou, Patrick Valduriez

First, we propose an asynchronous FL system model with an efficient model aggregation method for improving the FL convergence.

Federated Learning

(Debiased) Contrastive Learning Loss for Recommendation (Technical Report)

no code implementations13 Dec 2023 Ruoming Jin, Dong Li

In this paper, we perform a systemic examination of the recommendation losses, including listwise (softmax), pairwise(BPR), and pointwise (mean-squared error, MSE, and Cosine Contrastive Loss, CCL) losses through the lens of contrastive learning.

Contrastive Learning

Revisiting Recommendation Loss Functions through Contrastive Learning (Technical Report)

no code implementations13 Dec 2023 Dong Li, Ruoming Jin, Bin Ren

Inspired by the success of contrastive learning, we systematically examine recommendation losses, including listwise (softmax), pairwise (BPR), and pointwise (MSE and CCL) losses.

Contrastive Learning

Zone-based Federated Learning for Mobile Sensing Data

no code implementations10 Mar 2023 Xiaopeng Jiang, Thinh On, NhatHai Phan, Hessamaldin Mohammadi, Vijaya Datta Mayyuri, An Chen, Ruoming Jin, Cristian Borcea

However, currently there is no mobile sensing DL system that simultaneously achieves good model accuracy while adapting to user mobility behavior, scales well as the number of users increases, and protects user data privacy.

Federated Learning Human Activity Recognition

Generation-Augmented Query Expansion For Code Retrieval

no code implementations20 Dec 2022 Dong Li, Yelong Shen, Ruoming Jin, Yi Mao, Kuan Wang, Weizhu Chen

Pre-trained language models have achieved promising success in code retrieval tasks, where a natural language documentation query is given to find the most relevant existing code snippet.

Code Generation Retrieval

Towards Reliable Item Sampling for Recommendation Evaluation

no code implementations28 Nov 2022 Dong Li, Ruoming Jin, Zhenming Liu, Bin Ren, Jing Gao, Zhi Liu

Since Rendle and Krichene argued that commonly used sampling-based evaluation metrics are "inconsistent" with respect to the global metrics (even in expectation), there have been a few studies on the sampling-based recommender system evaluation.

Recommendation Systems

Lifelong DP: Consistently Bounded Differential Privacy in Lifelong Machine Learning

1 code implementation26 Jul 2022 Phung Lai, Han Hu, NhatHai Phan, Ruoming Jin, My T. Thai, An M. Chen

In this paper, we show that the process of continually learning new tasks and memorizing previous tasks introduces unknown privacy risks and challenges to bound the privacy loss.

BIG-bench Machine Learning

Scalable Deep Graph Clustering with Random-walk based Self-supervised Learning

no code implementations31 Dec 2021 Xiang Li, Dong Li, Ruoming Jin, Gagan Agrawal, Rajiv Ramnath

Though other methods (particularly those based on Laplacian Smoothing) have reported better accuracy, a fundamental limitation of all the work is a lack of scalability.

Clustering Deep Clustering +3

Validating the Lottery Ticket Hypothesis with Inertial Manifold Theory

no code implementations NeurIPS 2021 Zeru Zhang, Jiayin Jin, Zijie Zhang, Yang Zhou, Xin Zhao, Jiaxiang Ren, Ji Liu, Lingfei Wu, Ruoming Jin, Dejing Dou

Despite achieving remarkable efficiency, traditional network pruning techniques often follow manually-crafted heuristics to generate pruned sparse networks.

Network Pruning

FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps

no code implementations17 Nov 2021 Xiaopeng Jiang, Han Hu, Vijaya Datta Mayyuri, An Chen, Devu M. Shila, Adriaan Larmuseau, Ruoming Jin, Cristian Borcea, NhatHai Phan

This article presents the design, implementation, and evaluation of FLSys, a mobile-cloud federated learning (FL) system, which can be a key component for an open ecosystem of FL models and apps.

Data Augmentation Federated Learning +3

On the regularization landscape for the linear recommendation models

no code implementations29 Sep 2021 Dong Li, Zhenming Liu, Ruoming Jin, Zhi Liu, Jing Gao, Bin Ren

Recently, a wide range of recommendation algorithms inspired by deep learning techniques have emerged as the performance leaders several standard recommendation benchmarks.

On Sampling Top-K Recommendation Evaluation

no code implementations20 Jun 2021 Dong Li, Ruoming Jin, Jing Gao, Zhi Liu

Recently, Rendle has warned that the use of sampling-based top-$k$ metrics might not suffice.

Towards a Better Understanding of Linear Models for Recommendation

no code implementations27 May 2021 Ruoming Jin, Dong Li, Jing Gao, Zhi Liu, Li Chen, Yang Zhou

Through the derivation and analysis of the closed-form solutions for two basic regression and matrix factorization approaches, we found these two approaches are indeed inherently related but also diverge in how they "scale-down" the singular values of the original user-item interaction matrix.

regression

On Estimating Recommendation Evaluation Metrics under Sampling

no code implementations2 Mar 2021 Ruoming Jin, Dong Li, Benjamin Mudrak, Jing Gao, Zhi Liu

The proposed approaches either are rather uninformative (linking sampling to metric evaluation) or can only work on simple metrics, such as Recall/Precision (Krichene and Rendle 2020; Li et al. 2020).

Adversarial Attacks on Deep Graph Matching

no code implementations NeurIPS 2020 Zijie Zhang, Zeru Zhang, Yang Zhou, Yelong Shen, Ruoming Jin, Dejing Dou

Despite achieving remarkable performance, deep graph learning models, such as node classification and network embedding, suffer from harassment caused by small adversarial perturbations.

Adversarial Attack Density Estimation +5

Differential Privacy in Adversarial Learning with Provable Robustness

no code implementations25 Sep 2019 NhatHai Phan, My T. Thai, Ruoming Jin, Han Hu, Dejing Dou

In this paper, we aim to develop a novel mechanism to preserve differential privacy (DP) in adversarial learning for deep neural networks, with provable robustness to adversarial examples.

Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness

4 code implementations2 Jun 2019 NhatHai Phan, Minh Vu, Yang Liu, Ruoming Jin, Dejing Dou, Xintao Wu, My T. Thai

In this paper, we propose a novel Heterogeneous Gaussian Mechanism (HGM) to preserve differential privacy in deep neural networks, with provable robustness against adversarial examples.

Preserving Differential Privacy in Adversarial Learning with Provable Robustness

no code implementations23 Mar 2019 NhatHai Phan, My T. Thai, Ruoming Jin, Han Hu, Dejing Dou

In this paper, we aim to develop a novel mechanism to preserve differential privacy (DP) in adversarial learning for deep neural networks, with provable robustness to adversarial examples.

Cryptography and Security

A Deep Embedding Model for Co-occurrence Learning

no code implementations11 Apr 2015 Yelong Shen, Ruoming Jin, Jianshu Chen, Xiaodong He, Jianfeng Gao, Li Deng

Co-occurrence Data is a common and important information source in many areas, such as the word co-occurrence in the sentences, friends co-occurrence in social networks and products co-occurrence in commercial transaction data, etc, which contains rich correlation and clustering information about the items.

Clustering

Axiomatic Ranking of Network Role Similarity

4 code implementations18 Feb 2011 Ruoming Jin, Victor E. Lee, Hui Hong

For the first problem, we justify several axiomatic properties necessary for a role similarity measure or metric: range, maximal similarity, automorphic equivalence, transitive similarity, and the triangle inequality.

Social and Information Networks Physics and Society H.2.8

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