Search Results for author: Chenglin Miao

Found 4 papers, 1 papers with code

Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning

no code implementations NeurIPS 2021 Xinyi Xu, Lingjuan Lyu, Xingjun Ma, Chenglin Miao, Chuan Sheng Foo, Bryan Kian Hsiang Low

In this paper, we adopt federated learning as a gradient-based formalization of collaborative machine learning, propose a novel cosine gradient Shapley value to evaluate the agents’ uploaded model parameter updates/gradients, and design theoretically guaranteed fair rewards in the form of better model performance.

BIG-bench Machine Learning Fairness +1

Revisiting Prioritized Experience Replay: A Value Perspective

2 code implementations5 Feb 2021 Ang A. Li, Zongqing Lu, Chenglin Miao

Furthermore, we successfully extend our theoretical framework to maximum-entropy RL by deriving the lower and upper bounds of these value metrics for soft Q-learning, which turn out to be the product of $|\text{TD}|$ and "on-policyness" of the experiences.

Atari Games Q-Learning +1

Data Poisoning Attack against Knowledge Graph Embedding

no code implementations26 Apr 2019 Hengtong Zhang, Tianhang Zheng, Jing Gao, Chenglin Miao, Lu Su, Yaliang Li, Kui Ren

Knowledge graph embedding (KGE) is a technique for learning continuous embeddings for entities and relations in the knowledge graph. Due to its benefit to a variety of downstream tasks such as knowledge graph completion, question answering and recommendation, KGE has gained significant attention recently.

Data Poisoning Knowledge Graph Completion +2

Towards Differentially Private Truth Discovery for Crowd Sensing Systems

no code implementations10 Oct 2018 Yaliang Li, Houping Xiao, Zhan Qin, Chenglin Miao, Lu Su, Jing Gao, Kui Ren, Bolin Ding

To better utilize sensory data, the problem of truth discovery, whose goal is to estimate user quality and infer reliable aggregated results through quality-aware data aggregation, has emerged as a hot topic.

Privacy Preserving

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