Search Results for author: Eric Zhao

Found 9 papers, 4 papers with code

A Unifying Perspective on Multi-Calibration: Unleashing Game Dynamics for Multi-Objective Learning

no code implementations21 Feb 2023 Nika Haghtalab, Michael I. Jordan, Eric Zhao

We provide a unifying framework for the design and analysis of multi-calibrated and moment-multi-calibrated predictors.


On-Demand Sampling: Learning Optimally from Multiple Distributions

1 code implementation22 Oct 2022 Nika Haghtalab, Michael I. Jordan, Eric Zhao

Social and real-world considerations such as robustness, fairness, social welfare and multi-agent tradeoffs have given rise to multi-distribution learning paradigms, such as collaborative, group distributionally robust, and fair federated learning.

Fairness Federated Learning +1

Scaling Fair Learning to Hundreds of Intersectional Groups

no code implementations29 Sep 2021 Eric Zhao, De-An Huang, Hao liu, Zhiding Yu, Anqi Liu, Olga Russakovsky, Anima Anandkumar

In real-world applications, however, there are multiple protected attributes yielding a large number of intersectional protected groups.

Fairness Knowledge Distillation

A unified Neural Network Approach to E-CommerceRelevance Learning

no code implementations26 Apr 2021 Yunjiang Jiang, Yue Shang, Rui Li, Wen-Yun Yang, Guoyu Tang, Chaoyi Ma, Yun Xiao, Eric Zhao

We describe a highly-scalable feed-forward neural model to provide relevance score for (query, item) pairs, using only user query and item title as features, and both user click feedback as well as limited human ratings as labels.

Information Retrieval Retrieval

ERMAS: Learning Policies Robust to Reality Gaps in Multi-Agent Simulations

no code implementations1 Jan 2021 Eric Zhao, Alexander R Trott, Caiming Xiong, Stephan Zheng

Policies for real-world multi-agent problems, such as optimal taxation, can be learned in multi-agent simulations with AI agents that emulate humans.


Active Learning under Label Shift

no code implementations16 Jul 2020 Eric Zhao, Anqi Liu, Animashree Anandkumar, Yisong Yue

We address the problem of active learning under label shift: when the class proportions of source and target domains differ.

Active Learning

Graph Neural Networks for Social Recommendation

7 code implementations19 Feb 2019 Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin

These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user social graph and user-item graph; and learning latent factors of users and items is the key.

Ranked #3 on Recommendation Systems on Epinions (using extra training data)

Recommendation Systems

Streaming Graph Neural Networks

2 code implementations24 Oct 2018 Yao Ma, Ziyi Guo, Zhaochun Ren, Eric Zhao, Jiliang Tang, Dawei Yin

Current graph neural network models cannot utilize the dynamic information in dynamic graphs.

Community Detection General Classification +3

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