Search Results for author:
Found 9 papers, 4 papers with code
A Unifying Perspective on Multi-Calibration: Unleashing Game Dynamics for Multi-Objective Learning
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
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.
Scaling Fair Learning to Hundreds of Intersectional Groups
In real-world applications, however, there are multiple protected attributes yielding a large number of intersectional protected groups.
Learning to Play General-Sum Games Against Multiple Boundedly Rational Agents
We study the problem of training a principal in a multi-agent general-sum game using reinforcement learning (RL).
A unified Neural Network Approach to E-CommerceRelevance Learning
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.
ERMAS: Learning Policies Robust to Reality Gaps in Multi-Agent Simulations
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
We address the problem of active learning under label shift: when the class proportions of source and target domains differ.
Graph Neural Networks for Social Recommendation
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)
Streaming Graph Neural Networks
Current graph neural network models cannot utilize the dynamic information in dynamic graphs.
