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Found 10 papers, 4 papers with code
Streaming Graph Neural Networks
Current graph neural network models cannot utilize the dynamic information in dynamic graphs.
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)
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.
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.
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.
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).
On-Demand Sampling: Learning Optimally from Multiple Distributions
This improves upon the best known sample complexity bounds for fair federated learning by Mohri et al. and collaborative learning by Nguyen and Zakynthinou by multiplicative factors of $n$ and $\log(n)/\epsilon^3$, respectively.
The Sample Complexity of Multi-Distribution Learning for VC Classes
Multi-distribution learning is a natural generalization of PAC learning to settings with multiple data distributions.
Stacking as Accelerated Gradient Descent
Stacking, a heuristic technique for training deep residual networks by progressively increasing the number of layers and initializing new layers by copying parameters from older layers, has proven quite successful in improving the efficiency of training deep neural networks.
