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20182025
most citedDomain Aggregation Networks for Multi-Source Domain Adaptation

26 citations · 55 across the 5 of their papers we have counts for

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5 papers · 1 filter

cs.LG20222 cited

Find Your Friends: Personalized Federated Learning with the Right Collaborators

Yi Sui, Junfeng Wen, Yenson Lau +2

In the traditional federated learning setting, a central server coordinates a network of clients to train one global model. However, the global model may serve many clients poorly…

cs.LG202011 cited

Batch Stationary Distribution Estimation

Junfeng Wen, Bo Dai, Lihong Li +1

We consider the problem of approximating the stationary distribution of an ergodic Markov chain given a set of sampled transitions. Classical simulation-based approaches assume acc…

cs.LG202011 cited

Universal Successor Features for Transfer Reinforcement Learning

Chen Ma, Dylan R. Ashley, Junfeng Wen +1

Transfer in Reinforcement Learning (RL) refers to the idea of applying knowledge gained from previous tasks to solving related tasks. Learning a universal value function (Schaul et…

cs.LG201926 cited

Domain Aggregation Networks for Multi-Source Domain Adaptation

Junfeng Wen, Russell Greiner, Dale Schuurmans

In many real-world applications, we want to exploit multiple source datasets of similar tasks to learn a model for a different but related target dataset -- e.g., recognizing chara…

cs.LG2018

Few-Shot Self Reminder to Overcome Catastrophic Forgetting

Junfeng Wen, Yanshuai Cao, Ruitong Huang

Deep neural networks are known to suffer the catastrophic forgetting problem, where they tend to forget the knowledge from the previous tasks when sequentially learning new tasks.…