454 citations · 5.5k across the 247 of their papers we have counts for
34 papers · 1 filter
Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics
Johannes Ackermann, Volker Gabler, Takayuki Osa +1
Many real world tasks require multiple agents to work together. Multi-agent reinforcement learning (RL) methods have been proposed in recent years to solve these tasks, but current…
Learning with Multiple Complementary Labels
Lei Feng, Takuo Kaneko, Bo Han +3
A complementary label (CL) simply indicates an incorrect class of an example, but learning with CLs results in multi-class classifiers that can predict the correct class. Unfortuna…
Where is the Bottleneck of Adversarial Learning with Unlabeled Data?
Jingfeng Zhang, Bo Han, Gang Niu +2
Deep neural networks (DNNs) are incredibly brittle due to adversarial examples. To robustify DNNs, adversarial training was proposed, which requires large-scale but well-labeled da…
Learning Only from Relevant Keywords and Unlabeled Documents
Nontawat Charoenphakdee, Jongyeong Lee, Yiping Jin +2
We consider a document classification problem where document labels are absent but only relevant keywords of a target class and unlabeled documents are given. Although heuristic me…
A unified view of likelihood ratio and reparameterization gradients and an optimal importance sampling scheme
Paavo Parmas, Masashi Sugiyama
Reparameterization (RP) and likelihood ratio (LR) gradient estimators are used throughout machine and reinforcement learning; however, they are usually explained as simple mathemat…
Learning from Indirect Observations
Yivan Zhang, Nontawat Charoenphakdee, Masashi Sugiyama
Weakly-supervised learning is a paradigm for alleviating the scarcity of labeled data by leveraging lower-quality but larger-scale supervision signals. While existing work mainly f…