4 citations · 7 across the 15 of their papers we have counts for
15 papers
Drugs Resistance Analysis from Scarce Health Records via Multi-task Graph Representation
Honglin Shu, Pei Gao, Lingwei Zhu +1
Clinicians prescribe antibiotics by looking at the patient's health record with an experienced eye. However, the therapy might be rendered futile if the patient has drug resistance…
Generalized Munchausen Reinforcement Learning using Tsallis KL Divergence
Lingwei Zhu, Zheng Chen, Matthew Schlegel +1
Many policy optimization approaches in reinforcement learning incorporate a Kullback-Leilbler (KL) divergence to the previous policy, to prevent the policy from changing too quickl…
Cancer Subtyping by Improved Transcriptomic Features Using Vector Quantized Variational Autoencoder
Zheng Chen, Ziwei Yang, Lingwei Zhu +5
Defining and separating cancer subtypes is essential for facilitating personalized therapy modality and prognosis of patients. The definition of subtypes has been constantly recali…
Cyclic Policy Distillation: Sample-Efficient Sim-to-Real Reinforcement Learning with Domain Randomization
Yuki Kadokawa, Lingwei Zhu, Yoshihisa Tsurumine +1
Deep reinforcement learning with domain randomization learns a control policy in various simulations with randomized physical and sensor model parameters to become transferable to…
Automated Cancer Subtyping via Vector Quantization Mutual Information Maximization
Zheng Chen, Lingwei Zhu, Ziwei Yang +1
Cancer subtyping is crucial for understanding the nature of tumors and providing suitable therapy. However, existing labelling methods are medically controversial, and have driven…
Enforcing KL Regularization in General Tsallis Entropy Reinforcement Learning via Advantage Learning
Lingwei Zhu, Zheng Chen, Eiji Uchibe +1
Maximum Tsallis entropy (MTE) framework in reinforcement learning has gained popularity recently by virtue of its flexible modeling choices including the widely used Shannon entrop…
-Munchausen Reinforcement Learning
Lingwei Zhu, Zheng Chen, Eiji Uchibe +1
The recently successful Munchausen Reinforcement Learning (M-RL) features implicit Kullback-Leibler (KL) regularization by augmenting the reward function with logarithm of the curr…
Multi-Tier Platform for Cognizing Massive Electroencephalogram
Zheng Chen, Lingwei Zhu, Ziwei Yang +1
An end-to-end platform assembling multiple tiers is built for precisely cognizing brain activities. Being fed massive electroencephalogram (EEG) data, the time-frequency spectrogra…
Adaptive Spike-Like Representation of EEG Signals for Sleep Stages Scoring
Lingwei Zhu, Koki Odani, Ziwei Yang +4
Recently there has seen promising results on automatic stage scoring by extracting spatio-temporal features from electroencephalogram (EEG). Such methods entail laborious manual fe…
Cancer Subtyping via Embedded Unsupervised Learning on Transcriptomics Data
Ziwei Yang, Lingwei Zhu, Zheng Chen +4
Cancer is one of the deadliest diseases worldwide. Accurate diagnosis and classification of cancer subtypes are indispensable for effective clinical treatment. Promising results on…
Automated Sleep Staging via Parallel Frequency-Cut Attention
Zheng Chen, Ziwei Yang, Lingwei Zhu +6
This paper proposes a novel framework for automatically capturing the time-frequency nature of electroencephalogram (EEG) signals of human sleep based on the authoritative sleep me…
Geometric Value Iteration: Dynamic Error-Aware KL Regularization for Reinforcement Learning
Toshinori Kitamura, Lingwei Zhu, Takamitsu Matsubara
The recent boom in the literature on entropy-regularized reinforcement learning (RL) approaches reveals that Kullback-Leibler (KL) regularization brings advantages to RL algorithms…
Cautious Policy Programming: Exploiting KL Regularization in Monotonic Policy Improvement for Reinforcement Learning
Lingwei Zhu, Toshinori Kitamura, Takamitsu Matsubara
In this paper, we propose cautious policy programming (CPP), a novel value-based reinforcement learning (RL) algorithm that can ensure monotonic policy improvement during learning.…
Cautious Actor-Critic
Lingwei Zhu, Toshinori Kitamura, Takamitsu Matsubara
The oscillating performance of off-policy learning and persisting errors in the actor-critic (AC) setting call for algorithms that can conservatively learn to suit the stability-cr…
Ensuring Monotonic Policy Improvement in Entropy-regularized Value-based Reinforcement Learning
Lingwei Zhu, Takamitsu Matsubara
This paper aims to establish an entropy-regularized value-based reinforcement learning method that can ensure the monotonic improvement of policies at each policy update. Unlike pr…