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Wen-Jie Shen

dblp:352/9898 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Efficient and distributed learning · 70% Reinforcement learning · 30%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Efficient and distributed learning
active learning		 0.712023
Learning Generalizable Batch Active Learning Strategies via Deep Q-networks (Student Abstract) · AAAI 2023
Machine learning › Efficient and distributed learning › active learning
batch active learning		 0.712023
Learning Generalizable Batch Active Learning Strategies via Deep Q-networks (Student Abstract) · AAAI 2023
Machine learning › Reinforcement learning › deep reinforcement learning
deep q-network		 0.712023
Learning Generalizable Batch Active Learning Strategies via Deep Q-networks (Student Abstract) · AAAI 2023
Machine learning › Efficient and distributed learning › active learning
uncertainty sampling		 0.212023
Learning Generalizable Batch Active Learning Strategies via Deep Q-networks (Student Abstract) · AAAI 2023

Methods — techniques the papers use, named apart from their topics

markov decision process · 0.7deep q-network · 0.7
YearPublicationVenuePosition
2023 Learning Generalizable Batch Active Learning Strategies via Deep Q-networks (Student Abstract)
abstract
To handle a large amount of unlabeled data, batch active learning (BAL) queries humans for the labels of a batch of the most valuable data points at every round. Most current BAL strategies are based on human-designed heuristics, such as uncertainty sampling or mutual information maximization. However, there exists a disagreement between these heuristics and the ultimate goal of BAL, i.e., optimizing the model's final performance within the query budgets. This disagreement leads to a limited generality of these heuristics. To this end, we formulate BAL as an MDP and propose a data-driven approach based on deep reinforcement learning. Our method learns the BAL strategy by maximizing the model's final performance. Experiments on the UCI benchmark show that our method can achieve competitive performance compared to existing heuristics-based approaches.
Yi-Chen Li 0001, Wen-Jie Shen, Feng Mao, Zongzhang Zhang, Yang Yu 0001
 AAAI2