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Yaodong Yang

83 papers hereh-index 428.3k citations137 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author10
  • middle author41
  • last author25

Across the 76 of 83 papers where every author was matched, so the position is known.

fields
  • cs.LG28
  • cs.MA19
  • cs.AI16
  • cs.GT7
  • cs.RO4
  • stat.ML4
same name
  • Yaodong Yang — 45 papers, h 18
  • Yaodong Yang — 18 papers, h 8
  • Yaodong Yang — 12 papers, h 3
  • Yaodong Yang — 10 papers, h 6
  • Yaodong Yang — 7 papers
  • Yaodong Yang — 7 papers, h 5

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20172023
most citedMean Field Multi-Agent Reinforcement Learning

313 citations · 1.8k across the 83 of their papers we have counts for

collaborators
Showing 2018Show all

4 papers · 1 filter

q-fin.RM2018★ 2 cited

Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting

Yaodong Yang, Alisa Kolesnikova, Stefan Lessmann +3

The paper examines the potential of deep learning to support decisions in financial risk management. We develop a deep learning model for predicting whether individual spread trade…

cs.LG2018★ 5 cited

Benchmarking Deep Sequential Models on Volatility Predictions for Financial Time Series

Qiang Zhang, Rui Luo, Yaodong Yang +1

Volatility is a quantity of measurement for the price movements of stocks or options which indicates the uncertainty within financial markets. As an indicator of the level of risk…

cs.MA2018★ 36 cited

Factorized Q-Learning for Large-Scale Multi-Agent Systems

Ming Zhou, Yong Chen, Ying Wen +5

Deep Q-learning has achieved significant success in single-agent decision making tasks. However, it is challenging to extend Q-learning to large-scale multi-agent scenarios, due to…

cs.MA2018★ 313 cited

Mean Field Multi-Agent Reinforcement Learning

Yaodong Yang, Rui Luo, Minne Li +3

Existing multi-agent reinforcement learning methods are limited typically to a small number of agents. When the agent number increases largely, the learning becomes intractable due…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.