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Fingerprint Dive into the research topics where Mengdi Wang is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

  • 3 Similar Profiles
Gradient methods Engineering & Materials Science
Markov Decision Process Mathematics
Sampling Engineering & Materials Science
Gradient Method Mathematics
Complex networks Engineering & Materials Science
Chemical analysis Engineering & Materials Science
Stochastic Methods Mathematics
Reinforcement learning Engineering & Materials Science

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Projects 2016 2022

Research Output 2012 2019

  • 146 Citations
  • 7 h-Index
  • 12 Conference contribution
  • 12 Article
  • 4 Paper
  • 4 Conference article
Hardness
Optimization Problem
Approximation
Loss Function
Penalty
Cardinality Constraints
Spatial Model
Graphical Models
Convex Geometry
Geometry

Learning to Control in Metric Space with Optimal Regret

Wang, M., Sep 2019, 2019 57th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2019. Institute of Electrical and Electronics Engineers Inc., p. 726-733 8 p. 8919864. (2019 57th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2019).

Princeton University

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Regret
Metric space
Reinforcement learning
Reinforcement Learning
Metric

Maximum Likelihood Tensor Decomposition of Markov Decision Process

Wang, M., Jul 2019, 2019 IEEE International Symposium on Information Theory, ISIT 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., p. 3062-3066 5 p. 8849765. (IEEE International Symposium on Information Theory - Proceedings; vol. 2019-July).

Princeton University

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Tensor Decomposition
Markov Decision Process
Maximum likelihood
Tensors
Maximum Likelihood
1 Citation (Scopus)

Multilevel stochastic gradient methods for nested composition optimization

Wang, M., Jan 1 2019, In : SIAM Journal on Optimization. 29, 1, p. 616-659 44 p.

Princeton University

Research output: Contribution to journalArticle

Stochastic Gradient
Gradient methods
Stochastic Methods
Gradient Method
Optimization