What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Shie Mannor spends much of his time researching Mathematical optimization, Reinforcement learning, Artificial intelligence, Algorithm and Markov decision process.
His research in the fields of Cross-entropy method overlaps with other disciplines such as CVAR.
His Reinforcement learning research incorporates themes from Decision problem, Function approximation and Bellman equation.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and Pattern recognition.
His work carried out in the field of Algorithm brings together such families of science as Binary classification, Support vector machine and Regression.
His work deals with themes such as Dynamic programming, Curse of dimensionality and Minimax, which intersect with Markov decision process.
His most cited work include:
- A Tutorial on the Cross-Entropy Method (1243 citations)
- The kernel recursive least-squares algorithm (754 citations)
- Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems (310 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Mathematical optimization, Reinforcement learning, Artificial intelligence, Markov decision process and Algorithm.
As a part of the same scientific family, Shie Mannor mostly works in the field of Mathematical optimization, focusing on Robustness and, on occasion, Robust optimization.
He combines subjects such as Function approximation, Applied mathematics and Bellman equation with his study of Reinforcement learning.
His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning and Pattern recognition.
His study with Markov decision process involves better knowledge in Markov process.
His research brings together the fields of Theoretical computer science and Algorithm.
He most often published in these fields:
- Mathematical optimization (42.58%)
- Reinforcement learning (27.36%)
- Artificial intelligence (23.70%)
What were the highlights of his more recent work (between 2017-2021)?
- Reinforcement learning (27.36%)
- Mathematical optimization (42.58%)
- Artificial intelligence (23.70%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Reinforcement learning, Mathematical optimization, Artificial intelligence, Regret and Markov decision process.
He has included themes like Convergence, Dynamic programming, Natural language, Robustness and Function approximation in his Reinforcement learning study.
Robustness is often connected to Regularization in his work.
Particularly relevant to Bellman equation is his body of work in Mathematical optimization.
In his research, Simple is intimately related to Machine learning, which falls under the overarching field of Artificial intelligence.
His research integrates issues of Function and Construct in his study of Regret.
Between 2017 and 2021, his most popular works were:
- Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning (66 citations)
- Reward Constrained Policy Optimization (63 citations)
- Finite Sample Analyses for TD(0) With Function Approximation. (60 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary scientific interests are in Reinforcement learning, Mathematical optimization, Artificial intelligence, Regret and Robustness.
His Reinforcement learning research is multidisciplinary, incorporating perspectives in Convergence, Trust region, Markov decision process and Function approximation.
His research on Mathematical optimization focuses in particular on Bellman equation.
Shie Mannor has included themes like Machine learning, Optics and Extension in his Artificial intelligence study.
His Regret study combines topics from a wide range of disciplines, such as Matching and Smoothness.
His study on Robustness also encompasses disciplines like
- State most often made with reference to Speedup,
- Regularization which connect with Adversarial system and Control.
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