What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Computer network
Yishay Mansour spends much of his time researching Mathematical optimization, Algorithm, Discrete mathematics, Combinatorics and Mathematical economics.
His research integrates issues of Function, Partially observable Markov decision process, Game theory and Reinforcement learning in his study of Mathematical optimization.
He interconnects Markov decision process, Incremental decision tree, Decision tree learning and Control theory in the investigation of issues within Algorithm.
His studies in Discrete mathematics integrate themes in fields like Stream cipher, Pseudorandom permutation, Substitution-permutation network and Transposition cipher.
His work deals with themes such as Computational complexity theory, Upper and lower bounds, Distribution and Constant, which intersect with Combinatorics.
The various areas that Yishay Mansour examines in his Mathematical economics study include Common value auction, Bounded function and Approximation algorithm.
His most cited work include:
- Policy Gradient Methods for Reinforcement Learning with Function Approximation (3144 citations)
- Constant depth circuits, Fourier transform, and learnability (547 citations)
- A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes (324 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Mathematical optimization, Algorithm, Combinatorics, Discrete mathematics and Regret.
His study looks at the relationship between Mathematical optimization and topics such as Competitive analysis, which overlap with Scheduling.
Yishay Mansour usually deals with Algorithm and limits it to topics linked to Network packet and Distributed computing.
Combinatorics is closely attributed to Upper and lower bounds in his research.
His Discrete mathematics research incorporates elements of Function and Computation.
His Regret study frequently links to adjacent areas such as Sequence.
He most often published in these fields:
- Mathematical optimization (23.31%)
- Algorithm (16.33%)
- Combinatorics (17.53%)
What were the highlights of his more recent work (between 2017-2021)?
- Regret (15.74%)
- Mathematical optimization (23.31%)
- Combinatorics (17.53%)
In recent papers he was focusing on the following fields of study:
Regret, Mathematical optimization, Combinatorics, Upper and lower bounds and Reinforcement learning are his primary areas of study.
His study in Regret is interdisciplinary in nature, drawing from both Adversarial system, Discrete mathematics, Shortest path problem, Markov decision process and Bounded function.
His Discrete mathematics research incorporates themes from Function and Rational agent.
Specifically, his work in Mathematical optimization is concerned with the study of Online algorithm.
As part of one scientific family, Yishay Mansour deals mainly with the area of Combinatorics, narrowing it down to issues related to the Sequence, and often Random variable and Thompson sampling.
His research in Reinforcement learning intersects with topics in Travelling salesman problem and State.
Between 2017 and 2021, his most popular works were:
- Three Approaches for Personalization with Applications to Federated Learning. (52 citations)
- Online Linear Quadratic Control (36 citations)
- Making the Most of Your Samples (27 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Computer network
Yishay Mansour focuses on Regret, Mathematical optimization, Discrete mathematics, Convex optimization and Upper and lower bounds.
His Regret research includes themes of Contrast, Quadratic equation, Shortest path problem, Linear quadratic and Function.
Within one scientific family, he focuses on topics pertaining to State space under Function, and may sometimes address concerns connected to Online algorithm and Regularization.
By researching both Mathematical optimization and Mean squared prediction error, Yishay Mansour produces research that crosses academic boundaries.
He combines subjects such as Leader election, Rational agent, Asynchronous communication, Node and Tree with his study of Discrete mathematics.
His Upper and lower bounds research integrates issues from Sampling scheme, Order, Combinatorics and Gibbs sampling.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
