What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Nicolò Cesa-Bianchi focuses on Algorithm, Regret, Mathematical optimization, Perceptron and Artificial intelligence.
His Regret research is multidisciplinary, incorporating elements of Repeated game and Time horizon.
His studies deal with areas such as Stochastic process and Multi-armed bandit as well as Mathematical optimization.
His Multi-armed bandit research integrates issues from Thompson sampling, Stochastic game and Reinforcement learning.
His work deals with themes such as Monte Carlo tree search and General game playing, which intersect with Thompson sampling.
The Artificial intelligence study combines topics in areas such as Machine learning, General theorem and Pattern recognition.
His most cited work include:
- Finite-time Analysis of the Multiarmed Bandit Problem (3984 citations)
- Prediction, learning, and games (2247 citations)
- The Nonstochastic Multiarmed Bandit Problem (1609 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Regret, Artificial intelligence, Algorithm, Mathematical optimization and Machine learning.
His research integrates issues of Theoretical computer science, Minimax and Combinatorics in his study of Regret.
His Algorithm study deals with Perceptron intersecting with Support vector machine.
His Mathematical optimization research incorporates elements of Common value auction, Multi-armed bandit and Reinforcement learning.
The concepts of his Multi-armed bandit study are interwoven with issues in Stochastic process, Stochastic game and Statistical assumption.
His Machine learning study integrates concerns from other disciplines, such as Data mining and Online algorithm.
He most often published in these fields:
- Regret (32.17%)
- Artificial intelligence (29.57%)
- Algorithm (23.91%)
What were the highlights of his more recent work (between 2014-2021)?
- Regret (32.17%)
- Combinatorics (13.91%)
- Mathematical optimization (20.43%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Regret, Combinatorics, Mathematical optimization, Artificial intelligence and Order.
He performs multidisciplinary studies into Regret and Online learning in his work.
His Mathematical optimization study combines topics from a wide range of disciplines, such as Telecommunications network, Reduction, Multi-armed bandit and Random variable.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning, Regression and Pattern recognition.
His Machine learning study which covers Data mining that intersects with Data stream.
He works mostly in the field of Time horizon, limiting it down to topics relating to Adversarial system and, in certain cases, Algorithm, as a part of the same area of interest.
Between 2014 and 2021, his most popular works were:
- Regret Minimization for Reserve Prices in Second-Price Auctions (65 citations)
- Online Learning with Feedback Graphs: Beyond Bandits (41 citations)
- Advances in Neural Information Processing Systems 31 (38 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Nicolò Cesa-Bianchi mainly investigates Regret, Mathematical optimization, Artificial intelligence, Online learning and Order.
His work carried out in the field of Regret brings together such families of science as Discrete mathematics, Independence number, Winnow and Combinatorics.
His work in Mathematical optimization covers topics such as Common value auction which are related to areas like Nonparametric statistics.
He combines subjects such as Mathematical economics and Machine learning with his study of Artificial intelligence.
Nicolò Cesa-Bianchi interconnects Bounding overwatch and Synthetic data in the investigation of issues within Machine learning.
His Theoretical computer science research includes elements of Graph, Computation and Dimension.
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.
