What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Quantum mechanics
David H. Wolpert mainly investigates Artificial intelligence, Collective intelligence, Mathematical optimization, Cross-validation and No free lunch in search and optimization.
He usually deals with Artificial intelligence and limits it to topics linked to Set and Computational learning theory.
His studies in Mathematical optimization integrate themes in fields like Simple, Bayesian probability, Gaussian, Error detection and correction and Multi-armed bandit.
His Cross-validation study integrates concerns from other disciplines, such as Algorithm and Rest.
His No free lunch in search and optimization research integrates issues from Optimization problem, No free lunch theorem, Beam search, Search algorithm and Evolutionary computation.
His Algorithm design research incorporates themes from Information theory and Minimax.
His most cited work include:
- No free lunch theorems for optimization (6785 citations)
- Original Contribution: Stacked generalization (2811 citations)
- The lack of a priori distinctions between learning algorithms (961 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Mathematical optimization, Game theory, Algorithm and Statistical physics.
All of his Artificial intelligence and Collective intelligence, Reinforcement learning, No free lunch in search and optimization and Cross-validation investigations are sub-components of the entire Artificial intelligence study.
David H. Wolpert specializes in Mathematical optimization, namely Optimization problem.
His Algorithm research incorporates elements of Sampling, Physical system, Function and Prior probability.
His Function research is multidisciplinary, incorporating perspectives in Upper and lower bounds, Maxwell's demon and Distribution.
His research in Statistical physics focuses on subjects like Non-equilibrium thermodynamics, which are connected to Computation and Work.
He most often published in these fields:
- Artificial intelligence (18.60%)
- Mathematical optimization (17.83%)
- Game theory (12.02%)
What were the highlights of his more recent work (between 2017-2021)?
- Statistical physics (12.40%)
- Entropy production (6.98%)
- Master equation (10.47%)
In recent papers he was focusing on the following fields of study:
His main research concerns Statistical physics, Entropy production, Master equation, Applied mathematics and Computation.
His research in Master equation intersects with topics in Function, Space–time tradeoff and Thermodynamics.
His work carried out in the field of Applied mathematics brings together such families of science as Bayes' theorem and Distribution.
His Distribution study combines topics in areas such as Maxwell's demon, Conditional probability distribution and No free lunch in search and optimization.
David H. Wolpert interconnects Optimization problem, Electronic circuit and Topology in the investigation of issues within Computation.
His study in Physical system is interdisciplinary in nature, drawing from both Non-equilibrium thermodynamics, Information theory and Algorithm.
Between 2017 and 2021, his most popular works were:
- The Relationship Between PAC, the Statistical Physics Framework, the Bayesian Framework, and the VC Framework (69 citations)
- Semantic information, autonomous agency and non-equilibrium statistical physics. (38 citations)
- The stochastic thermodynamics of computation (37 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Quantum mechanics
The scientist’s investigation covers issues in Computation, Applied mathematics, Entropy production, Turing machine and Master equation.
His work investigates the relationship between Applied mathematics and topics such as Distribution that intersect with problems in Upper and lower bounds, Function and Maxwell's demon.
As a part of the same scientific family, David H. Wolpert mostly works in the field of Turing machine, focusing on Salient and, on occasion, Information theory.
His Information theory research includes themes of Range and Thermodynamics.
His Master equation research is multidisciplinary, relying on both Exploit, Algorithm, Space–time tradeoff and Conditional probability distribution.
He performs multidisciplinary study in Process and Optimization problem in his work.
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