What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His scientific interests lie mostly in Artificial intelligence, Reinforcement learning, Stability, Machine learning and Probably approximately correct learning.
His study in Artificial intelligence focuses on Semi-supervised learning, Unsupervised learning and Concept class.
His Semi-supervised learning research focuses on Algorithmic learning theory and how it connects with Instance-based learning and Online machine learning.
His Reinforcement learning study combines topics in areas such as Dialogue management, Human–computer interaction, Markov decision process and Mathematical optimization.
His work on Generalization error as part of general Stability research is frequently linked to Sanity, bridging the gap between disciplines.
The concepts of his Probably approximately correct learning study are interwoven with issues in Theoretical computer science and Computation.
His most cited work include:
- An Introduction to Computational Learning Theory (1318 citations)
- Near-Optimal Reinforcement Learning in Polynomial Time (717 citations)
- Efficient noise-tolerant learning from statistical queries (588 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Mathematical optimization, Algorithm, Theoretical computer science and Discrete mathematics.
His biological study deals with issues like Machine learning, which deal with fields such as Probabilistic logic.
His study in Mathematical optimization is interdisciplinary in nature, drawing from both Mathematical economics and Regret.
The various areas that Michael Kearns examines in his Theoretical computer science study include Computation, Game theory and Stochastic game.
As part of one scientific family, Michael Kearns deals mainly with the area of Computational learning theory, narrowing it down to issues related to the Algorithmic learning theory, and often Instance-based learning.
His Semi-supervised learning study incorporates themes from Stability and Unsupervised learning.
He most often published in these fields:
- Artificial intelligence (24.34%)
- Mathematical optimization (14.23%)
- Algorithm (13.86%)
What were the highlights of his more recent work (between 2014-2021)?
- Mathematical economics (10.49%)
- Mathematical optimization (14.23%)
- Algorithm (13.86%)
In recent papers he was focusing on the following fields of study:
Mathematical economics, Mathematical optimization, Algorithm, Constraint and Regret are his primary areas of study.
His Mathematical economics research is multidisciplinary, incorporating perspectives in State, Differential privacy and Bounding overwatch.
He combines subjects such as Distribution, Empirical risk minimization, Group and Minimax with his study of Algorithm.
His work carried out in the field of Constraint brings together such families of science as Time complexity, Theoretical computer science, Heuristic and Reinforcement learning.
His Regret study integrates concerns from other disciplines, such as Polynomial, Dimension and Mahalanobis distance, Artificial intelligence.
Artificial intelligence is closely attributed to Computation in his work.
Between 2014 and 2021, his most popular works were:
- Fairness in Criminal Justice Risk Assessments: The State of the Art (263 citations)
- PARP Inhibition Elicits STING-Dependent Antitumor Immunity in Brca1-Deficient Ovarian Cancer. (131 citations)
- Fairness in learning: classic and contextual bandits (115 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Michael Kearns mostly deals with Regret, Mathematical economics, Constraint, Class and Theoretical computer science.
His Regret research includes themes of Mahalanobis distance and Artificial intelligence.
He interconnects Structure and State in the investigation of issues within Artificial intelligence.
The study incorporates disciplines such as Total cost, Payment, Incentive and Principal in addition to Mathematical economics.
His work deals with themes such as Time complexity, Test, Differential privacy and Reinforcement learning, which intersect with Constraint.
His research investigates the link between Theoretical computer science and topics such as Computational problem that cross with problems in Statistic, Heuristic and Oracle.
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