What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of investigation include Mathematical optimization, Algorithm, Regret, Reinforcement learning and Artificial intelligence.
Sham M. Kakade brings together Mathematical optimization and System identification to produce work in his papers.
His Algorithm research incorporates themes from Mixture model, Unsupervised learning, Implicit function and Hidden Markov model.
The various areas that Sham M. Kakade examines in his Regret study include Curse of dimensionality, Code, Function, Upper and lower bounds and Compressed sensing.
His study in Reinforcement learning is interdisciplinary in nature, drawing from both Robot and State space.
Sham M. Kakade combines subjects such as Machine learning and Pattern recognition with his study of Artificial intelligence.
His most cited work include:
- Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design (877 citations)
- Tensor decompositions for learning latent variable models (713 citations)
- Cover trees for nearest neighbor (677 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Mathematical optimization, Algorithm, Artificial intelligence, Applied mathematics and Reinforcement learning.
His biological study spans a wide range of topics, including Stochastic gradient descent, Gradient descent, Simple, Markov decision process and Regret.
The concepts of his Algorithm study are interwoven with issues in Mixture model, Matrix, Unsupervised learning and Hidden Markov model.
His Artificial intelligence study incorporates themes from Machine learning and Pattern recognition.
His Applied mathematics study integrates concerns from other disciplines, such as Regularization, Covariance, Convexity, Estimator and Generalization.
His work deals with themes such as Parameterized complexity, Supervised learning, Sample, Polynomial and Function approximation, which intersect with Reinforcement learning.
He most often published in these fields:
- Mathematical optimization (31.43%)
- Algorithm (18.21%)
- Artificial intelligence (15.71%)
What were the highlights of his more recent work (between 2018-2021)?
- Mathematical optimization (31.43%)
- Reinforcement learning (12.50%)
- Applied mathematics (15.00%)
In recent papers he was focusing on the following fields of study:
Sham M. Kakade spends much of his time researching Mathematical optimization, Reinforcement learning, Applied mathematics, Algorithm and Markov decision process.
He interconnects Sampling, Control and Sample size determination in the investigation of issues within Mathematical optimization.
He has researched Reinforcement learning in several fields, including Upper and lower bounds, Sample, Function approximation and Curse of dimensionality.
The study incorporates disciplines such as Regularization, Stochastic gradient descent and Regret in addition to Applied mathematics.
His Algorithm study combines topics from a wide range of disciplines, such as Entropy, Generative grammar and Pruning.
His work in Markov decision process covers topics such as State space which are related to areas like Generative model.
Between 2018 and 2021, his most popular works were:
- Meta-Learning with Implicit Gradients (144 citations)
- PACT: Privacy Sensitive Protocols and Mechanisms for Mobile Contact Tracing (85 citations)
- Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes (81 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
The scientist’s investigation covers issues in Regret, Mathematical optimization, Artificial intelligence, Linear dynamical system and Applied mathematics.
His work on Intelligent decision support system expands to the thematically related Regret.
His research brings together the fields of Markov decision process and Mathematical optimization.
His Artificial intelligence research includes themes of Contrast and Sample.
His studies examine the connections between Linear dynamical system and genetics, as well as such issues in Control, with regards to Statistical noise.
His work carried out in the field of Applied mathematics brings together such families of science as Measure, Double descent and Convexity.
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