What is he best known for?
The fields of study he is best known for:
- Statistics
- Mathematical analysis
- Machine learning
Sébastien Bubeck mostly deals with Regret, Mathematical optimization, Upper and lower bounds, Minimax and Algorithm.
Sébastien Bubeck has included themes like Discrete mathematics, Combinatorial optimization and Reinforcement learning in his Regret study.
The Linear programming research Sébastien Bubeck does as part of his general Mathematical optimization study is frequently linked to other disciplines of science, such as Binary number, therefore creating a link between diverse domains of science.
His Upper and lower bounds research incorporates themes from Normalization, Mathematical economics and Randomized algorithm.
His study in Minimax is interdisciplinary in nature, drawing from both Lipschitz continuity, Bounded function and Euclidean space, Combinatorics.
His Algorithm study also includes fields such as
- Convex function which is related to area like Convergence and Multiplicative function,
- Gradient descent, which have a strong connection to Stochastic optimization, Interior point method, Randomness and Convex optimization,
- Rate of convergence which is related to area like Stochastic gradient descent and Random coordinate descent.
His most cited work include:
- Regret Analysis of Stochastic and Nonstochastic Multi-Armed Bandit Problems (1332 citations)
- Convex Optimization: Algorithms and Complexity (770 citations)
- Minimax policies for adversarial and stochastic bandits (281 citations)
What are the main themes of his work throughout his whole career to date?
Combinatorics, Regret, Upper and lower bounds, Mathematical optimization and Discrete mathematics are his primary areas of study.
He interconnects Competitive analysis, Lipschitz continuity and Regular polygon in the investigation of issues within Combinatorics.
His work carried out in the field of Regret brings together such families of science as Mathematical economics, Minimax, Bounded function and Logarithm.
His Upper and lower bounds research integrates issues from Statistical hypothesis testing and Benchmark.
Sébastien Bubeck combines subjects such as Sampling, Estimator, Cluster analysis and Convex optimization with his study of Mathematical optimization.
His studies deal with areas such as Sample and Dimension as well as Discrete mathematics.
He most often published in these fields:
- Combinatorics (41.18%)
- Regret (38.24%)
- Upper and lower bounds (27.65%)
What were the highlights of his more recent work (between 2017-2021)?
- Combinatorics (41.18%)
- Regret (38.24%)
- Upper and lower bounds (27.65%)
In recent papers he was focusing on the following fields of study:
Sébastien Bubeck mainly investigates Combinatorics, Regret, Upper and lower bounds, Competitive analysis and Discrete mathematics.
His Combinatorics research is multidisciplinary, incorporating perspectives in Sequence, Lipschitz continuity and Convex optimization.
His studies in Regret integrate themes in fields like Randomness and Mathematical optimization.
His studies deal with areas such as Gradient descent, Logarithm and Dimension as well as Upper and lower bounds.
His Competitive analysis study combines topics from a wide range of disciplines, such as Bounded function, Online algorithm, Metric space and Regular polygon.
Sébastien Bubeck has included themes like Q-learning, Reinforcement learning, Time horizon, Scale and Sample in his Discrete mathematics study.
Between 2017 and 2021, his most popular works were:
- Is Q-learning Provably Efficient? (173 citations)
- Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers (133 citations)
- Optimal Algorithms for Non-Smooth Distributed Optimization in Networks (80 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Machine learning
- Mathematical analysis
The scientist’s investigation covers issues in Combinatorics, Convex function, Lipschitz continuity, Smoothing and Simple.
In his study, Generalization, Bounded function, Ball and Curvature is inextricably linked to Sequence, which falls within the broad field of Combinatorics.
His study explores the link between Convex function and topics such as Rate of convergence that cross with problems in Convex optimization and Convexity.
His research in Convex optimization focuses on subjects like Upper and lower bounds, which are connected to Mathematical optimization.
Sébastien Bubeck studied Mathematical optimization and Convex body that intersect with Mirror descent and Regret.
Sébastien Bubeck has researched Smoothing in several fields, including Code and Machine learning, Deep learning, Robustness, Artificial intelligence.
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