What is he best known for?
The fields of study he is best known for:
- Statistics
- Algorithm
- Machine learning
His primary scientific interests are in Mathematical optimization, Rate of convergence, Nash equilibrium, Combinatorics and Mathematical economics.
His research investigates the link between Mathematical optimization and topics such as Artificial intelligence that cross with problems in Multinomial logistic regression.
His Rate of convergence study combines topics from a wide range of disciplines, such as Optimization problem and Estimator.
His work carried out in the field of Nash equilibrium brings together such families of science as Time complexity and Folk theorem.
His Combinatorics research includes themes of Discrete mathematics and Computation.
When carried out as part of a general Mathematical economics research project, his work on Correlated equilibrium and Repeated game is frequently linked to work in Separable space and Arrow–Debreu model, therefore connecting diverse disciplines of study.
His most cited work include:
- Settling the Complexity of Two-Player Nash Equilibrium (445 citations)
- Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization (440 citations)
- Settling the complexity of computing two-player Nash equilibria (422 citations)
What are the main themes of his work throughout his whole career to date?
Xi Chen mainly investigates Combinatorics, Mathematical optimization, Discrete mathematics, Estimator and Algorithm.
His work is dedicated to discovering how Combinatorics, Constant are connected with Computation and other disciplines.
His Mathematical optimization study incorporates themes from Regret and Bayesian probability.
In his research on the topic of Bayesian probability, Data mining is strongly related with Machine learning.
His studies deal with areas such as Graph and Constraint satisfaction problem as well as Discrete mathematics.
The study incorporates disciplines such as Directed graph and Mathematical economics, Nash equilibrium in addition to Time complexity.
He most often published in these fields:
- Combinatorics (32.78%)
- Mathematical optimization (19.87%)
- Discrete mathematics (17.55%)
What were the highlights of his more recent work (between 2019-2021)?
- Combinatorics (32.78%)
- Estimator (12.25%)
- Algorithm (11.92%)
In recent papers he was focusing on the following fields of study:
Xi Chen mainly focuses on Combinatorics, Estimator, Algorithm, Rate of convergence and Econometrics.
His Binary logarithm, Representation and Time complexity study, which is part of a larger body of work in Combinatorics, is frequently linked to Deletion channel and Distribution, bridging the gap between disciplines.
The Estimator study combines topics in areas such as Stochastic gradient descent, Uncertainty quantification, Degrees of freedom, Range and Quantile regression.
His study in Algorithm is interdisciplinary in nature, drawing from both Statistical inference, Stochastic optimization and Convex optimization.
Xi Chen performs multidisciplinary study on Variable and Mathematical optimization in his works.
His Mathematical optimization research is multidisciplinary, incorporating elements of Sequential analysis, Prior probability and Bayes' theorem.
Between 2019 and 2021, his most popular works were:
- Statistical inference for model parameters in stochastic gradient descent (13 citations)
- Polynomial-time trace reconstruction in the smoothed complexity model (10 citations)
- Robust inference via multiplier bootstrap (8 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Algorithm
- Machine learning
The scientist’s investigation covers issues in Estimator, Combinatorics, Applied mathematics, Metric space and Representation.
His Estimator research integrates issues from Linear regression, Additive model, Degrees of freedom, Quantile regression and Rate of convergence.
His research investigates the connection with Quantile regression and areas like Quantile which intersect with concerns in Mathematical optimization.
His Mathematical optimization study integrates concerns from other disciplines, such as Regret and Reinforcement learning.
His work in the fields of Combinatorics, such as Binary logarithm, Multiset and Time complexity, overlaps with other areas such as Trace and Deletion channel.
His studies in Applied mathematics integrate themes in fields like Stochastic optimization, Stochastic gradient descent, Ergodicity and Stationary point.
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