What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
Eric Moulines mainly investigates Speech recognition, Applied mathematics, Mathematical optimization, Algorithm and Speech synthesis.
The various areas that he examines in his Speech recognition study include Transformation and Waveform, Signal, Noise.
His Applied mathematics study combines topics in areas such as Econometrics, Central limit theorem, Asymptotic distribution, Noise and Markov model.
The study incorporates disciplines such as Convergence, Stochastic approximation, Type inequality, Logarithm and Regret in addition to Mathematical optimization.
His Algorithm research integrates issues from Analysis of covariance, Statistics, Total variation, Contrast and Dimension.
His work on PSOLA is typically connected to Concatenation as part of general Speech synthesis study, connecting several disciplines of science.
His most cited work include:
- A blind source separation technique using second-order statistics (2268 citations)
- Subspace methods for the blind identification of multichannel FIR filters (1429 citations)
- Inference in Hidden Markov Models (1175 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Applied mathematics, Algorithm, Mathematical optimization, Markov chain Monte Carlo and Estimator.
His Applied mathematics study incorporates themes from Convergence, Stochastic approximation, State space, Central limit theorem and Markov chain.
Eric Moulines combines subjects such as Rate of convergence and Expectation–maximization algorithm with his study of Stochastic approximation.
The concepts of his Algorithm study are interwoven with issues in Subspace topology, Artificial intelligence and Speech recognition.
His research in Artificial intelligence intersects with topics in Machine learning and Pattern recognition.
Eric Moulines focuses mostly in the field of Markov chain Monte Carlo, narrowing it down to topics relating to Ergodicity and, in certain cases, Ergodic theory.
He most often published in these fields:
- Applied mathematics (32.22%)
- Algorithm (30.40%)
- Mathematical optimization (21.58%)
What were the highlights of his more recent work (between 2017-2021)?
- Applied mathematics (32.22%)
- Algorithm (30.40%)
- Stochastic approximation (10.94%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Applied mathematics, Algorithm, Stochastic approximation, Markov chain Monte Carlo and Convergence.
His Applied mathematics study integrates concerns from other disciplines, such as Sampling, Ergodicity, Markov chain, Stochastic optimization and Upper and lower bounds.
The Algorithm study combines topics in areas such as Probability distribution, Kernel, Variational inequality, High dimensional and Column.
His Stochastic approximation research includes themes of Geodesic, Conditional probability distribution, Random walk and Expectation–maximization algorithm.
His Convergence research is multidisciplinary, relying on both Optimization problem, Mathematical optimization and Scale.
Eric Moulines interconnects Value and Bayesian probability in the investigation of issues within Mathematical optimization.
Between 2017 and 2021, his most popular works were:
- Efficient Bayesian computation by proximal Markov chain Monte Carlo: when Langevin meets Moreau (56 citations)
- High-dimensional Bayesian inference via the unadjusted Langevin algorithm (46 citations)
- The promises and pitfalls of Stochastic Gradient Langevin Dynamics (18 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
Markov chain, Algorithm, Applied mathematics, Markov chain Monte Carlo and Langevin dynamics are his primary areas of study.
His biological study spans a wide range of topics, including Uncertainty quantification, Variable-order Bayesian network, Mathematical optimization and Bayesian probability.
While working in this field, Eric Moulines studies both Algorithm and Mirror descent.
His Applied mathematics research incorporates elements of Convergence, Stochastic approximation, Markov process, Distribution and Stochastic optimization.
His Markov chain Monte Carlo research is multidisciplinary, incorporating perspectives in Bayes estimator, Sample variance, Delta method and Variance reduction.
His Langevin dynamics research integrates issues from Invariant, Monte Carlo method and Mathematical analysis.
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