Publications related to Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

The convex

\ell_1

-regularized

\log\det

divergence criterion has been shown to produce theoretically consistent graph learning. However, this objective function is challenging since the

\ell_1

-regularization is nonsmooth, the

\log\det

objective is n ...

2013

Fast Proximal Algorithms For Self-Concordant Function Minimization With Application To Sparse Graph Selection

Volkan Cevher, Anastasios Kyrillidis

The convex l(1)-regularized log det divergence criterion has been shown to produce theoretically consistent graph learning. However, this objective function is challenging since the l(1)-regularization is nonsmooth, the log det objective is not globally Li ...

IEEE2013

Monte-Carlo utility estimates for Bayesian reinforcement learning

Christos Dimitrakakis

This paper introduces a set of algorithms for Monte-Carlo Bayesian reinforcement learning. Firstly, Monte-Carlo estimation of upper bounds on the Bayes-optimal value function is employed to construct an optimistic policy. Secondly, gradient-based algorithm ...

2013

Measurement-based Real-Time Optimization of Chemical Processes

Dominique Bonvin, Grégory François

This chapter presents recent developments in the field of process optimization. In the presence of uncertainty in the form of plant-model mismatch and process disturbances, the standard model-based optimization techniques might not achieve optimality for t ...

Academic Press, Elsevier2013

Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning

Jie Luo

Many state-of-the-art approaches for Multi Kernel Learning (MKL) struggle at finding a compromise between performance, sparsity of the solution and speed of the optimization process. In this paper we look at the MKL problem at the same time from a learning ...

2011

Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning

Jie Luo

Many state-of-the-art approaches for Multi Kernel Learning (MKL) struggle at finding a compromise between performance, sparsity of the solution and speed of the optimization process. In this paper we look at the MKL problem at the same time from a learning ...

Idiap2011

Comparison of Gradient Estimation Methods for Real-time Optimization

Dominique Bonvin, Grégory François

Various real-time optimization techniques proceed by controlling the gradient to zero. These methods primarily differ in the way the gradient is estimated. This paper compares various gradient estimation methods. It is argued that methods with model-based ...

Elsevier2011

Hybrid Simulated Annealing and Its Application to Optimization of Hidden Markov Models for Visual Speech Recognition

Jong Seok Lee

We propose a novel stochastic optimization algorithm, hybrid simulated annealing (SA), to train hidden Markov models (HMMs) for visual speech recognition. In our algorithm, SA is combined with a local optimization operator that substitutes a better solutio ...

2010

A Dual Modifier-Adaptation Approach for Real-Time Optimization

Dominique Bonvin, Benoît Chachuat, Alejandro Gabriel Marchetti

For good performance in practice, real-time optimization schemes need to be able to deal with the inevitable plant-model mismatch problem. Unlike the two-step schemes combining parameter estimation and optimization, the modifier-adaptation approach does no ...

Elsevier2010

Real-time Optimization with Estimation of Experimental Gradient

Dominique Bonvin, Benoît Chachuat, Alejandro Gabriel Marchetti

For good performance in practice, real-time optimization schemes need to be able to deal with the inevitable model-mismatch problem. Unlike the two-step schemes combining parameter estimation and optimization, the modifier-adaptation approach uses experime ...

2009

Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval

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Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

Fast Proximal Algorithms For Self-Concordant Function Minimization With Application To Sparse Graph Selection

Monte-Carlo utility estimates for Bayesian reinforcement learning

Measurement-based Real-Time Optimization of Chemical Processes

Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning

Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning

Comparison of Gradient Estimation Methods for Real-time Optimization

Hybrid Simulated Annealing and Its Application to Optimization of Hidden Markov Models for Visual Speech Recognition

A Dual Modifier-Adaptation Approach for Real-Time Optimization

Real-time Optimization with Estimation of Experimental Gradient

Monte-Carlo utility estimates for Bayesian reinforcement learning

Measurement-based Real-Time Optimization of Chemical Processes

Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning

Comparison of Gradient Estimation Methods for Real-time Optimization

A Dual Modifier-Adaptation Approach for Real-Time Optimization

Real-time Optimization with Estimation of Experimental Gradient

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning

Fast Proximal Algorithms For Self-Concordant Function Minimization With Application To Sparse Graph Selection

Hybrid Simulated Annealing and Its Application to Optimization of Hidden Markov Models for Visual Speech Recognition