The Equi-Correlation Network: a New Kernelized-LARS with Automatic Kernel Parameters Tuning

Machine learning heavily relies on the ability to learn/approximate real functions. State variables, the perceptions, internal states, etc, of an agent are often represented as real numbers; grounded on them, the agent has to predict something, or act in some way. In this view, this outcome is a nonlinear function of the inputs. It is thus a very common task to fit a nonlinear function to observations, namely solving a regression problem. Among other approaches, the LARS is very appealing, for its nice theoretical properties, and actual efficiency to compute the whole $l_1$ regularization path of a supervised learning problem, along with the sparsity. In this paper, we consider the kernelized version of the LARS. In this setting, kernel functions generally have some parameters that have to be tuned. In this paper, we propose a new algorithm, the Equi-Correlation Network (ECON), which originality is that while computing the regularization path, ECON automatically tunes kernel hyper-parameters; thus, this opens the way to working with infinitely many kernel functions, from which, the most interesting are selected. Interestingly, our algorithm is still computationaly efficient, and provide state-of-the-art results on standard benchmarks, while lessening the hand-tuning burden

A Unified View of TD Algorithms; Introducing Full-Gradient TD and Equi-Gradient Descent TD

International audienceThis paper addresses the issue of policy evaluation in Markov Decision Processes, using linear function approximation. It provides a unified view of algorithms such as TD(lambda), LSTD(lambda), iLSTD, residual-gradient TD. It is asserted that they all consist in minimizing a gradient function and differ by the form of this function and their means of minimizing it. Two new schemes are introduced in that framework: Full-gradient TD which uses a generalization of the principle introduced in iLSTD, and EGD TD, which reduces the gradient by successive equi-gradient descents. These three algorithms form a new intermediate family with the interesting property of making much better use of the samples than TD while keeping a gradient descent scheme, which is useful for complexity issues and optimistic policy iteration

Sparse Temporal Difference Learning using LASSO

International audienceWe consider the problem of on-line value function estimation in reinforcement learning. We concentrate on the function approximator to use. To try to break the curse of dimensionality, we focus on non parametric function approximators. We propose to fit the use of kernels into the temporal difference algorithms by using regression via the LASSO. We introduce the equi-gradient descent algorithm (EGD) which is a direct adaptation of the one recently introduced in the LARS algorithm family for solving the LASSO. We advocate our choice of the EGD as a judicious algorithm for these tasks. We present the EGD algorithm in details as well as some experimental results. We insist on the qualities of the EGD for reinforcement learning

ECON: a Kernel Basis Pursuit Algorithm with Automatic Feature Parameter Tuning, and its Application to Photometric Solids Approximation

International audienceThis paper introduces a new algorithm, namely the Equi-Correlation Network (ECON), to perform supervised classification, and regression. ECON is a kernelized LARS-like algorithm, by which we mean that ECON uses an $l_1$ regularization to produce sparse estimators, ECON efficiently rides the regularization path to obtain the estimator associated to any regularization constant values, and ECON represents the data by way of features induced by a feature function. The originality of ECON is that it automatically tunes the parameters of the features while riding the regularization path. So, ECON has the unique ability to produce optimally tuned features for each value of the constant of regularization. We illustrate the remarkable experimental performance of ECON on standard benchmark datasets; we also present a novel application of machine learning in the field of computer graphics, namely the approximation of photometric solids

Equi-Gradient Temporal Difference Learning

Equi-Gradient Temporal Difference Learnin

Coupling Methodology within the Software Platform Alliances

CEA, ANDRA and EDF are jointly developing the software platform ALLIANCES which aim is to produce a tool for the simulation of nuclear waste storage and disposal repository. This type of simulations deals with highly coupled thermo-hydro-mechanical and chemical (T-H-M-C) processes. A key objective of Alliances is to give the capability for coupling algorithms development between existing codes. The aim of this paper is to present coupling methodology use in the context of this software platform.Comment: 7 page

The Iso-regularization Descent Algorithm for the LASSO

International audienceFollowing the introduction by Tibshirani of the LASSO technique for feature selection in regression, two algorithms were proposed by Osborne et al. for solving the associated problem. One is an homotopy method that gained popularity as the LASSO modification of the LARS algorithm. The other is a finite-step descent method that follows a path on the constraint polytope, and seems to have been largely ignored. One of the reason may be that it solves the constrained formulation of the LASSO, as opposed to the more practical regularized formulation. We give here an adaptation of this algorithm that solves the regularized problem, has a simpler formulation, and outperforms state-of-the-art algorithms in terms of speed

Experimental Infection of Squirrel Monkeys with Nipah Virus

We infected squirrel monkeys (Saimiri sciureus) with Nipah virus to determine the monkeys’ suitability for use as primate models in preclinical testing of preventive and therapeutic treatments. Infection of squirrel monkeys through intravenous injection was followed by high death rates associated with acute neurologic and respiratory illness and viral RNA and antigen production

Genome-wide association analyses for lung function and chronic obstructive pulmonary disease identify new loci and potential druggable targets

Chronic obstructive pulmonary disease (COPD) is characterized by reduced lung function and is the third leading cause of death globally. Through genome-wide association discovery in 48,943 individuals, selected from extremes of the lung function distribution in UK Biobank, and follow-up in 95,375 individuals, we increased the yield of independent signals for lung function from 54 to 97. A genetic risk score was associated with COPD susceptibility (odds ratio per 1 s.d. of the risk score (∼6 alleles) (95% confidence interval) = 1.24 (1.20-1.27), P = 5.05 × 10‾⁴⁹), and we observed a 3.7-fold difference in COPD risk between individuals in the highest and lowest genetic risk score deciles in UK Biobank. The 97 signals show enrichment in genes for development, elastic fibers and epigenetic regulation pathways. We highlight targets for drugs and compounds in development for COPD and asthma (genes in the inositol phosphate metabolism pathway and CHRM3) and describe targets for potential drug repositioning from other clinical indications.This work was funded by a Medical Research Council (MRC) strategic award to M.D.T., I.P.H., D.S. and L.V.W. (MC_PC_12010). This research has been conducted using the UK Biobank Resource under application 648. This article presents independent research funded partially by the National Institute for Health Research (NIHR). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the UK Department of Health. This research used the ALICE and SPECTRE High-Performance Computing Facilities at the University of Leicester. Additional acknowledgments and funding details can be found in the Supplementary Note