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Keep the gradient flowing

Policy Gradients Part 1: The REINFORCE Estimator On the Link Between Optimization and Polynomials, Part 6. Optimization Nuggets: Stochastic Polyak Step-size, Part 2 Optimization Nuggets: Stochastic Polyak Step-size On the Convergence of the Unadjusted Langevin Algorithm The Russian Roulette: An Unbiased Estimator of the Limit Notes on the Frank-Wolfe Algorithm, Part III: backtracking line-search On the Link Between Optimization and Polynomials, Part 5 Optimization Nuggets: Implicit Bias of Gradient-based Methods Optimization Nuggets: Exponential Convergence of SGD On the Link Between Optimization and Polynomials, Part 4 On the Link Between Optimization and Polynomials, Part 3 On the Link Between Optimization and Polynomials, Part 2 On the Link Between Polynomials and Optimization, Part 1 How to Evaluate the Logistic Loss and not NaN trying Notes on the Frank-Wolfe Algorithm, Part II: A Primal-dual Analysis Three Operator Splitting Notes on the Frank-Wolfe Algorithm, Part I Optimization inequalities cheatsheet A fully asynchronous variant of the SAGA algorithm Hyperparameter optimization with approximate gradient Lightning v0.1 scikit-learn-contrib, an umbrella for scikit-learn related projects. SAGA algorithm in the lightning library On the consistency of ordinal regression methods Holdout cross-validation generator IPython/Jupyter notebook gallery PyData Paris - April 2015 Data-driven hemodynamic response function estimation Plot memory usage as a function of time
Second scikits.learn coding sprint
Fabian Pedregosa · 2010-09-12 · via Keep the gradient flowing

Las week took place in Paris the second scikits.learn sprint. It was two days of insane activity (115 commits, 6 branches, 33 coffees) in which we did a lot of work, both implementing new algorithms and fixing or improving old ones. This includes: * sparse version of Lasso by coordinate descent. Not (yet) merged into master, but can be looked from Olivier's branch. * new API for Pipeline. An example of this can be found in the document SVM-Anova: SVM with univariate feature selection. * documentation for the bayesian methods and cross validation: Vincent Michel contributed a lot of documentation, mainly taken from chapters of his thesis. * Ledoit-Wolf covariance estimation. * Pure python Fast ICA implementation. And the family picture, featuring (from left to right): Alexandre Gramfort, Bertrand Thirion, Virgine Fritsch, Gael Varoquaux, Vincent Michel, Olivier Grisel and me (taking the picture).