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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
scikit-learn coding sprint in Paris
Fabian Pedregosa · 2011-04-02 · via Keep the gradient flowing

Yesterday was the scikit-learn coding sprint in Paris. It was great to meet with old developers (Vincent Michel) and new ones: some of whom I was already familiar with from the mailing list while others came just to say hi and get familiar with the code. It was really great to have people from such different backgrounds discuss on concrete problems and getting things done. A lot of work was done, most of it unmerged yet, but if I had to highlight the three most important for me, that would be the the merge of the hcluster2 branch, the awesome work of thouis in replacing the C++ interface to the ball_tree with a Cython one and suppport for Python3 (not bug-free but imports OK). As for me, I've been working mostly in providing efficient cross-validatation for Support Vector Machines. The status of this is: low-level API seems to work fine (scikits.learn.svm.libsvm.cross_validation) but high-level API still needs some work. This is the picture featuring (most) of the people that were at the sprint around 16h in Logilab's headquarters.