
SGD in the Large: Averagecase Analysis, Asymptotics, and Stepsize Criticality
We propose a new framework, inspired by random matrix theory, for analyz...
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Averagecase Acceleration for Bilinear Games and Normal Matrices
Advances in generative modeling and adversarial learning have given rise...
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Halting Time is Predictable for Large Models: A Universality Property and Averagecase Analysis
Averagecase analysis computes the complexity of an algorithm averaged o...
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Averagecase Acceleration Through Spectral Density Estimation
We develop a framework for designing optimal quadratic optimization meth...
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A Test for Shared Patterns in Crossmodal Brain Activation Analysis
Determining the extent to which different cognitive modalities (understo...
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SciPy 1.0Fundamental Algorithms for Scientific Computing in Python
SciPy is an open source scientific computing library for the Python prog...
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The Difficulty of Training Sparse Neural Networks
We investigate the difficulties of training sparse neural networks and m...
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Information matrices and generalization
This work revisits the use of information criteria to characterize the g...
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FrankWolfe Splitting via Augmented Lagrangian Method
Minimizing a function over an intersection of convex sets is an importan...
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Adaptive Three Operator Splitting
We propose and analyze a novel adaptive step size variant of the DavisY...
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FrankWolfe with Subsampling Oracle
We analyze two novel randomized variants of the FrankWolfe (FW) or cond...
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Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization
Due to their simplicity and excellent performance, parallel asynchronous...
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On the convergence rate of the three operator splitting scheme
The three operator splitting scheme was recently proposed by [Davis and ...
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ASAGA: Asynchronous Parallel SAGA
We describe ASAGA, an asynchronous parallel version of the incremental g...
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Hyperparameter optimization with approximate gradient
Most models in machine learning contain at least one hyperparameter to c...
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Machine Learning for Neuroimaging with ScikitLearn
Statistical machine learning methods are increasingly used for neuroimag...
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Second order scattering descriptors predict fMRI activity due to visual textures
Second layer scattering descriptors are known to provide good classifica...
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Learning to rank from medical imaging data
Medical images can be used to predict a clinical score coding for the se...
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Improved brain pattern recovery through ranking approaches
Inferring the functional specificity of brain regions from functional Ma...
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Fabian Pedregosa
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Currently a Marie SkłodowskaCurie postdoctoral fellow between ETH Zurich and UC Berkeley.