Adaptive Sketch-and-Project Methods for Solving Linear Systems, 2019.


Towards closing the gap between the theory and practice of SVRG, Neurips 2019.

Preprint Code

. RSN: Randomized Subspace Newton, Neurips 2019.


. Optimal mini-batch and step sizes for SAGA, ICML 2019.

Preprint Code Proceedings

SGD: general analysis and improved rates, (extended oral presentation) ICML 2019.

Preprint Proceedings

Improving SAGA via a probabilistic interpolation with gradient descent, 2018.


Stochastic quasi-gradient methods: variance reduction via Jacobian sketching, 2018.

Preprint Code

Accelerated stochastic matrix inversion: general theory and speeding up BFGS rules for faster second-order optimization, NIPS, 2018.

Preprint Code Proceedings Poster

Greedy stochastic algorithms for entropy-regularized optimal transport problems, AISTATS, 2018.

Preprint Proceedings Poster

Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods, AISTATS (Oral presenation), 2018.

Preprint Code Proceedings Slides Poster

Randomized quasi-Newton updates are linearly convergent matrix inversion algorithms, SIAM Journal on Matrix Analysis and Applications, 2017.

Preprint Code Journal Slides

Linearly Convergent Randomized Iterative Methods for Computing the Pseudoinverse, 2016.

Preprint Code Slides

Sketch and Project: Randomized Iterative Methods for Linear Systems and Inverting Matrices, PhD Dissertation, School of Mathematics, The University of Edinburgh, 2016.

Preprint Code Slides

Stochastic Block BFGS: Squeezing More Curvature out of Data, ICML, 2016.

Preprint Code Proceedings Slides Poster

Stochastic dual ascent for solving linear systems, 2015.

Preprint Code

Randomized iterative methods for linear systems, SIAM Journal on Matrix Analysis and Applications, 2015.

Preprint Journal Slides Code Most downloaded on SIMAX

High order reverse automatic differentiation with emphasis on the third order, Mathematical Programming, 2014.

Preprint Journal Slides Code

Computing the sparsity pattern of Hessians using automatic differentiation, ACM Transactions on Mathematical Software, 2014.

Preprint Journal Code

A new framework for Hessian automatic differentiation, Optimization Methods and Software, 2012.

Preprint Journal Code

Reports and Notes

Train Positioning Using Video Odometry, 2014.


Action constrained quasi-Newton methods, Technical Report ERGO 14-020, 2014

Report Code

Conjugate Gradients: The short and painful explanation with oblique projections


Hessian matrices via automatic differentiation, State University of Campinas technical report and Msc Thesis 2011

Report Master's thesis

Efficient calculation of derivatives through graph coloring, State University of Campinas technical report, undergraduate project 2009

Report I Report II

Recent & Upcoming Talks

Aug 5, 2019
Expected smoothness is the key to understanding the mini-batch complexity of stochastic gradient methods Slides


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