Zak Mhammedi

Postdoctoral Associate

Massachusetts Institute of Technology

About Me

I am a Postdoctoral Associate at MIT working with Sasha Rakhlin on theoretical aspects of Reinforcement Learning among other exciting topics. Before that I was a PhD student in computer science at the Australian National University, advised by Bob Williamson and Wouter Koolen.

Interests

Online Learning
Stochastic Optimization
Reinforcement Learning
Statistical Learning (Generalization Bounds)

Education

PhD in Computer Science, 2021
Australian National University
MPhil in Computer Science, 2016-2017
The University of Melbourne

Publications

Exploiting the Curvature of Feasible Sets for Faster Projection-Free Online Learning

Damped Online Newton Step for Portfolio Selection

Zak Mhammedi, Alexander Rakhlin

Efficient Projection-Free Online Convex Optimization with Membership Oracle

Risk-Monotonicity in Statistical Learning

NeurIPS 2021 (Oral Presentation${}^{\dagger}$)

Learning the Linear Quadratic Regulator from Nonlinear Observations

Zak Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford

Pre-Print Poster

Lipschitz and Comparator-Norm Adaptivity in Online Learning

Zak Mhammedi, Wouter M. Koolen

PAC-Bayesian Bound for the Conditional Value at Risk

NeurIPS 2020 (Spotlight Presentation${}^{\star}$)

Zak Mhammedi, Benjamin Guedj, Robert C. Williamson

Pre-Print Poster

Lipschitz Adaptivity with Multiple Learning Rates in Online Learning

Zak Mhammedi, Wouter M. Koolen, Tim Van Erven

PAC-Bayes Un-Expected Bernstein Inequality

Zak Mhammedi, Peter Grünwald, Benjamin Guedj

Geometry Aware Constrained Optimization Techniques for Deep Learning

Soumava Kumar Roy, Zak Mhammedi, Mehrtash Harandi

Constant Regret, Generalized Mixability, and Mirror Descent

NeurIPS 2018 (Spotlight Presentation${}^{\star}$)

Zak Mhammedi, Robert C Williamson

Efficient orthogonal parametrisation of Recurrent Neural Networks using householder reflections

Zak Mhammedi, Andrew Hellicar, Ashfaqur Rahman, James Bailey

Adversarial Generation of Real-time Feedback with Neural Networks for Simulation-based Training

Xingjun Ma, Sudanthi Wijewickrema, Shuo Zhou, Yun Zhou, Zakaria Mhammedi, Stephen O'Leary, James Bailey

${}^\dagger$About 1% of submissions at NeurIPS receive an Orale.

${}^\star$About 3% of submissions at NeurIPS receive a spotlight.

In all papers, authors are ordered according to their contribution.

Services

Program Commitee: COLT 2021, COLT 2022

Reviewer: COLT 2019, COLT 2020, NeurIPS 2020, NeurIPS 2021, ICML 2020, ICML 2021, ICML 2022, AISTATS 2022, JMLR