Mohammad Ghavamzadeh                  Google Scholar

 
 

2018


  1. Bullet    I will serve as an area chair for ICML-2019.


  1. Bullet    Our paper on “Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity” published at Journal of Artificial Intelligence Research (JAIR).


  1. Bullet    Two papers on “A Lyapunov-based Approach to Safe Reinforcement Learning” and “A Block Coordinate Ascent Algorithm for Mean-Variance Optimization” got accepted at NIPS-2018.


  1. Bullet    Two papers on “Path Consistency Learning in Tsallis Entropy Regularized MDPs” and “More Robust Doubly Robust Off-policy Evaluation” got accepted at ICML-2018.


  1. Bullet    I taught at the Deep Learning & Reinforcement Learning summer school organized by CIFAR and the Vector Institute at the University of Toronto in August.


  1. Bullet    Our paper on “Robust Locally-Linear Controllable Embedding” got accepted at AISTATS-2018.


  1. Bullet    Our paper on “Risk-Constrained Reinforcement Learning with Percentile Risk Criteria” published at Journal of Machine Learning Research (JMLR).


  1. Bullet    I served as an area chair for NIPS-2018 and ICML-2018, and as a senior program committee member for IJCAI-2018 and AAAI-2018.    


2017


  1. Bullet    A journal paper published: “Sequential Decision-making with Coherent Risk” at IEEE Transaction on Automatic Control (TAC).


  1. Bullet    Eight conference papers published: “Conservative Contextual Linear Bandits” at NIPS-2017, “Active Learning for Accurate Estimation of Linear Models”, “Bottleneck Conditional Density Estimation”, “Diffusion Independent Semi-Bandit Influence Maximization”, and “Online Learning to Rank in Stochastic Click Models” at ICML-2017, “Sequential Multiple Hypothesis Testing with Type I Error Control” at AISTATS-2017, “Predictive Off-Policy Evaluation for Nonstationary Decision Problems” and “Automated Data Cleansing through Meta-Learning” at IAAI-2017.


  1. Bullet    Together with Marek Petrik, we gave a tutorial on “Risk-averse Decision-making and Control” at AAAI-2017. (tutorial website)    


  1. Bullet    I gave an invited talk at the 2nd Asian Workshop on Reinforcement Learning in Seoul, South Korea on November 15, 2017.


  1. Bullet    I served as an area chair for NIPS-2017 and as a senior program committee member for AAAI-2017. 


2016   


  1. Bullet    Four journal papers published: “Analysis of Classification-based Policy Iteration Algorithms”, “Bayesian Policy Gradient and Actor-Critic Algorithms”, and “Regularized Policy Iteration for Non-Parametric Function Spaces” at Journal of Machine Learning Research (JMLR), and “Variance-constrained Actor-Critic Algorithms for Discounted and Average Reward MDPs” at Machine Learning Journal (MLJ).


  1. Bullet    Four conference papers published: “Safe Policy Improvement by Minimizing Robust Baseline Regret” at NIPS-2016, “Improved Learning Complexity in Combinatorial Pure Exploration Bandits” at AISTATS-2016, “Proximal Gradient Temporal Difference Learning Algorithms” at the sister conference best paper track at IJCAI-2016, and “Graphical Model Sketch” at ECML-2016. 


  1. Bullet    I gave an invited talk at the 13th European Workshop on Reinforcement Learning (EWRL) in Barcelona on December 3-4, 2016.


  1. Bullet    I served as a senior program committee member for IJCAI-2016 and ECML-2016.


2015   


  1. Bullet    Three journal papers published: “Approximate Modified Policy Iteration and its Application to the Game of Tetris” at Journal of Machine Learning Research (JMLR), “Classification-based Approximate Policy Iteration” at IEEE Transactions on Automatic Control (TAC), and “Bayesian Reinforcement Learning: A Survey” at Foundation and Trends in Machine Learning. 


  1. Bullet    Five conference papers published: “High Confidence Off-Policy Evaluation” at AAAI-2015, “Maximum Entropy Semi-Supervised Inverse Reinforcement Learning” at IJCAI-2015, “Building Personalized Ad Recommendation Systems for Life-Time Value Optimization with Guarantees” at IJCAI-2015, “High Confidence Policy Improvement” at ICML-2015, and “Policy Gradient for Coherent Risk Measures” at NIPS-2015.


  1. Bullet    Our paper entitled “Finite-Sample Analysis of Proximal Gradient TD Algorithms” won the Facebook best student paper award at UAI-2015


  1. Bullet    I co-chaired two workshops: 12th European Workshop on Reinforcement Learning (EWRL-12) as a workshop at ICML-2015 and “Machine Learning in eCommerce” at NIPS-2015.


  1. Bullet    My student, Victor Gabillon, won the AFIA (French Association for Artificial Intelligence) prize for the 2nd best Ph.D. thesis (completed in 2014) on artificial intelligence in France.


  1. Bullet    I served as a senior program committee member for IJCAI-2015. 


2014


  1. Bullet    A paper published: “Algorithms for CVaR Optimization in MDPs” at NIPS-2014.


  1. Bullet    I co-chaired three workshops: “Sequential Decision-Making with Big Data” at AAAI-2014, Customers Value Optimization in Digital Marketing” at ICML-2014, and Large-scale Reinforcement Learning and Markov Decision Problems” at NIPS-2014.


  1. Bullet    I successfully defended my “Habilitation à Diriger des Recherches” (HDR) thesis and graduated my Ph.D. student Victor Gabillon in June 2014. Victor will be a postdoc with Prof. Peter Bartlett at UC Berkeley starting October 2014.


  1. Bullet    I served as an area chair for NIPS-2014.


Professional Experience


Senior Research Scientist (on leave from INRIA)

Facebook AI Research (FAIR), CA  (Oct. 2018 - present)


Senior Research Scientist (on leave from INRIA)

Google DeepMind, CA  (Jun. 2017 - Oct. 2018)


Senior Analytics Researcher

Adobe Research, San Jose, CA  (Oct. 2013 - Jun. 2017)


Habilitation à Diriger des Recherches (HDR)

Université Lille 1, France  (June 2014)


Chargé de Recherche  CR1 (on leave at Adobe)

INRIA Lille - Team SequeL, France  (2010 - Oct. 2013)


Chargé de recherche  CR2

INRIA Lille - Team SequeL, France  (2008 - 2010)


postdoctoral Fellow

University of Alberta, Canada  (2005 - 2008)


Ph.D. in Computer Science

University of Massachusetts Amherst, USA  (2001 - 2005)




Research Interests


Machine Learning

Artificial Intelligence

Control

Reinforcement Learning




Contact Information


Mailing Address

1 Facebook Way

Menlo Park, CA 94025


Office

Facebook Building 21


Email

mgh at fb dot com

mohammad dot ghavamzadeh at inria dot fr

 

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