Mark Rowland
Research Scientist | DeepMind
About Me
I'm a Research Scientist at DeepMind. I'm interested in many areas across machine learning, statistics, probability, and optimisation, and interactions with areas of pure maths such as group theory and optimal transport theory. To date, I've worked on areas including inference problems for discrete graphical models, Monte Carlo methods (particularly over non-Abelian groups), reinforcement learning, and probabilistic deep learning. Prior to joining DeepMind, I was a PhD student at Clare College, Cambridge University, co-supervised by Rich Turner at the Machine Learning Group and John Aston at Statslab. Before my PhD, I studied for a BA and MMath at Cambridge. I focused mainly on Probability and Algebra courses in the final year, and wrote my Part III essay "Mixing Times of Random Transpositions" under the supervision of Nathanaël Berestycki.
News
Our paper Antithetic and Monte Carlo Kernel Estimators for Partial Rankings has been accepted to Statistics and Computing.
Our paper Orthogonal Estimation of Wasserstein Distances has been accepted to AISTATS 2019.
Preprints
Gaussian Process Behaviour in Wide Deep Neural Networks
Alexander G. de G. Matthews, Mark Rowland, Jiri Hron, Richard E. Turner, Zoubin Ghahramani
Extended version of the paper appearing at ICLR 2018
Antithetic and Monte Carlo Kernel Estimators for Partial Rankings
Maria Lomeli, Mark Rowland, Arthur Gretton, Zoubin Ghahramani
Publications
Orthogonal Estimation of Wasserstein Distances
with Jiri Hron, Yunhao Tang, Krzysztof Choromanski, Tamas Sarlos and Adrian Weller
To appear at AISTATS 2019
Geometrically Coupled Monte Carlo Sampling
Mark Rowland*, Krzysztof Choromanski*, François Chalus, Aldo Pacchiano, Tamas Sarlos, Richard E. Turner, Adrian Weller [*equal contribution]
NIPS 2018
Structured Evolution with Compact Architectures for Scalable Policy Optimization
Krzysztof Choromanski*, Mark Rowland*, Vikas Sindhwani, Richard E. Turner, Adrian Weller [*equal contribution]
ICML 2018
Gaussian Process Behaviour in Wide Deep Neural Networks
Alexander G. de G. Matthews, Jiri Hron, Mark Rowland, Richard E. Turner, Zoubin Ghahramani
ICLR 2018
An Analysis of Categorical Distributional Reinforcement Learning
Mark Rowland, Marc G. Bellemare, Will Dabney, Rémi Munos, Yee Whye Teh
AISTATS 2018
The Geometry of Random Features
Krzysztof Choromanski*, Mark Rowland*, Tamas Sarlos, Vikas Sindhwani, Richard E. Turner, Adrian Weller [*equal contribution]
AISTATS 2018
Distributional Reinforcement Learning with Quantile Regression
Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos
AAAI 2018
Uprooting and Rerooting Higher-Order Graphical Models
Mark Rowland*, Adrian Weller* [*equal contribution]
NIPS 2017
The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings
Krzysztof Choromanski*, Mark Rowland*, Adrian Weller [*equal contribution]
NIPS 2017
Magnetic Hamiltonian Monte Carlo
Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard E. Turner
ICML 2017
[paper] [supplementary material] [poster]
Conditions Beyond Treewidth for Tightness of Higher-Order LP Relaxations
Mark Rowland, Aldo Pacchiano, Adrian Weller
AISTATS 2017
[paper] [supplementary material] [poster]
Also presented at the International Conference on Principles and Practices of Constraint Programming (CP 2017)
Black-box Alpha Divergence Minimization
Yingzhen Li, José Miguel Hernández-Lobato, Mark Rowland, Daniel Hernández-Lobato, Thang Bui, Richard E. Turner
ICML 2016
[paper] [supplementary material]
Tightness of LP Relaxations for Almost Balanced Models
Adrian Weller, Mark Rowland, David Sontag
AISTATS 2016
[paper] [supplementary material] [poster]
Also presented at the International Conference on Principles and Practices of Constraint Programming (CP 2016)
Teaching
During my PhD, I gave supervisions on Part IA Groups, Part IA Probability, Part IB Linear Algebra, and Part IB Statistics. I also gave examples classes for Part III Bayesian Modelling and Computation in Lent 2017, and ran MATLAB introduction sessions for Maths Tripos students.
Contact
Feel free to get in touch via mr504 [ at ] cam [ dot ] ac [ dot ] uk.