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

(September 2018)

I've just joined DeepMind as a Research Scientist.

Our paper Geometrically Coupled Monte Carlo Sampling has been accepted to NIPS 2018 with spotlight presentation.

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

[preprint]


Antithetic and Monte Carlo Kernel Estimators for Partial Rankings

Maria Lomeli, Mark Rowland, Arthur Gretton, Zoubin Ghahramani

[preprint]


Publications


Geometrically Coupled Monte Carlo Sampling

Mark Rowland*, Krzysztof Choromanski*, François Chalus, Aldo Pacchiano, Tamas Sarlos, Richard E. Turner, Adrian Weller [*equal contribution]

To appear at NIPS 2018

[preprint]


Structured Evolution with Compact Architectures for Scalable Policy Optimization

Krzysztof Choromanski*, Mark Rowland*, Vikas Sindhwani, Richard E. Turner, Adrian Weller [*equal contribution]

ICML 2018

[preprint] [poster]


Gaussian Process Behaviour in Wide Deep Neural Networks

Alexander G. de G. Matthews, Jiri Hron, Mark Rowland, Richard E. Turner, Zoubin Ghahramani

ICLR 2018

[preprint]


An Analysis of Categorical Distributional Reinforcement Learning

Mark Rowland, Marc G. Bellemare, Will Dabney, Rémi Munos, Yee Whye Teh

AISTATS 2018

[preprint]


The Geometry of Random Features

Krzysztof Choromanski*, Mark Rowland*, Tamas Sarlos, Vikas Sindhwani, Richard E. Turner, Adrian Weller [*equal contribution]

AISTATS 2018

[preprint]


Distributional Reinforcement Learning with Quantile Regression

Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos

AAAI 2018

[paper]


Uprooting and Rerooting Higher-Order Graphical Models

Mark Rowland*, Adrian Weller* [*equal contribution]

NIPS 2017

[paper] [poster]


The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings

Krzysztof Choromanski*, Mark Rowland*, Adrian Weller [*equal contribution]

NIPS 2017

[paper] [poster]


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.