Adrian Weller, Academic Homepage at the University of Cambridge
January 25, 2017











Adrian Weller is a senior researcher in the Machine Learning Group at the University of Cambridge, in the Computational and Biological Learning Lab, advised by Prof Zoubin Ghahramani. He received a PhD in computer science (machine learning) at Columbia University, advised by Prof Tony Jebara.

Most of his academic research relates to graphical models but he is also very interested in other areas including: finance, anything on intelligence (natural or artificial), networks, robust learning, interpretability, deep learning, reinforcement learning, ethics, social policy, music and methods for big data.

He is also a faculty fellow at the Alan Turing Institute (ATI), an executive fellow at the Leverhulme Centre for the Future of Intelligence (CFI), and is affiliated with the Centre for Science and Policy (CSaP), and the Centre for the Study of Existential Risk (CSER).

Previously, Adrian held senior positions in financial trading. He continues to be active as an angel investor and advisor.

Contact: first name (dot) last name (at) eng.cam.ac.uk


Co-organized the following events

NIPS 2016 Symposium on Machine Learning and the Law

NIPS 2016 Workshop on Reliable Machine Learning in the Wild

ICML 2016 Workshop on Reliable Machine Learning in the Wild

DALI 2016 Workshop on Machine Learning and Society

NIPS 2015 Symposium on Algorithms Among Us: the Societal Impacts of Machine Learning


Publications

Full conference papers (refereed and archived)

M. Rowland, A. Pacchiano and A. Weller. Conditions beyond treewidth for tightness of higher-order LP relaxations. To appear in Artificial Intelligence and Statistics (AISTATS), 2017.

A. Weller. Characterizing tightness of LP relaxations by forbidding signed minors. In Uncertainty in Artificial Intelligence (UAI), 2016.
Poster

A. Weller. Uprooting and rerooting graphical models. In the International Conference on Machine Learning (ICML), 2016.
Slides Poster Video

O. Meshi, M. Mahdavi, A. Weller and D. Sontag. Train and test tightness of LP relaxations in structured prediction. In the International Conference on Machine Learning (ICML), 2016.

A. Weller, M. Rowland and D. Sontag. Tightness of LP relaxations for almost balanced models. In Artificial Intelligence and Statistics (AISTATS), 2016 [selected for oral presentation].
Slides Poster
Also presented by Mark Rowland at the International Conference on Principles and Practice of Constraint Programming (CP), 2016.
Slides

A. Weller* and J. Domke*. Clamping improves TRW and mean field approximations. In Artificial Intelligence and Statistics (AISTATS), 2016 [*equal contribution].
Poster

A. Weller. Bethe and related pairwise entropy approximations. In Uncertainty in Artificial Intelligence (UAI), 2015.
Poster

A. Weller. Revisiting the limits of MAP inference by MWSS on perfect graphs. In Artificial Intelligence and Statistics (AISTATS), 2015.
Poster
Also presented at the International Conference on Principles and Practice of Constraint Programming (CP), 2015.
Slides

A. Weller and T. Jebara. Clamping variables and approximate inference. In Neural Information Processing Systems (NIPS), 2014 [selected for oral presentation].
Slides Poster Video

A. Weller and T. Jebara. Approximating the Bethe partition function. In Uncertainty in Artificial Intelligence (UAI), 2014.
Poster

A. Weller, K. Tang, D. Sontag and T. Jebara. Understanding the Bethe approximation: When and how can it go wrong?. In Uncertainty in Artificial Intelligence (UAI), 2014.
Poster

A. Weller and T. Jebara. On MAP inference by MWSS on perfect graphs. In Uncertainty in Artificial Intelligence (UAI), 2013 [selected for oral presentation].

A. Weller and T. Jebara. Bethe bounds and approximating the global optimum. In Artificial Intelligence and Statistics (AISTATS), 2013.

Workshop papers (refereed)

N. Grgic-Hlaca, M. Zafar, K. P. Gummadi and A. Weller. The case for process fairness in learning: feature selection for fair decision making. In NIPS Symposium on Machine Learning and the Law, December 2016 [CFI-Clifford Chance notable paper award].

B. London, O. Meshi and A. Weller. Bounding the integrality distance of LP relaxations for structured prediction. In NIPS Workshop on Optimization for Machine Learning, December 2016.

K. Tang, A. Weller and T. Jebara. Network ranking with Bethe pseudomarginals. In NIPS Workshop on Discrete Optimization in Machine Learning, December 2013.

PhD thesis

A. Weller. Methods for Inference in Graphical Models. Columbia University, 2014.

Earlier work

A. Weller, D. Ellis and T. Jebara. Structured Prediction Models for Chord Transcription of Music Audio. International Conference on Machine Learning and Applications (ICMLA), December 2009.

These methods were used to provide a slight improvement to Dan Ellis' existing, powerful approach to chord transcription, which led to us submitting the best entry to the MIREX open competition that year, see results here. A brief description of the overall 2010 LabROSA chord recognition system is given here.

Selected presentations

Clamping variables and approximate inference, Microsoft Research, March 2016 Video (starting around 1:13:15)

MLSALT4 graphical models lecture 3: Junction tree algorithm, belief propagation, and variational methods, Feb 29

MLSALT4 graphical models lecture 2: Directed and Undirected Graphical Models, Feb 26

MLSALT4 graphical models lecture 1: An introduction to LP relaxations for MAP inference, Feb 15

Cambridge CBL tea talk on Penney's game, Nov 2015