Publications and Output

  • Gupta, Ankush and Vedaldi, Andrea and Zisserman, Andrew.  Synthetic Data for Text Localisation in Natural Images The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) June 2016
  • Ghoshal, Sid and S. Roberts, Extracting Predictive Information from Heterogeneous Data Streams using Gaussian Processes, Algorithmic Finance, vol. 5, no. 1-2, pp. 21-30, 2016.
  • Leonard Hasenclever, Stefan Webb, Thibaut Lienart, Sebastian Vollmer, Balaji Lakshminarayanan, Charles Blundell, Yee Whye Teh, Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server. Journal of Machine Learning Research 18 (2017) 1-37.
  • Learning Grimaces by Watching TV”  – Sam Albanie and Andrea Vedaldi, accepted into BMVC 2016.
  • “James Thewlis, Shuai Zheng, Philip H. S. Torr, Andrea Vedaldi.  Fully-Trainable Deep Matching. British  Machine Vision Conference (BMVC), 2016.
  • Bartlett, O, C. Gurau, L. Marchegiani, and I. Posner, “Enabling Intelligent Energy Management for Robots using Publicly Available Maps,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, South Korea, 2016. PDF
  • Berrada, Leo, Trusting SVM for Piecewise Linear CNNs” has been accepted at ICLR 2017 (International Conference on Learning Representations). It is available on arXiv at https://arxiv.org/abs/1611.02185
  • Ghoshal, S and Roberts, S. Reading the Tea Leaves: A Neural Network Perspective on Technical Trading. Knowledge Discovery and Data Mining (KDD), 2017.
  • Cobb, A and Markham, A and Roberts, S. Learning from lions: inferring the utility of agents from their trajectories. Available at https://arxiv.org/pdf/1709.02357.pdf
  • Jakob Foerster, Nantas Nardelli, Gregory Farquhar, Triantafyllos, Philip H.S. Torr, Pushmeet Kohli, Shimon Whiteson, Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning.   Accepted at ICML2017. Available at https://arxiv.org/abs/1702.08887
  • Unsupervised object learning from dense invariant image labelling, J.Thewlis and H. Bilen and A. Vedaldi, Proceedings of Advances in Neural Information Processing Systems (NIPS), 2017 (oral presentation)
  • Unsupervised learning of object landmarks by factorized spatial embeddings, J. Thewlis and H. Bilen and A. Vedaldi, Proceedings of the International Conference on Computer Vision (ICCV), 2017 (oral presentation)
  • Zand, Jaleh & Roberts, S. MiDGaP: Mixture Density Gaussian Processes.  NIPS time series workshop 2017.
  • Closed-loop quantitative verification of rate-adaptive pacemakers. N Paoletti, A Patane, M Kwiatkowska. ACM Transactions on Cyber-Physical Systems, to appear, 2018
  • Stefan Webb and Yee Whye Teh. A Tighter Monte Carlo Objective with Renyi divergence Measures. NIPS2016 Workshop in Bayesian Deep Learning.
  • Stefan Webb, Adam Golinski, Robert Zinkov and Frank Wood. Faithful Model Inversion Substantially Improves Auto-encoding Variational Inference. NIPS2017 Workshop in Bayesian Deep Learning.
  • Stefan Webb, Adam Golinski, Robert Zinkov, N. Siddharth, Yee Whye Teh, and Frank Wood. Faithful Model Inversion Substantially Improves Auto-encoding Variational Inference. https://arxiv.org/abs/1712.00287
  • Foerster, J., Farquhar, G., Afouras, T., Nardelli, N., & Whiteson, S. (2018). Counterfactual multi-agent policy gradients. 32nd AAAI Conference on Artificial Intelligence (AAAI’18)
  • Farquhar, G., Rocktäschel, T., Igl, M., & Whiteson, S.  TreeQN and ATreeC: Differentiable Tree Planning for Deep Reinforcement Learning. ICLR 2018. (To appear)
  • Border, J. D. Gammell, and P. Newman, “Surface Edge Explorer (SEE): Planning Next Best Views Directly from 3D Observations,” ICRA, 2018.
  •  Le, Tuan Anh, Igl, Maximilian, Jin, Tom, Rainforth, Tom, Wood, Frank. Auto-encoding sequential Monte Carlo. In ICLR (To Appear), 2018.
  • D. Novotny*, S. Albanie*, D. Larlus, A. Vedaldi, Self-supervised Learning of Geometrically Stable Features Through Probabilistic Introspection, CVPR, 2018. (* denotes equal contribution)
  • A. Nagrani, S. Albanie, A. Zisserman, Seeing Voices and Hearing Faces: Cross-modal biometric matching, CVPR, 2018.  (* denotes equal contribution)
  • S. Albanie, H. Shakespeare and T. Gunter, Unknowable Manipulators: Social Network Curator Algorithms, (NIPS 2016 Symposium: Machine Learning and the Law)
  • Cornish, R., Wood, F., & Yang, H. (2017). “Towards a testable notion of generalisation for generative adversarial networks”. In NIPS Workshop on Deep Learning: Bridging Theory and Practice.
  • Baydin, A. G., Cornish, R., Martínez Rubio, D., Schmidt, M., & Wood, F. (2017).  Online Learning Rate Adaptation with Hypergradient Descent.  In Sixth International Conference on Learning Representations (ICLR), Vancouver, Canada, April 30 – May 3, 2018.
  • Adam D. Cobb, Richard Everett, Andrew Markham, Stephen J. Roberts. (2018). Identifying Sources and Sinks in the Presence of Multiple Gaents with Gaussian Process Vector Calculus, https://arxiv.org/abs/1802.10446
  • When Your Fitness Tracker Betrays You: Quantifying the Predictability of Biometric Features Across Contexts. Simon Eberz, Giulio Lovisotto, Andrea Patané, Marta Kwiatkowska, Vincent Lenders, and Ivan Martinovic. IEEE Symposium on Security and Privacy (S&P), San Francisco, CA, USA, May 2018
  • Loss-Calibrated Approximate Inference in Bayesian Neural Betworks. Adam D. Cobb, Stephen J. Roberts, Yarin Gal.  https://arxiv.org/abs/1805.03901