Google Scholar; Baldi, P, Sadowski, P, and Whiteson, D. Searching for exotic particles in high-energy physics with deep learning. I am a Staff Research Scientist in the Google Brain team. Computation and Neural Systems, California Institute of Technology, Pasadena, CA 91125, U.S.A. roweis@gatsby.ucl.ac.uk. [1611.02247] Q-Prop: Sample-Efficient Policy Gradient with ... In the case of mixture models, local maxima often involve having too many components of a mixture model in one part of the space and too few in another, widely separated part of . Zoubin Ghahramani. Zoubin Ghahramani. arXiv preprint arXiv:2106.04013. , 2021. Zoubin Ghahramani Professor, University of Cambridge, and Distinguished Researcher, Google Verified email at eng.cam.ac.uk Bernhard Schölkopf Director, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and Distinguished Verified email at tuebingen.mpg.de Research interests: Gaussian Processes, Sensorimotor Control, Computational Neuroscience, Bayesian Machine Learning, Statistics Google Scholar, Springer, CiteSeer, Microsoft Academic Search, Scirus, DBlife Description Professor Zoubin Ghahramani is Professor of Information Engineering, Department of Engineering , University of Cambridge . B Bloem-Reddy, A Foster, E Mathieu, YW Teh. Model Reductions for Inference: Generality of Pairwise ... Zoubin Ghahramani Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, U.K. zoubin@eng.cam.ac.uk. The rise of powerful AI will be either the best or the worst thing ever to happen to humanity. Not only can adversarial images be generated easily, but these images will often be adversarial for networks trained on disjoint subsets of data or with . International Conference on Machine Learning (ICML), 1183-1192. Zoubin Ghahramani Professor, University of Cambridge, and Distinguished Researcher, Google Verified email at eng.cam.ac.uk Robert D Nowak Nosbusch Professor in Engineering and Wisconsin Institute for Discovery, UW-Madison Verified email at wisc.edu Awards & Achievements. [1608.00853] A study of the effect of JPG compression on ... 5. 2015 - Fellow of the Royal Society (UK) Profile was last updated on May 25th, 2021. Policy gradient methods are a widely used class of model-free reinforcement learning algorithms where a state-dependent baseline is used to reduce gradient estimator variance. 497. Received: September 14 2011 . Neural network image classifiers are known to be vulnerable to adversarial images, i.e., natural images which have been modified by an adversarial perturbation specifically designed to be imperceptible to humans yet fool the classifier. ( 2016 ). The ones marked * may be different from the article in the profile. I am a PhD Candidate in Engineering (Probabilistic Machine Learning) at the University of Cambridge, working under the supervision of prof. Zoubin Ghahramani.I am also part-time affiliated to Secondmind (formerly PROWLER.io), where I fulfil the role of senior machine learning researcher.. PDF Dropout as a Bayesian Approximation: Representing Model ... N Ueda, R Nakano, Z Ghahramani, GE Hinton. Google Scholar; D Koller, D McAllester, and A Pfeffer. Are arm trajectories planned in kinematic or dynamic ... CoRR abs/1706.00387 (2017) Abstract. Google Scholar; Barber, D and Bishop, C M. Ensemble learning in Bayesian neural networks. GitHub Pages - Vincent DutordoirVariational Learning for Switching State-Space Models ... Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Bernhard Schölkopf, Sergey Levine: Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning. A theoretically grounded application of dropout in recurrent neural networks. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States, Christopher J. C. Burges, Léon Bottou, Zoubin Ghahramani, and Kilian Q. Weinberger (Eds.). The research done by this centre will be crucial . Professor, University of Cambridge, and Distinguished Researcher, Google. Learning from labeled and unlabeled data with label propagation. Shixiang (Shane) Gu - Research Scientist - Google | LinkedIn However, existing VAEs can still not directly handle data that are heterogenous (mixed continuous and discrete) or incomplete (with missing data at random), which is indeed common in real-world . Zoubin Ghahramani, Cambridge University, Machine Learning, Gatsby Computational Neuroscience Unit, University College London. arXiv preprint arXiv:1807.03113. , 2018. The Future is Log-Gaussian: ResNets and Their Infinite-Depth-and-Width Limit at Initialization. Zoubin Ghahramani Professor, University of Cambridge, and Distinguished Researcher, Google Verified email at eng.cam.ac.uk Jennifer Listgarten Professor, UC Berkeley EECS and Center for Computational Biology Verified email at berkeley.edu About. Zoubin Ghahramani, Geoffrey E. Hinton. . A Unifying Review of Linear Gaussian Models. This paper presents a tutorial introduction to the use of variational methods for inference and learning in graphical models (Bayesian networks and Markov random fields). ( 2016 ). NATO ASI SERIES F COMPUTER AND SYSTEMS SCIENCES, 168:215-238, 1998. Several recent papers extend the baseline to depend on both the state and action and suggest that this significantly reduces variance and improves sample efficiency without introducing bias into the gradient estimates . 2016. Our aim at the Leverhulme Centre for the Future of Intelligence is to bring together the best of human intelligence so that we can make the most of machine intelligence. Matej Balog, Nilesh Tripuraneni, Zoubin Ghahramani, Adrian Weller. MB Li, M Nica, DM Roy. Charles Sutton Google, University of Edinburgh Verified email at inf.ed.ac.uk. Affiliations. 2015 - Fellow of the Royal Society (UK) Profile was last updated on May 25th, 2021. Zoubin Ghahramani FRS CONTACT DETAILS Department: Department of Engineering University of Cambridge . Google - Cited by 5,052 - Machine Learning - Artificial Intelligence - Computer Vision - Natural Language Processing . The EM algorithm for Gaussian mixture models often gets caught in local maxima of the likelihood which involve having too many Gaussians in one part of the space and too few in another, widely separated part of the space. Google Scholar; Xiaojin Zhu and Zoubin Ghahramani. 2000. Neural Computation, vol. 1190. Zoubin Ghahramani, Cambridge University, Machine Learning, Gatsby Computational Neuroscience Unit, University College London. 463-469 Spiking Boltzmann Machines Zoubin Ghahramani. (2002). Proceedings of the Pan-Sydney area workshop on Visual information processing …. Zoubin Ghahramani FRS (Persian: زوبین قهرمانی; born 8 February 1970) is a British-Iranian researcher and Professor of Information Engineering at the University of Cambridge.He holds joint appointments at University College London and the Alan Turing Institute. (Gal & Ghahramani, 2016) ⇒ Yarin Gal, and Zoubin Ghahramani. Research interests: Gaussian Processes, Sensorimotor Control, Computational Neuroscience, Bayesian Machine Learning, Statistics GK Dziugaite, K Hsu, W Gharbieh, G Arpino, D Roy. " Dropout As a Bayesian Approximation: Representing Model Uncertainty in Deep Learning ." Zoubin Ghahramani FRS CONTACT DETAILS Department: Department of Engineering University of Cambridge . 2021. ZG Benjamin Bloem-Reddy, Emile Mathieu, Adam Foster, Tom Rainforth, Yee Whye . ICML 2017 (best paper honorable mention award). Zoubin Ghahramani Professor, University of Cambridge, and Distinguished Researcher, Google Verified email at eng.cam.ac.uk R Devon Hjelm Microsoft Research, University of Montreal, Mila Verified email at microsoft.com International Conference on Artificial Intelligence and Statistics, 604-612. 2292-2300. Advances in neural information processing systems, 5574-5584. , 2017. Their combined citations are counted only for the first article. 2018. In the case of mixture models, local maxima often involve having too many components of a mixture model in one part of the space and too few in another, widely separated part of . Optimization models of trajectory planning, as well as . Google Scholar Leonard, J., Tardós, J. D., Thrun, S., and Choset, H. editors. A defining property of HMMs is that the time . Advances in neural information processing systems 29, 1019-1027. , 2016. Author notes. Zoubin Ghahramani Professor, University of Cambridge, and Distinguished Researcher, Google Verified email at eng.cam.ac.uk Marc Deisenroth University College London Verified email at ucl.ac.uk Joaquin Quiñonero Candela Distinguished Tech Lead for Responsible AI at Facebook Verified email at fb.com We present a split-and-merge expectation-maximization (SMEM) algorithm to overcome the local maxima problem in parameter estimation of finite mixture models. Google Focused Research Award for the \Automated Statistician", 2013, $750;000 . Parametric mixture models for multi-labeled text. Dean's Scholar Award, University of Pennsylvania, 1988 University Scholar, University of Pennsylvania, 1986 . Deep Bayesian Active Learning with Image Data. Zoubin Ghahramani Professor, University of Cambridge, and Distinguished Researcher, Google Verified email at eng.cam.ac.uk Michael I. Jordan Professor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC Berkeley Verified email at cs.berkeley.edu 2003. TD-style methods, such as off-policy actor-critic and Q-learning . ICML 2017. Semi-supervised learning using gaussian fields and harmonic functions. Finale Doshi-Velez is an assistant professor at the Paulson School of Engineering and Applied Sciences at Harvard. Third International Conference on Information Technology and Applications …. 831-864 Learning to Parse Images Geoffrey E. Hinton, Zoubin Ghahramani, Yee Whye Teh NIPS (1999), pp. 912--919. 2002. Google Scholar. Google Scholar. We briefly review basic models in unsupervised learning, including factor analysis, PCA, mixtures of Gaussians, ICA, hidden Markov models, state-space models, and many variants and extensions. Zoubin Ghahramani Professor, University of Cambridge, and Distinguished Researcher, Google Verified email at eng.cam.ac.uk R Devon Hjelm Microsoft Research, University of Montreal, Mila Verified email at microsoft.com We present a split-and-merge expectation-maximization (SMEM) algorithm to overcome the local maxima problem in parameter estimation of finite mixture models. He was Associate Research Professor at Carnegie Mellon . Google Scholar Daniel M. Wolpert, Zoubin Ghahramani, and Michael I. Jordan Science • 29 Sep 1995 • Vol 269 , Issue 5232 • pp. Zoubin Ghahramani. The system can't perform the operation now. Unsupervised learning can be motivated from information theoretic and Bayesian principles. He was a founding Cambridge Director of the Alan Turing Institute, the UK's national institute for data . Y Gal. Dean tasked research director Zoubin Ghahramani with clarifying the rules. Google Focused Research Award for the \Automated Statistician", 2013, $750;000 . Uncertainty in Deep Learning. Research.com Ranking is based on Google Scholar H-Index. Zoubin Ghahramani Professor, University of Cambridge, and Distinguished Researcher, Google Verified email at eng.cam.ac.uk Peter Bartlett Professor, EECS and Statistics, UC Berkeley Verified email at cs.berkeley.edu Introduction. 2002. Author notes. Present address: Department of Computer Science, University of Toronto, Toronto, Canada, M5S 3H5. View Shixiang (Shane) Gu's profile on LinkedIn, the world's largest professional community. Zoubin Ghahramani Professor, University of Cambridge, and Distinguished Researcher, Google Verified email at eng.cam.ac.uk. Sam Roweis, Sam Roweis. Google Scholar. He is also Deputy Director of the Leverhulme Centre for the Future of Intelligence, and a Fellow of St John's College. We derive the EM algorithm and give an overview of fundamental . See the complete profile on LinkedIn and . SMEM algorithm for mixture models. Nature communications, 5, 2014. Shixiang (Shane) has 8 jobs listed on their profile. Author and Article Information Zoubin Ghahramani Gatsby Computational Neuroscience Unit, University College London, London WC1N 3AR, U.K. Geoffrey E. Hinton Gatsby Computational Neuroscience Unit, University College London, London WC1N 3AR, U.K. . Hidden Markov models (HMMs) are a rich family of probabilistic time series models with a long and successful history of applications in natural language processing, speech recognition, computer vision, bioinformatics, and many other areas of engineering, statistics and computer science. We do not yet know which. Zoubin Ghahramani - Google Scholar Now scholar.google.co.uk Professor, University of Cambridge, and Chief Scientist, Uber - Cited by 61,061 - Machine Learning - Bayesian Statistics - Neural Networks - Artificial Intelligence . Google Scholar. Convolutional neural networks (CNNs) work well on large datasets. In compari-son, Bayesian models offer a mathematically grounded framework to reason about model un- 1. We present an efficient Bayesian CNN, offering better robustness to over-fitting on small data than traditional approaches. Dean's Scholar Award, University of Pennsylvania, 1988 University Scholar, University of Pennsylvania, 1986 . This "Cited by" count includes citations to the following articles in Scholar. Download citation file: We present a number of examples of graphical models, including the QMR-DT database, the sigmoid belief network, the Boltzmann machine, and several variants of hidden Markov models, in which it is infeasible to run exact . Welcome to my webspace! The following articles are merged in Scholar. 2020. However such tools for regression and classification do not capture model uncertainty. Search for other works by this author on: This Site. Batch policy gradient methods offer stable learning, but at the cost of high variance, which often requires large batches. But labelled data is hard to collect, and in some applications larger amounts of data are not available. This is by placing a . Awards & Achievements. There are several invariant features of pointto-point human arm movements: trajectories tend to be straight, smooth, and have bell-shaped velocity profiles. Abstract. Zoubin Ghahramani Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, . Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard Turner. Machine Learning Bayesian Statistics Neural Networks Artificial Intelligence. We present a new EM algorithm which performs split and merge operations on the Gaussians to escape from these configurations. Affiliations. , 2016. Google Scholar. 2012 IEEE Computer Society Conference on Computer Vision and Pattern …. Neural computation 12 (9), 2109-2128. , 2000. Lost Relatives of the Gumbel Trick. Zoubin Ghahramani. I have broad in interests in machine learning and artificial intelligence, with a particular focusses on scalable methods, vision, language, and generalization. "We need to be comfortable with that discomfort" of self-critical research, Ghahramani said, according to Reuters. Dorthe Malzahn, Manfred Opper, Thomas G. Dietterich (Editor), Suzanna Becker (Editor), Zoubin Ghahramani (Editor) Research output : Contribution to journal › Article › peer-review Overview One approach to accounting for these data is via optimization theory; a movement is specified implicitly as the optimum of a cost function, e.g., integrated jerk or torque change. Sampling and inference for Beta Neutral-to-the-Left models of sparse networks. A Kendall, Y Gal. Model-free deep reinforcement learning (RL) methods have been successful in a wide variety of simulated domains. A/Prof Richard Yi Da Xu. This algorithm uses two novel criteria for . Zoubin Ghahramani. Y Gal, R Islam, Z Ghahramani. 2353. The problem then is how to use CNNs with small data -- as CNNs overfit quickly. I work on the development of probabilistic machine learning models for autonomous . Google Scholar; James Robert Lloyd, David Duvenaud, Roger Grosse, Joshua B Tenenbaum, and Zoubin Ghahramani. (Gal & Ghahramani, 2016) ⇒ Yarin Gal, and Zoubin Ghahramani. Shakir completed his PhD with Zoubin Ghahramani in 2010 at the University of Cambridge, where he was a Commonwealth Scholar to the United Kingdom and a member of St John's College. 2017. Y Gal, Z Ghahramani. I'm a machine learning Ph.D candidate in the Institute for Adaptive and Neural Computation at the University of Edinburgh, working with Charles Sutton.Before this, I worked with Zoubin Ghahramani for one and a half years in the Cambridge Machine Learning Group.I'm a core member of the team behind Turing, a popular probabilistic programming language in Julia. Automatic construction and Natural-Language description of nonparametric regression models. 1880 - 1882 • DOI: 10.1126/science.7569931 PREVIOUS ARTICLE . Department of Brain . Google Scholar; Xiaojin Zhu, Zoubin Ghahramani, and John D Lafferty. Close. "Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering", Advances in Neural Information Processing Systems 14: Proceedings of the 2001 Conference, Thomas G. Dietterich, Suzanna Becker, Zoubin Ghahramani. 12 (2000), pp. ZOUBIN GHAHRAMANI zoubin@gatsby.ucl.ac.uk Gatsby Computational Neuroscience Unit, University College London WC1N 3AR, UK TOMMI S. JAAKKOLA tommi@ai.mit.edu Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA LAWRENCE K. SAUL lsaul@research.att.edu AT&T Labs-Research, Florham Park, NJ 07932, USA Editor: David Heckerman Abstract. On the role of data in PAC-Bayes. Department of Brain . Advances in neural information processing systems, 737-744. Research.com Ranking is based on Google Scholar H-Index. Zoubin Ghahramani is Professor of Information Engineering at the University of Cambridge and Chief Scientist at Uber. Present address: Department of Computer Science, University of Toronto, Toronto, Canada, M5S 3H5. Zoubin Ghahramani Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, . Google Scholar Quantitative criticism of literary relationships. Variational autoencoders (VAEs), as well as other generative models, have been shown to be efficient and accurate for capturing the latent structure of vast amounts of complex high-dimensional data. and has been a Fellow of St John's College, Cambridge since 2009. CoRR abs/1706.00387 (2017) Zoubin Ghahramani Professor . " Dropout As a Bayesian Approximation: Representing Model Uncertainty in Deep Learning ." In Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Bernhard Schölkopf, Sergey Levine: Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning. Zoubin Ghahramani Professor, University of Cambridge, and Distinguished Researcher, Google Verified email at eng.cam.ac.uk Kai Xu / 徐锴 University of Edinburgh Verified email at ed.ac.uk Yee Whye Teh Professor of Statistical Machine Learning, Oxford, Research Scientist, DeepMind Verified email at stats.ox.ac.uk Prior to joining Google, I received a PhD from the Cambridge Computational and Biological Learning lab . Zoubin Ghahramani. University of Cambridge. Articles Cited by Public access Co-authors. Zoubin Ghahramani Professor, University of Cambridge, and Distinguished Researcher, Google Verified email at eng.cam.ac.uk Edith A. Fernández-Figueroa Investigador en Ciencias Médicas A Verified email at inmegen.edu.mx Sampling and inference for discrete random probability measures in probabilistic programs. N Ueda, K Saito. Zoubin Ghahramani Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, U.K. zoubin@eng.cam.ac.uk. Received: September 14 2011 . In Association for the Advancement of Artificial Intelligence (AAAI), July 2014. Zoubin Ghahramani ZG201@CAM.AC.UK University of Cambridge Abstract Deep learning tools have gained tremendous at-tention in applied machine learning. However, a major obstacle facing deep RL in the real world is their high sample complexity. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Workshop Notes of the ICRA Workshop on Concurrent Mapping and Localization for Autonomous Mobile Robots (W4), Washington, DC, May 11-15. In ICML. Verified email at eng.cam.ac.uk - Homepage.