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Based in the Department of Statistics at the University of Oxford, our research spans the whole range of modern statistics and machine learning with particular strengths in probabilistic modelling, nonparametric methods, Monte Carlo, variational inference, deep learning, causality, theoretical statistics, learning theory, and applications in genetics, genomics and medicine. Groups: Computational Statistics Machine Learning Statistical Methodology Statistical Theory Information for prospective students Links: Seminars Events Stats Dept Latest News OxCSML at NeurIPS 2021 Oct 6, 2021 The group is participating in NeurIPS 2021. Please feel free to stop by any of our poster sessions or presentations! We have 25 papers accepted to the main program of the conference: Online Variational Filtering and Parameter Learning by Andrew Campbell, Yuyang Shi, Tom Rainforth, Arnaud Doucet Poster session: Wed 8 Dec 12:30 a.m. PST — 2 a.m. PST Oral presentation in Generative Modeling: Tue 7 Dec midnight PST — 1 a.m. PST Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms by Alexander Camuto, George Deligiannidis, Murat A Erdogdu, Mert Gurbuzbalaban, Umut Simsekli, Lingjiong Zhu Spotlight presentation: Tue 7 Dec 8:30 a.m. PST — 10 a.m. PST Deconditional Downscaling with Gaussian processes by Siu Lun Chau, Shahine Bouabid, Dino Sejdinovic Poster session: Tue 7 Dec 8:30 a.m. PST — 10 a.m. PST BayesIMP: Uncertainty Quantification for Causal Data Fusion by Siu Lun Chau, Jean-François Ton, Javier González, Yee Whye Teh, Dino Sejdinovic Poster session: Tue 7 Dec 8:30 a.m. PST — 10 a.m. PST Provably Strict Generalisation Benefit for Invariance in Kernel Methods by Bryn Elesedy Poster session: Tue 7 Dec 8:30 a.m. PST — 10 a.m. PST Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning by Jannik Kossen, Neil Band, Clare Lyle, Aidan N. Gomez, Tom Rainforth, Yarin Gal Poster session: Tue 7 Dec 8:30 a.m. PST — 10 a.m. PST Neural Ensemble Search for Uncertainty Estimation and Dataset Shift by Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris Holmes, Frank Hutter, Yee Whye Teh Poster session: Tue 7 Dec 8:30 a.m. PST — 10 a.m. PST Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels by Michael Hutchinson, Alexander Terenin, Viacheslav Borovitskiy, So Takao, Yee Whye Teh, Marc Deisenroth Poster session: Tue 7 Dec 8:30 a.m. PST — 10 a.m. PST On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations by Tim G. J. Rudner, Cong Lu, Michael A. Osborne, Yarin Gal, Yee Whye Teh Poster session: Tue 7 Dec 4:30 p.m. PST — 6 p.m. PST Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods by Desi R. Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Tom Rainforth Poster session: Wed 8 Dec 12:30 a.m. PST — 2 a.m. PST On Optimal Interpolation in Linear Regression by Eduard Oravkin, Patrick Rebeschini Poster session: Wed 8 Dec 12:30 a.m. PST — 2 a.m. PST Stability & Generalisation of Gradient Descent for Shallow Neural Networks without the Neural Tangent Kernel by Dominic Richards, Ilja Kuzborskij Poster session: Wed 8 Dec 4:30 p.m. PST — 6 p.m. PST NEO: Non Equilibrium Sampling on the Orbit of a Deterministic Transform by Achille Thin, Yazid Janati El Idrissi, Sylvain Le Corff, Charles Ollion, Eric Moulines, Arnaud Doucet, Alain Durmus, Christian P Robert Poster session: Thu 9 Dec 12:30 a.m. PST — 2 a.m. PST Time-independent Generalization Bounds for SGLD in Non-convex Settings by Tyler Farghly, Patrick Rebeschini Poster session: Thu 9 Dec 12:30 a.m. PST — 2 a.m. PST Powerpropagation: A sparsity inducing weight reparameterisation by Jonathan Schwarz, Sid M Jayakumar, Razvan Pascanu, Peter E Latham, Yee Whye Teh Poster session: Thu 9 Dec 12:30 a.m. PST — 2 a.m. PST Outcome-Driven Reinforcement Learning via Variational Inference by Tim G. J. Rudner, Vitchyr H. Pong, Rowan McAllister, Yarin Gal, Sergey Levine Poster session: Thu 9 Dec 8:30 a.m. PST — 10 a.m. PST Conformal Bayesian Computation by Edwin Fong, Chris Holmes Poster session: Thu 9 Dec 8:30 a.m. PST — 10 a.m. PST Distributed Machine Learning with Sparse Heterogeneous Data by Dominic Richards, Sahand N. Negahban, Patrick Rebeschini Poster session: Thu 9 Dec 8:30 a.m. PST — 10 a.m. PST Group Equivariant Subsampling by Jin Xu, Hyunjik Kim, Tom Rainforth, Yee Whye Teh Poster session: Thu 9 Dec 8:30 a.m. PST — 10 a.m. PST Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling by Valentin De Bortoli, James Thornton, Jeremy Heng, Arnaud Doucet Spotlight presentation: Thu 9 Dec 4:30 p.m. PST — 6 p.m. PST Uniform Sampling over Episode Difficulty by Sébastien M. R. Arnold, Guneet S. Dhillon, Avinash Ravichandran, Stefano Soatto Spotlight presentation: Thu 9 Dec 4:30 p.m. PST — 6 p.m. PST On Locality of Local Explanation Models by Sahra Ghalebikesabi, Lucile Ter-Minassian, Karla Diaz-Ordaz, Chris Holmes Poster session: Fri 10 Dec 8:30 a.m. PST — 10 a.m. PST On Contrastive Representations of Stochastic Processes by Emile Mathieu, Adam Foster, Yee Whye Teh Poster session: Fri 10 Dec 8:30 a.m. PST — 10 a.m. PST Implicit Regularization in Matrix Sensing via Mirror Descent by Fan Wu, Patrick Rebeschini Poster session: Fri 10 Dec 8:30 a.m. PST — 10 a.m. PST Multi-Facet Clustering Variational Autoencoders by Fabian Falck, Haoting Zhang, Matthew Willetts, George Nicholson, Christopher Yau, Chris Holmes Poster session: Fri 10 Dec 8:30 a.m. PST — 10 a.m. PST We have a paper in the Datasets and Benchmarks Track: Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks by Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Michael W. Dusenberry, Ghassen Jerfel, Dustin Tran, Yarin Gal Poster at Datasets & Benchmarks Session Spotlight at Workshop on Distribution Shifts Poster at Symposium on Machine Learning for Health (ML4H) Poster at Workshop on Bayesian Deep Learning We also have three workshop papers: Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning by Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W. Dusenberry, Sebastian Farquhar, Qixuan Feng, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Zhe Liu, Zelda E Mariet, Jeremy Nixon, Shreyas Padhy, Jie Ren, Tim G. J. Rudner, Yeming Wen, Florian Wenzel, Kevin Patrick Murphy, D. Sculley, Balaji Lakshminarayanan, Jasper Snoek, Yarin Gal, Dustin Tran Poster at Workshop on Bayesian Deep Learning PCA Subspaces Are Not Always Optimal for Bayesian Learning by Alexandre Bense, Amir Joudaki, Tim G. J. Rudner, Vincent Fortuin Poster at Workshop on Distribution Shifts Uncertainty Quantification in End-to-End Implicit Neural Representations for Medical Imaging by Francisca Vasconcelos, Bobby He, Yee Whye Teh Oral presentation at MedNeuRIPs Workshop Poster at Bayesian Deep Learning Workshop And don’t miss Yee Whye Teh’s invited talk A Bayesian Perspective on Neural Processes at Bayesian Deep Learning Workshop on Dec 14! OxCSML at ICML 2021 Jul 18, 2021 The group is participating in ICML 2021. Please feel free to stop by any of our poster sessions or presentations! We have 14 papers accepted to the main program of the conference: Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design by Adam Foster, Desi R. Ivanova, Ilyas Malik and Tom Rainforth Differentiable Particle Filtering via Entropy-Regularized Optimal Transport by Adrien Corenflos*, James Thornton*, George Deligiannidis, Arnaud Doucet Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding by Yangjun Ruan, Karen Ullrich, Daniel Severo, James Townsend, Ashish Khisti, Arnaud Doucet, Alireza Makhzani and Chris J. Maddison Provably Strict Generalisation Benefit for Equivariant Models by Bryn Elesedy and Sheheryar Zaidi Active Testing: Sample-Efficient Model Evaluation by Jannik Kossen, Sebastian Farquhar, Yarin Gal and Tom Rainforth Probabilistic Programs with Stochastic Conditioning by David Tolpin, Yuan Zhou, Tom Rainforth and Hongseok Yang Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning by Luisa Zintgraf, Leo Feng, Cong Lu, Maximilian Igl, Kristian Hartikainen, Katja Hofmann and Shimon Whiteson Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment) by Philip J. Ball*, Cong Lu*, Jack Parker-Holder and Stephen Roberts Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces by Xingchen Wan, Vu Nguyen, Huong Ha, Binxin Ru, Cong Lu, Michael A. Osborne Monte Carlo Variational Auto-Encoders by Achille Thin, Nikita Kotelevskii, Alain Durmus, Maxim Panov, Eric Moulines , Arnaud Doucet LieTransformer: Equivariant Self-Attention for Lie Groups by Michael Hutchinson*, Charline Le Lan*, Sheheryar Zaidi*, Emilien Dupont, Yee Whye Teh, Hyunjik Kim Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes by Peter Holderrieth, Michael Hutchinson, Yee Whye Teh Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections by Alexander Camuto, Xiaoyu Wang, Lingjiong Zhu, Chris Holmes, Mert Gürbüzbalaban and Umut Şimşekli On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Process by Tim Rudner, Oscar Key, Yarin Gal and Tom Rainforth In addition, Yee Whye Teh received the Test of Time Award for his 2011 paper with Max Welling Bayesian Learning via Stochastic Gradient Langevin Dynamics See here for a quick run down of each paper, plus the presentations and poster sessions for each. 2 UAI 2020 Accepted Papers! Jul 8, 2020 2 papers co-authored by the OxCSML group members have been accepted to the main program of UAI 2020 People Faculty François Caron George Deligiannidis Arnaud Doucet Robin Evans Chris Holmes Geoff Nicholls Tom Rainforth Patrick Rebeschini Judith Rousseau Dino Sejdinovic Yee Whye Teh Affiliated Faculty Sarah Filippi Postdocs M. Azim Ansari Emile Mathieu George Nicholson Students Moustafa Abdalla Freddie Bickford Smith Shahine Bouabid Christian Carmona Perez Anthony Caterini Sam Davenport Emilien Dupont Fabian Falck Tyler Farghly Jake Fawkes Edwin Fong Adam Foster Adam Goliński Aidan N. Gomez Frauke Harms Bobby He Zhiyuan Hu Robert Hu Desi R. Ivanova Jannik Kossen Charline Le Lan Cong Lu Ning Miao Cian Naik Francesca Panero Emilia Pompe Tim Reichelt Tim G. J. Rudner Yuyang Shi Jean-Francois Ton Hanwen Xing Jin Xu Schyan Zafar Sheheryar Zaidi
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