OxCSML at NeurIPS 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!