Schedule & Accepted Papers
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Confirmed Speakers
Schedule
The start and end times are 11am -- 7pm GMT / 12pm -- 8pm CET / 6am -- 2pm EST / 3am - 11am PST / 8pm -- 4am JST. Our friends in the Americas are welcome to join the latter sessions, and our friends in eastern time zones are welcome to join the earlier sessions.
The schedule interleaves invited speakers, contributed talks, and gather.town poster presentations to allow for networking and socialising.
11.00 - 11.10 (GMT) 12.00 - 12.10 (CET) |
Welcome and Opening Remarks | ||
11.10 - 11.30 (GMT) 12.10 - 12.30 (CET) |
Invited talk | Emtiyaz Khan, Dharmesh Tailor, Siddharth Swaroop | Adaptive and Robust Learning with Bayes |
11.30 - 11.50 (GMT) 12.30 - 12.50 (CET) |
Invited talk | Yee Whye Teh | A Bayesian Perspective on Meta-Learning |
11.50 - 12.10 (GMT) 12.50 - 13.10 (CET) |
Competition talk | Shifts Challenge: Robustness and Uncertainty under Real-World Distributional Shift | |
12.10 - 12.20 (GMT) 13.10 - 13.20 (CET) |
Contributed talk | Melanie Rey | Gaussian Dropout as an Information Bottleneck Layer |
12.20 - 12.30 (GMT) 13.20 - 13.30 (CET) |
Contributed talk | Samuel Klein | Funnels: Exact Maximum Likelihood with Dimensionality Reduction |
12.30 - 13.30 (GMT) 13.30 - 14.30 (CET) |
Lunch Break (+ Posters) | ||
13.30 - 13.50 (GMT) 14.30 - 14.50 (CET) |
Invited talk | Atılım Güneş Baydin, Francesco Pinto | Spacecraft Collision Avoidance with Bayesian Deep Learning |
13.50 - 14.10 (GMT) 14.30 - 15.10 (CET) |
Invited talk | Danilo Rezende, Peter Wirnsberger | Inference & Sampling with Symmetries |
14.10 - 14.30 (GMT) 15.10 - 15.30 (CET) |
Invited talk | Asja Fischer, Sina Däubener | Bayesian Neural Networks, Andversarial Attacks, and How the Amount of Samples Matters |
14.30 - 16.00 (GMT) 15.30 - 17.00 (CET) |
Poster Session | ||
16.00 - 16.20 (GMT) 17.00 - 17.20 (CET) |
Invited talk | Adi Hanuka, Owen Convery | Quantified Uncertainty for Safe Operation of Particle Accelerators |
16.20 - 16.30 (GMT) 17.20 - 17.30 (CET) |
Contributed talk | Yashvir Grewal | Diversity is All You Need to Improve Bayesian Model Averaging |
16.30 - 16.40 (GMT) 17.30 - 17.40 (CET) |
Contributed talk | Alex Boyd, Antonios Alexos | Structure Stochastic Gradient MCMC: a hybrid VI and MCMC approach |
16.40 - 17.00 (GMT) 17.40 - 18.00 (CET) |
Competition talk | Evaluating Approximate Inference in Bayesian Deep Learning | |
17.00 - 17.20 (GMT) 18.00 - 18.20 (CET) |
Invited talk | Tamara Broderick, Ryan Giordano | An Automatic Finite-Data Robustness Metric for Bayes and Beyond: Can Dropping a Little Data Change Conclusions? |
17.20 - 17.25 (GMT) 18.20 - 18.25 (CET) |
Closing Remarks | ||
17.25 - 19.00 (GMT) 18.25 - 20.00 (CET) |
Social + Posters |
Accepted Abstracts
We added all camera ready submissions sent to us by 4/12/2021. If a paper is not online, please contact the lead author and encourage them to send us the camera ready.
Authors | Title |
Edith Zhang, David Blei | Unveiling Mode-connectivity of the ELBO Landscape paper |
Daniele Bracale, Stefano Favaro, Sandra Fortini, Stefano Peluchetti | Infinite-channel deep convolutional Stable neural networks paper |
Luong-Ha Nguyen, James-A. Goulet | Analytically Tractable Inference in Neural Networks - An Alternative to Backpropagation paper |
Tristan Cinquin, Alexander Immer, Max Horn, Vincent Fortuin | Pathologies in Priors and Inference for Bayesian Transformers paper |
Weichang Yu, Sara Wade, Howard Bondell, Lamiae Azizi | Non-stationary Gaussian process discriminant analysis with variable selection for high-dimensional functional data paper |
Ginevra Carbone, Luca Bortolussi, Guido Sanguinetti | Resilience of Bayesian Layer-Wise Explanations under Adversarial Attacks paper |
Tianci Liu, Jeffrey Regier | An Empirical Comparison of GANs and Normalizing Flows for Density Estimation paper |
Miles Martinez, John Pearson | Reproducible, incremental representation learning with Rosetta VAE paper |
Agustinus Kristiadi, Matthias Hein, Philipp Hennig | Being a Bit Frequentist Improves Bayesian Neural Networks paper |
Konstantinos P. Panousis, Sotirios Chatzis, Sergios Theodoridis | Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness paper |
Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Michael W Dusenberry, Ghassen Jerfel, Dustin Tran, Yarin Gal | Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks paper |
Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Zhe Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou | Deep Classifiers with Label Noise Modeling and Distance Awareness paper |
Vitaliy Kinakh, Mariia Drozdova, Guillaume Quétant, Tobias Golling, Slava Voloshynovskiy | Information-theoretic stochastic contrastive conditional GAN: InfoSCC-GAN paper |
Mingtian Zhang, Peter Noel Hayes, David Barber | Generalization Gap in Amortized Inference paper |
Kumud Lakara, Akshat Bhandari, Pratinav Seth, Ujjwal Verma | Evaluating Predictive Uncertainty and Robustness to Distributional Shift Using Real World Data paper |
Francisca Vasconcelos, Bobby He, Yee Whye Teh | Uncertainty Quantification in End-to-End Implicit Neural Representations for Medical Imaging paper |
Mariia Drozdova, Vitaliy Kinakh, Guillaume Quetant, Tobias Golling, Slava Voloshynovskiy | Generation of data on discontinuous manifolds via continuous stochastic non-invertible networks paper |
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 | Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning paper |
Laha Ale, Scott King, Ning Zhang | Deep Bayesian Learning for Car Hacking Detection paper |
Hui Jin, Pradeep Kr. Banerjee, Guido Montufar | Power-law asymptotics of the generalization error for GP regression under power-law priors and targets paper |
Masanori Koyama, Kentaro Minami, Takeru Miyato, Yarin Gal | Contrastive Representation Learning with Trainable Augmentation Channel paper |
Antonios Alexos, Alex James Boyd, Stephan Mandt | Structured Stochastic Gradient MCMC: a hybrid VI and MCMC approach paper |
Michal Lisicki, Arash Afkanpour, Graham W. Taylor | An Empirical Study of Neural Kernel Bandits paper |
Joost van Amersfoort, Lewis Smith, Andrew Jesson, Oscar Key, Yarin Gal | On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty paper |
Aleksei Tiulpin, Matthew B. Blaschko | Greedy Bayesian Posterior Approximation with Deep Ensembles paper |
Richard Kurle, Tim Januschowski, Jan Gasthaus, Bernie Wang | On Symmetries in Variational Bayesian Neural Nets paper |
Ben Barrett, Alexander Camuto, Matthew Willetts, Tom Rainforth | Certifiably Robust Variational Autoencoders paper |
Laya Rafiee, Thomas Fevens | Contrastive Generative Adversarial Network for Anomaly Detection paper |
Dominik Schnaus, Jongseok Lee, Rudolph Triebel | Kronecker-Factored Optimal Curvature paper |
Runa Eschenhagen, Erik Daxberger, Philipp Hennig, Agustinus Kristiadi | Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning paper |
Matias Valdenegro-Toro | Exploring the Limits of Epistemic Uncertainty Quantification in Low-Shot Settings paper |
Ming Gui, Ziqing Zhao, Tianming Qiu, Hao Shen | Laplace Approximation with Diagonalized Hessian for Over-parameterized Neural Networks paper |
Thomas M. Sutter, Julia E Vogt | Multimodal Relational VAE paper |
Maria Perez-Ortiz, Omar Rivasplata, Emilio Parrado-Hernández, Benjamin Guedj, John Shawe-Taylor | Progress in Self-Certified Neural Networks paper |
Samuel Klein, John Andrew Raine, Tobias Golling, Slava Voloshynovskiy, Sebastion Pina-Otey | Funnels: Exact maximum likelihood with dimensionality reduction paper |
Melanie Rey, Andriy Mnih | Gaussian dropout as an information bottleneck layer paper |
Haiwen Huang, Joost van Amersfoort, Yarin Gal | Decomposing Representations for Deterministic Uncertainty Estimation paper |
Lei Zhao | Precision Agriculture Based on Bayesian Neural Network paper |
Matthew Willetts, Xenia Miscouridou, Stephen J. Roberts, Christopher C. Holmes | Relaxed-Responsibility Hierarchical Discrete VAEs paper |
Mariia Vladimirova, Julyan Arbel, Stephane Girard | Dependence between Bayesian neural network units paper |
Yehao Liu, Matteo Pagliardini, Tatjana Chavdarova, Sebastian U Stich | The Peril of Popular Deep Learning Uncertainty Estimation Methods paper |
Chelsea Murray, James Urquhart Allingham, Javier Antoran, José Miguel Hernández-Lobato | Depth Uncertainty Networks for Active Learning paper |
Jannik Wolff, Tassilo Klein, Moin Nabi, Rahul G Krishnan, Shinichi Nakajima | Mixture-of-experts VAEs can disregard unimodal variation in surjective multimodal data paper |
Albert Qiaochu Jiang, Clare Lyle, Lisa Schut, Yarin Gal | Can Network Flatness Explain the Training Speed-Generalisation Connection? paper |
Aaqib Parvez Mohammed, Matias Valdenegro-Toro | Benchmark for Out-of-Distribution Detection in Deep Reinforcement Learning paper |
Stefano Bonasera, Giacomo Acciarini, Jorge Pérez-Hernández, Bernard Benson, Edward Brown, Eric Sutton, Moriba Jah, Christopher Bridges, Atilim Gunes Baydin | Dropout and Ensemble Networks for Thermospheric Density Uncertainty Estimation paper |
Johanna Rock, Tiago Azevedo, René de Jong, Daniel Ruiz-Muñoz, Partha Maji | On Efficient Uncertainty Estimation for Resource-Constrained Mobile Applications paper |
Isaiah Brand, Michael Noseworthy, Sebastian Castro, Nicholas Roy | Object-Factored Models with Partially Observable State paper |
Jiaming Song, Stefano Ermon | Likelihood-free Density Ratio Acquisition Functions are not Equivalent to Expected Improvements paper |
Gianluigi Silvestri, Emily Fertig, Dave Moore, Luca Ambrogioni | Model-embedding flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling paper |
Dae Heun Koh, Aashwin Mishra, Kazuhiro Terao | Evaluating Deep Learning Uncertainty Quantification Methods for Neutrino Physics Applications paper |
Hector Javier Hortua | Constraining cosmological parameters from N-body simulations with Bayesian Neural Networks paper |
Laixi Shi, Peide Huang, Rui Chen | Latent Goal Allocation for Multi-Agent Goal-Conditioned Self-Supervised Learning paper |
Lipi Gupta, Aashwin Ananda Mishra, Auralee Edelen | Reliable Uncertainty Quantification of Deep Learning Models for a Free Electron Laser Scientific Facility paper |
Roman Novak, Jascha Sohl-Dickstein, Samuel Stern Schoenholz | Fast Finite Width Neural Tangent Kernel paper |
Jimmy T.H. Smith, Dieterich Lawson, Scott Linderman | Bayesian Inference in Augmented Bow Tie Networks paper |
Thang D Bui | Biases in variational Bayesian neural networks paper |
Lee Zamparo, Marc-Etienne Brunet, Thomas George, Sepideh Kharaghani, Gintare Karolina Dziugaite | The Dynamics of Functional Diversity throughout Neural Network Training paper |
Kushal Chauhan, Pradeep Shenoy, Manish Gupta, Devarajan Sridharan | Robust outlier detection by de-biasing VAE likelihoods paper |
Yixiu Zhao, Scott Linderman | Revisiting the Structured Variational Autoencoder paper |
Max-Heinrich Laves, Malte Tölle, Alexander Schlaefer, Sandy Engelhardt | Posterior Temperature Optimization in Variational Inference for Inverse Problems paper |
Jongha Jon Ryu, Yoojin Choi, Young-Han Kim, Mostafa El-Khamy, Jungwon Lee | Adversarial Learning of a Variational Generative Model with Succinct Bottleneck Representation paper |
Soufiane Hayou, Bobby He, Gintare Karolina Dziugaite | Stochastic Pruning: Fine-Tuning, and PAC-Bayes bound optimization paper |
Natalia Evgenievna Khanzhina, Alexey Lapenok, Andrey Filchenkov | Towards Robust Object Detection: Bayesian RetinaNet for Homoscedastic Aleatoric Uncertainty Modeling paper |
Michael John Hutchinson, Matthias Reisser, Christos Louizos | Federated Functional Variational Inference paper |
Au Khai Xiang, Alexandre H. Thiery | Reflected Hamiltonian Monte Carlo paper |
Mayank Kumar Nagda, Charu James, Sophie Burkhardt, Marius Kloft | Hierarchical Topic Evaluation: Statistical vs. Neural Models paper |
Sepideh Saran, Mahsa Ghanbari, Uwe Ohler | An Empirical Analysis of Uncertainty Estimation in Genomics Applications paper |
Alexandre Almin, Anh Ngoc Phuong Duong, Léo Lemarié, Ravi Kiran | Reducing redundancy in Semantic-KITTI: Study on data augmentations within Active Learning paper |
Sankalp Gilda, Neel Bhandari, Wendy Mak, Andrea Panizza | Robust Calibration For Improved Weather Prediction Under Distributional Shift paper |
Yashvir Singh Grewal, Thang D Bui | Diversity is All You Need to Improve Bayesian Model Averaging paper |
Arnaud Delaunoy, Gilles Louppe | SAE: Sequential Anchored Ensembles paper |