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Tue Dec 14 03:00 AM -- 11:00 AM (PST)
Bayesian Deep Learning
Yarin Gal · Yingzhen Li · Sebastian Farquhar · Christos Louizos · Eric Nalisnick · Andrew Gordon Wilson · Zoubin Ghahramani · Kevin Murphy · Max Welling

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To deploy deep learning in the wild responsibly, we must know when models are making unsubstantiated guesses. The field of Bayesian Deep Learning (BDL) has been a focal point in the ML community for the development of such tools. Big strides have been made in BDL in recent years, with the field making an impact outside of the ML community, in fields including astronomy, medical imaging, physical sciences, and many others. But the field of BDL itself is facing an evaluation crisis: most BDL papers evaluate uncertainty estimation quality of new methods on MNIST and CIFAR alone, ignoring needs of real world applications which use BDL. Therefore, apart from discussing latest advances in BDL methodologies, a particular focus of this year’s programme is on the reliability of BDL techniques in downstream tasks. This focus is reflected through invited talks from practitioners in other fields and by working together with the two NeurIPS challenges in BDL — the Approximate Inference in Bayesian Deep Learning Challenge and the Shifts Challenge on Robustness and Uncertainty under Real-World Distributional Shift — advertising work done in applications including autonomous driving, medical, space, and more. We hope that the mainstream BDL community will adopt real world benchmarks based on such applications, pushing the field forward beyond MNIST and CIFAR evaluations.

Opening Remarks (Opening remarks (zoom))
Adaptive and Robust Learning with Bayes (Invited talk)
A Bayesian Perspective on Meta-Learning (Invited talk)
Shifts Challenge: Robustness and Uncertainty under Real-World Distributional Shift (Competition talk)
Gaussian Dropout as an Information Bottleneck Layer (Contributed talk)
Funnels: Exact Maximum Likelihood with Dimensionality Reduction (Contributed talk)
Posters (gather town link to the right) and lunch break (Poster)
Spacecraft Collision Avoidance with Bayesian Deep Learning (Invited talk)
Inference & Sampling with Symmetries (Invited talk)
Bayesian Neural Networks, Andversarial Attacks, and How the Amount of Samples Matters (Invited talk)
Posters (gather town) (Poster)
Quantified Uncertainty for Safe Operation of Particle Accelerators (Invited talk)
Diversity is All You Need to Improve Bayesian Model Averaging (Contributed talk)
Structure Stochastic Gradient MCMC: a hybrid VI and MCMC approach (Contributed talk)
Evaluating Approximate Inference in Bayesian Deep Learning (Competition talk)
An Automatic Finite-Data Robustness Metric for Bayes and Beyond: Can Dropping a Little Data Change Conclusions? (Invited talk)
Closing remarks
Social and Posters (gather town) (Poster)
Reliable Uncertainty Quantification of Deep Learning Models for a Free Electron Laser Scientific Facility (Poster)
Latent Goal Allocation for Multi-Agent Goal-Conditioned Self-Supervised Learning (Poster)
Constraining cosmological parameters from N-body simulations with Bayesian Neural Networks (Poster)
Evaluating Deep Learning Uncertainty Quantification Methods for Neutrino Physics Applications (Poster)
Model-embedding flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling (Poster)
Likelihood-free Density Ratio Acquisition Functions are not Equivalent to Expected Improvements (Poster)
Object-Factored Models with Partially Observable State (Poster)
On Efficient Uncertainty Estimation for Resource-Constrained Mobile Applications (Poster)
Dropout and Ensemble Networks for Thermospheric Density Uncertainty Estimation (Poster)
Benchmark for Out-of-Distribution Detection in Deep Reinforcement Learning (Poster)
Can Network Flatness Explain the Training Speed-Generalisation Connection? (Poster)
Mixture-of-experts VAEs can disregard unimodal variation in surjective multimodal data (Poster)
Depth Uncertainty Networks for Active Learning (Poster)
The Peril of Popular Deep Learning Uncertainty Estimation Methods (Poster)
Dependence between Bayesian neural network units (Poster)
Relaxed-Responsibility Hierarchical Discrete VAEs (Poster)
Precision Agriculture Based on Bayesian Neural Network (Poster)
Decomposing Representations for Deterministic Uncertainty Estimation (Poster)
Gaussian dropout as an information bottleneck layer (Poster)
Funnels: Exact maximum likelihood with dimensionality reduction (Poster)
Progress in Self-Certified Neural Networks (Poster)
Multimodal Relational VAE (Poster)
Laplace Approximation with Diagonalized Hessian for Over-parameterized Neural Networks (Poster)
Exploring the Limits of Epistemic Uncertainty Quantification in Low-Shot Settings (Poster)
Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning (Poster)
Kronecker-Factored Optimal Curvature (Poster)
Contrastive Generative Adversarial Network for Anomaly Detection (Poster)
Certifiably Robust Variational Autoencoders (Poster)
On Symmetries in Variational Bayesian Neural Nets (Poster)
Greedy Bayesian Posterior Approximation with Deep Ensembles (Poster)
On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty (Poster)
An Empirical Study of Neural Kernel Bandits (Poster)
Structured Stochastic Gradient MCMC: a hybrid VI and MCMC approach (Poster)
Contrastive Representation Learning with Trainable Augmentation Channel (Poster)
Power-law asymptotics of the generalization error for GP regression under power-law priors and targets (Poster)
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning (Poster)
Generation of data on discontinuous manifolds via continuous stochastic non-invertible networks (Poster)
Uncertainty Quantification in End-to-End Implicit Neural Representations for Medical Imaging (Poster)
Evaluating Predictive Uncertainty and Robustness to Distributional Shift Using Real World Data (Poster)
Generalization Gap in Amortized Inference (Poster)
Information-theoretic stochastic contrastive conditional GAN: InfoSCC-GAN (Poster)
Deep Classifiers with Label Noise Modeling and Distance Awareness (Poster)
Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks (Poster)
Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness (Poster)
Being a Bit Frequentist Improves Bayesian Neural Networks (Poster)
Reproducible, incremental representation learning with Rosetta VAE (Poster)
An Empirical Comparison of GANs and Normalizing Flows for Density Estimation (Poster)
Resilience of Bayesian Layer-Wise Explanations under Adversarial Attacks (Poster)
Non-stationary Gaussian process discriminant analysis with variable selection for high-dimensional functional data (Poster)
Pathologies in Priors and Inference for Bayesian Transformers (Poster)
Analytically Tractable Inference in Neural Networks - An Alternative to Backpropagation (Poster)
Infinite-channel deep convolutional Stable neural networks (Poster)
Unveiling Mode-connectivity of the ELBO Landscape (Poster)
Deep Bayesian Learning for Car Hacking Detection (Poster)
Diversity is All You Need to Improve Bayesian Model Averaging (Poster)
Regularizations Are All You Need: Weather Prediction Under Distributional Shift (Poster)
Reducing redundancy in Semantic-KITTI: Study on data augmentations within Active Learning (Poster)
An Empirical Analysis of Uncertainty Estimation in Genomics Applications (Poster)
Hierarchical Topic Evaluation: Statistical vs. Neural Models (Poster)
Reflected Hamiltonian Monte Carlo (Poster)
Federated Functional Variational Inference (Poster)
Towards Robust Object Detection: Bayesian RetinaNet for Homoscedastic Aleatoric Uncertainty Modeling (Poster)
Stochastic Pruning: Fine-Tuning, and PAC-Bayes bound optimization (Poster)
Adversarial Learning of a Variational Generative Model with Succinct Bottleneck Representation (Poster)
Posterior Temperature Optimization in Variational Inference for Inverse Problems (Poster)
Revisiting the Structured Variational Autoencoder (Poster)
Robust outlier detection by de-biasing VAE likelihoods (Poster)
The Dynamics of Functional Diversity throughout Neural Network Training (Poster)
Biases in variational Bayesian neural networks (Poster)
Bayesian Inference in Augmented Bow Tie Networks (Poster)
Fast Finite Width Neural Tangent Kernel (Poster)