Schedule

July 23 (Friday), 2021

Timezone: US/Eastern Time

Tentative schedule in EST (subject to change):

Note: All the invited and contributed talks will be pre-recorded. Poster sessions (gather town) and panel discussion will be live.

Session 1

9:00am - 9:15am Opening remarks (Balaji Lakshminarayanan)
9:15am - 9:45am Invited talk #1 Dustin Tran Uncertainty Modeling from 50M to 1B
9:45am - 11am Live Poster Session #1 [Room 1 (Uncertainty), Room 2 (Robustness)]

Coffee Break: 11-11:15am

Session 2

11:15am - 11:45am Invited talk #2 Alec Radford Some Thoughts on Generalization, Robustness, and their application with CLIP
11:45am - 1pm Live Poster Session #2 [Room 3 (Uncertainty), Room 4 (Robustness)]
1:00pm - 1:45pm Live Panel discussion with Chelsea Finn, Kamalika Chaudhuri, Uri Shalit & Yarin Gal (Moderator: Tom Dietterich)

Lunch: 1:45-2:15pm

Session 3

2:15pm - 2:25pm Contributed talk #1 Repulsive Deep Ensembles are Bayesian
2:25pm - 2:35pm Contributed talk #2 Wide Mean-Field Variational Bayesian Neural Networks Ignore the Data
2:35pm - 2:45pm Contributed talk #3 Are Bayesian neural networks intrinsically good at out-of-distribution detection?
2:45pm - 3:15pm Invited talk #3 Shiori Sagawa Improving Robustness to Distribution Shifts: Methods and Benchmarks

Coffee Break: 3:15-3:30pm

Session 4

3:30pm - 4:00pm Invited talk #4 Nazneen Rajani Evaluating deep learning models with applications to NLP
4:00pm - 4:10pm Contributed talk #4 Calibrated Out-of-Distribution Detection with Conformal P-values
4:10pm - 4:20pm Contributed talk #5 Provably Robust Detection of Out-of-distribution Data (almost) for free
4:20pm - 4:30pm Contributed talk #6 Out-of-Distribution Dynamics Detection: RL-Relevant Benchmarks and Results

4:30pm - 5pm Invited talk #5 Jinwoo Shin Contrastive Learning for Novelty Detection

Poster Session #1

Room 1 (Uncertainty)

Wide Mean-Field Variational Bayesian Neural Networks Ignore the Data
Repulsive Deep Ensembles are Bayesian
Precise characterization of the prior predictive distribution of deep ReLU networks
Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect
Are Bayesian neural networks intrinsically good at out-of-distribution detection?
Towards improving robustness of compressed CNNs
Efficient Gaussian Neural Processes for Regression
Rethinking Function-Space Variational Inference in Bayesian Neural Networks
Diverse and Amortised Counterfactual Explanations for Uncertainty Estimates
Class-Distribution-Aware Calibration for Long-Tailed Visual Recognition
Bayesian Neural Networks with Soft Evidence
A Bayesian Approach to Invariant Deep Neural Networks
Neural Variational Gradient Descent
The Hidden Uncertainty in a Neural Network’s Activations
Towards Stochastic Neural Networks via Inductive Wasserstein Embeddings
Distribution-free uncertainty quantification for classification under label shift
Learning to Align the Support of Distributions
Beyond First-Order Uncertainty Estimation with Evidential Models for Open-World Recognition
A variational approximate posterior for the deep Wishart process
On The Dark Side Of Calibration For Modern Neural Networks
Learning Invariant Weights in Neural Networks
On the Effectiveness of Mode Exploration in Bayesian Model Averaging for Neural Networks
Deep Deterministic Uncertainty for Semantic Segmentation
Stochastic Bouncy Particle Sampler for Bayesian Neural Networks
A Tale Of Two Long Tails
Intrinsic uncertainties and where to find them

Room 2 (Robustness)

Do We Really Need to Learn Representations from In-domain Data for Outlier Detection?
DATE: Detecting Anomalies in Text via Self-Supervision of Transformers
Implicit Ensemble Training for Efficient and Robust Multiagent Reinforcement Learning
Failures of Uncertainty Estimation on Out-Of-Distribution Samples: Experimental Results from Medical Applications Lead to Theoretical Insights
On Out-of-distribution Detection with Energy-Based Models
Transfer and Marginalize: Explaining Away Label Noise with Privileged Information
SAND-mask: An Enhanced Gradient Masking Strategy for Invariant Prediction in Domain Generalization
BETH Dataset: Real Cybersecurity Data for Anomaly Detection Research
Mean Embeddings with Test-Time Data Augmentation for Ensembling of Representations
Exact and Efficient Adversarial Robustness with Decomposable Neural Networks
Anomaly Detection for Event Data with Temporal Point Processes
An Empirical Study of Invariant Risk Minimization on Deep Models
Consistency Regularization Can Improve Robustness to Label Noise
Evaluating the Use of Reconstruction Error for Novelty Localization
Objective Robustness in Deep Reinforcement Learning
How does a Neural Network's Architecture Impact its Robustness to Noisy Labels?
Revisiting Out-of-Distribution Detection: A Simple Baseline is Surprisingly Effective
Contrastive Predictive Coding for Anomaly Detection and Segmentation
Multi-headed Neural Ensemble Search
Scaling Laws for the Out-of-Distribution Generalization of Image Classifiers
Relational Deep Reinforcement Learning and Latent Goals for Following Instructions in Temporal Logic
On the reversed bias-variance tradeoff in deep ensembles
Robust Generalization of Quadratic Neural Networks via Function Identification
Deep Random Projection Outlyingness for Unsupervised Anomaly Detection
DAIR: Data Augmented Invariant Regularization
Model-Based Robust Deep Learning: Generalizing to Natural, Out-of-Distribution Data
Novelty detection using ensembles with regularized disagreement
Thinkback: Task-Specific Out-of-Distribution Detection

Poster Session #2

Room 3 (Uncertainty)

Multiple Moment Matching Inference: A Flexible Approximate Inference Algorithm
PAC Prediction Sets Under Covariate Shift
Correct-N-Contrast: a Contrastive Approach for Improving Robustness to Spurious Correlations
Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification
Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification
Deterministic Neural Networks with Inductive Biases Capture Epistemic and Aleatoric Uncertainty
Meta-Calibration: Meta-Learning of Model Calibration Using Differentiable Expected Calibration Error
Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes
Safety & Exploration: A Comparative Study of Uses of Uncertainty in Reinforcement Learning
Understanding the Under-Coverage Bias in Uncertainty Estimation
Deep Ensemble Uncertainty Fails as Network Width Increases: Why, and How to Fix It
Quantization of Bayesian neural networks and its effect on quality of uncertainty
Batch Inverse-Variance Weighting: Deep Heteroscedastic Regression
Practical posterior Laplace approximation with optimization-driven second moment estimation
Variational Generative Flows for Reconstruction Uncertainty Estimation
Epistemic Uncertainty in Learning Chaotic Dynamical Systems
Top-label calibration
Uncertainty-Aware Boosted Ensembling in Multi-Modal Settings
RouBL: A computationally efficient way to go beyond mean-field variational inference
Domain Adaptation with Factorizable Joint Shift
Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate
Directly Training Joint Energy-Based Models for Conditional Synthesis and Calibrated Prediction of Multi-Attribute Data
Deep Learning with Quantified Uncertainty for Free Electron Laser Scientific Facilities
Identifying Invariant and Sparse Predictors in High-dimensional Data
On Misclassification-Aware Smoothing for Robustness and Uncertainty Calibration
On Pitfalls in OoD Detection: Entropy Considered Harmful
Notes on the Behavior of MC Dropout
Distribution-free Risk-controlling Prediction Sets

Room 4 (Robustness)

Exploring the Limits of Out-of-Distribution Detection
Calibrated Out-of-Distribution Detection with Conformal P-values
A simple fix to Mahalanobis distance for improving near-OOD detection
Provably Robust Detection of Out-of-distribution Data (almost) for free
Out-of-Distribution Dynamics Detection: RL-Relevant Benchmarks and Results
Rethinking Assumptions in Deep Anomaly Detection
Simple, General-Purpose Defense Against Targeted Training Set Attacks Using Gradient Alignment
Consistency Regularization for Training Confidence-Calibrated Classifiers
Improving the Accuracy-Robustness Trade-Off for Dual-Domain Adversarial Training
Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization
On the Calibration of Deterministic Epistemic Uncertainty
On Stein Variational Neural Network Ensembles
What Are Effective Labels for Augmented Data? Improving Calibration and Robustness with AutoLabel
No True State-of-the-Art? OOD Detection Methods are Inconsistent across Datasets
Out-of-Distribution Generalization with Deep Equilibrium Models
Mixture Proportion Estimation and PU Learning: A Modern Approach
Detecting OODs as datapoints with High Uncertainty
Multi-task Transformation Learning for Robust Out-of-Distribution Detection
Exploring Corruption Robustness: Inductive Biases in Vision Transformers and MLP-Mixers
PnPOOD : Out-Of-Distribution Detection for Text Classification via Plug andPlay Data Augmentation
Improved Adversarial Robustness via Uncertainty Targeted Attacks
Defending against Adversarial Patches with Robust Self-Attention
Dataset to Dataspace: A Topological-Framework to Improve Analysis of Machine Learning Model Performance
Analyzing And Improving Neural Networks By Generating Semantic Counterexamples Through Differentiable Rendering
Relating Adversarially Robust Generalization to Flat Minima
Deep Quantile Aggregation

Google Sites

Report abuse