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

Coffee Break: 11-11:15am

Session 2

Lunch: 1:45-2:15pm

Session 3

Coffee Break: 3:15-3:30pm

Session 4

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