Accepted Papers

Note: Papers listed here do not constitute workshop proceedings.

Accepted Papers

  1. Stanislav Fort, Jie Ren and Balaji Lakshminarayanan. Exploring the Limits of Out-of-Distribution Detection

  2. Beau Coker, Weiwei Pan and Finale Doshi-Velez. Wide Mean-Field Variational Bayesian Neural Networks Ignore the Data

  3. Francesco D'Angelo and Vincent Fortuin. Repulsive Deep Ensembles are Bayesian

  4. Stephen Bates, Emmanuel Candès, Lihua Lei, Yaniv Romano and Matteo Sesia. Calibrated Out-of-Distribution Detection with Conformal P-values

  5. Lorenzo Noci, Gregor Bachmann, Kevin Roth, Sebastian Nowozin and Thomas Hofmann. Precise characterization of the prior predictive distribution of deep ReLU networks

  6. Lorenzo Noci, Kevin Roth, Gregor Bachmann, Sebastian Nowozin and Thomas Hofmann. Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect

  7. Jie Ren, Stanislav Fort, Jeremiah Zhe Liu, Abhijit Guha Roy, Shreyas Padhy and Balaji Lakshminarayanan A simple fix to Mahalanobis distance for improving near-OOD detection

  8. Christian Henning, Francesco D'Angelo and Benjamin F. Grewe. Are Bayesian neural networks intrinsically good at out-of-distribution detection?

  9. Alexander Meinke, Julian Bitterwolf and Matthias Hein. Provably Robust Detection of Out-of-distribution Data (almost) for free

  10. Mohamad Hosein Danesh and Alan Fern. Out-of-Distribution Dynamics Detection: RL-Relevant Benchmarks and Results

  11. Lukas Ruff, Robert A Vandermeulen, Billy Joe Franks, Klaus-Robert Müller and Marius Kloft. Rethinking Assumptions in Deep Anomaly Detection

  12. Haonan Duan and Pascal Poupart. Multiple Moment Matching Inference: A Flexible Approximate Inference Algorithm

  13. Sangdon Park, Edgar Dobriban, Insup Lee and Osbert Bastani. PAC Prediction Sets Under Covariate Shift

  14. Michael Zhang, Nimit Sohoni, Hongyang Zhang, Chelsea Finn and Chris Ré. Correct-N-Contrast: a Contrastive Approach for Improving Robustness to Spurious Correlations

  15. Zhisheng Xiao, Qing Yan and Yali Amit. Do We Really Need to Learn Representations from In-domain Data for Outlier Detection?

  16. Andrei Manolache, Florin Brad and Elena Burceanu. DATE: Detecting Anomalies in Text via Self-Supervision of Transformers

  17. Youngseog Chung, Willie Neiswanger, Ian Char and Jeff Schneider. Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification

  18. Youngseog Chung, Ian Char, Han Guo, Jeff Schneider and Willie Neiswanger. Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification

  19. Macheng Shen and Jonathan How. Implicit Ensemble Training for Efficient and Robust Multiagent Reinforcement Learning

  20. Karina Zadorozhny, Dennis Ulmer and Giovanni Cina. Failures of Uncertainty Estimation on Out-Of-Distribution Samples: Experimental Results from Medical Applications Lead to Theoretical Insights

  21. Sven Elflein, Bertrand Charpentier, Daniel Zügner and Stephan Günnemann. On Out-of-distribution Detection with Energy-Based Models

  22. Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip Torr and Yarin Gal. Deterministic Neural Networks with Inductive Biases Capture Epistemic and Aleatoric Uncertainty

  23. Mark Collier, Rodolphe Jenatton, Efi Kokiopoulou and Jesse Berent. Transfer and Marginalize: Explaining Away Label Noise with Privileged Information

  24. Ondrej Bohdal, Yongxin Yang and Timothy Hospedales. Meta-Calibration: Meta-Learning of Model Calibration Using Differentiable Expected Calibration Error

  25. Ji Won Park, Ashley Villar, Yin Li, Yan-Fei Jiang, Shirley Ho, Joshua Yao-Yu Lin, Philip Marshall and Aaron Roodman. Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes

  26. Jasper Hoffmann, Shashank Agnihotri, Tonmoy Saikia and Thomas Brox. Towards improving robustness of compressed CNNs

  27. Soroosh Shahtalebi, Jean-Christophe Gagnon-Audet, Touraj Laleh, Mojtaba Faramarzi, Kartik Ahuja and Irina Rish. SAND-mask: An Enhanced Gradient Masking Strategy for Invariant Prediction in Domain Generalization

  28. Stratis Markou, James Requeima, Wessel Bruinsma and Richard Turner. Efficient Gaussian Neural Processes for Regression

  29. Zayd Hammoudeh and Daniel Lowd. Simple, Attack-Agnostic Defense Against Targeted Training Set Attacks Using Cosine Similarity

  30. Varun Tekur, Javin Pombra, Rose Hong and Weiwei Pan. Safety & Exploration: A Comparative Study of Uses of Uncertainty in Reinforcement Learning

  31. Tim G. J. Rudner, Zonghao Chen, Yee Whye Teh and Yarin Gal. Rethinking Function-Space Variational Inference in Bayesian Neural Networks

  32. Yu Bai, Song Mei, Huan Wang and Caiming Xiong. Understanding the Under-Coverage Bias in Uncertainty Estimation

  33. Kate Highnam, Kai Arulkumaran, Zach Hanif and Nicholas R. Jennings. BETH Dataset: Real Cybersecurity Data for Anomaly Detection Research

  34. Arsenii Ashukha, Andrei Atanov and Dmitry Vetrov. Mean Embeddings with Test-Time Data Augmentation for Ensembling of Representations

  35. Lisa Schut, Edward Hu, Greg Yang and Yarin Gal. Deep Ensemble Uncertainty Fails as Network Width Increases: Why, and How to Fix It

  36. Pranav Subramani, Antonio Vergari, Gautam Kamath and Robert Peharz. Exact and Efficient Adversarial Robustness with Decomposable Neural Networks

  37. Youngbum Hur, Jihoon Tack, Eunho Yang, Sung Ju Hwang and Jinwoo Shin. Consistency Regularization for Training Confidence-Calibrated Classifiers

  38. Dan Ley, Umang Bhatt and Adrian Weller. Diverse and Amortised Counterfactual Explanations for Uncertainty Estimates

  39. Mahesh Subedar, Ranganath Krishnan, Sidharth N Kashyap and Omesh Tickoo. Quantization of Bayesian neural networks and its effect on quality of uncertainty

  40. Mobarakol Islam, Lalithkumar Seenivasan, Hongliang Ren and Ben Glocker. Class-Distribution-Aware Calibration for Long-Tailed Visual Recognition

  41. Edward Yu. Bayesian Neural Networks with Soft Evidence

  42. Oleksandr Shchur, Ali Caner Turkmen, Tim Januschowski, Jan Gasthaus and Stephan Günnemann. Anomaly Detection for Event Data with Temporal Point Processes

  43. Vincent Mai, Waleed Khamies and Liam Paull. Batch Inverse-Variance Weighting: Deep Heteroscedastic Regression

  44. Yong Lin, Qing Lian and Tong Zhang. An Empirical Study of Invariant Risk Minimization on Deep Models

  45. Nikolaos Mourdoukoutas, Marco Federici, Georges Pantalos, Mark van der Wilk and Vincent Fortuin. A Bayesian Approach to Invariant Deep Neural Networks

  46. Christian S. Perone, Roberto Pereira Silveira and Thomas Paula. L2M: Practical posterior Laplace approximation with optimization-driven second moment estimation

  47. Jiaxin Zhang, Jan Drgona, Sayak Mukherjee, Mahantesh Halappanavar and Frank Liu. Variational Generative Flows for Reconstruction Uncertainty Estimation

  48. Chawin Sitawarin, Arvind Sridhar and David Wagner. Improving the Accuracy-Robustness Trade-Off for Dual-Domain Adversarial Training

  49. Erik Englesson and Hossein Azizpour. Consistency Regularization Can Improve Robustness to Label Noise

  50. Lauro Langosco, Vincent Fortuin and Heiko Strathmann. Neural Variational Gradient Descent

  51. Patrick Feeney and Michael Hughes. Evaluating the Use of Reconstruction Error for Novelty Localization

  52. John Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon and Ludwig Schmidt. Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization

  53. Janis Postels, Hermann Blum, Yannick Strümpler, Cesar Cadena, Roland Siegwart, Luc Van Gool and Federico Tombari. The Hidden Uncertainty in a Neural Network’s Activations

  54. Janis Postels, Mattia Segu, Tao Sun, Luc Van Gool, Fisher Yu and Federico Tombari. On the Calibration of Deterministic Epistemic Uncertainty

  55. Jack Koch, Lauro Langosco, Jacob Pfau, James Le and Lee Sharkey. Objective Robustness in Deep Reinforcement Learning

  56. Nate Gruver, Sanyam Kapoor, Miles Cranmer and Andrew Wilson. Epistemic Uncertainty in Learning Chaotic Dynamical Systems

  57. Hao Yang, Yongxin Yang, Da Li, Yun Zhou and Timothy Hospedales. Towards Stochastic Neural Networks via Inductive Wasserstein Embeddings

  58. Aleksandr Podkopaev and Aaditya Ramdas. Distribution-free uncertainty quantification for classification under label shift

  59. Jingling Li, Mozhi Zhang, Keyulu Xu, John Dickerson and Jimmy Ba. How does a Neural Network's Architecture Impact its Robustness to Noisy Labels?

  60. Chirag Gupta and Aaditya Ramdas. Top-label calibration

  61. Shangyuan Tong, Timur Garipov, Yang Zhang, Shiyu Chang and Tommi Jaakkola. Adversarial Support Alignment via Relaxed 1D Optimal Transport

  62. Charles Corbière, Marc Lafon, Nicolas Thome, Matthieu Cord and Patrick Pérez. Beyond First-Order Uncertainty Estimation with Evidential Models for Open-World Recognition

  63. Julian Bitterwolf, Alexander Meinke, Maximilian Augustin and Matthias Hein. Revisiting Out-of-Distribution Detection: A Simple Baseline is Surprisingly Effective

  64. Puck de Haan and Sindy Löwe. Contrastive Predictive Coding for Anomaly Detection and Segmentation

  65. Ashwin Raaghav Narayanan, Arbër Zela, Tonmoy Saikia, Thomas Brox and Frank Hutter. Multi-headed Neural Ensemble Search

  66. Sebastian Ober and Laurence Aitchison. A variational approximate posterior for the deep Wishart process

  67. Francesco D'Angelo, Vincent Fortuin and Florian Wenzel. On Stein Variational Neural Network Ensembles

  68. Yao Qin, Xuezhi Wang, Balaji Lakshminarayanan, Ed Chi and Alex Beutel. What Are Effective Labels for Augmented Data? Improving Calibration and Robustness with AutoLabel

  69. Utkarsh Sarawgi, Rishab Khincha, Wazeer Zulfikar, Satrajit Ghosh and Pattie Maes. Uncertainty-Aware Boosted Ensembling in Multi-Modal Settings

  70. Sahar Karimi, Beliz Gokkaya and Audrey Flower. RouBL: A computationally efficient way to go beyond mean-field variational inference

  71. Fahim Tajwar, Ananya Kumar, Sang Michael Xie and Percy Liang. No True State-of-the-Art? OOD Detection Methods are Inconsistent across Datasets

  72. Kaiqu Liang, Cem Anil, Yuhuai Wu and Roger Grosse. Out-of-Distribution Generalization with Deep Equilibrium Models

  73. Saurabh Garg, Yifan Wu, Alex Smola, Sivaraman Balakrishnan and Zachary Lipton. Mixture Proportion Estimation and PU Learning: A Modern Approach

  74. Aditya Singh, Alessandro Bay, Biswa Sengupta and Andrea Mirabile. On The Dark Side Of Calibration For Modern Neural Networks

  75. Hao He, Yuzhe Yang and Hao Wang. Domain Adaptation with Factorizable Joint Shift

  76. Gabriele Prato, Simon Guiroy, Ethan Caballero, Irina Rish and Sarath Chandar. Scaling Laws for the Out-of-Distribution Generalization of Image Classifiers

  77. Tycho van der Ouderaa and Mark van der Wilk. Learning Invariant Weights in Neural Networks

  78. Borja Gonzalez Leon, Murray Shanahan and Francesco Belardinelli. Relational Deep Reinforcement Learning and Latent Goals for Following Instructions in Temporal Logic

  79. John Holodnak and Allan Wollaber. On the Effectiveness of Mode Exploration in Bayesian Model Averaging for Neural Networks

  80. Lu Mi, Hao Wang, Yonglong Tian, Hao He and Nir Shavit. Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate

  81. Ramneet Kaur, Susmit Jha, Anirban Roy, Sangdon Park, Oleg Sokolsky and Insup Lee. Detecting OODs as datapoints with High Uncertainty

  82. Sina Mohseni, Arash Vahdat and Jay Yadawa. Multi-task Transformation Learning for Robust Out-of-Distribution Detection

  83. Jacob Kelly, Richard Zemel and Will Grathwohl. Directly Training Joint Energy-Based Models for Conditional Synthesis and Calibrated Prediction of Multi-Attribute Data

  84. Lipi Gupta, Aashwin Mishra and Auralee Edelen. Deep Learning with Quantified Uncertainty for Free Electron Laser Scientific Facilities

  85. Seijin Kobayashi, Johannes von Oswald and Benjamin F. Grewe. On the reversed bias-variance tradeoff in deep ensembles

  86. Kan Xu, Hamsa Bastani and Osbert Bastani. Robust Generalization of Quadratic Neural Networks via Function Identification

  87. Katelyn Morrison, Benjamin Gilby, Colton Lipchak, Adam Mattioli and Adriana Kovashka. Exploring Corruption Robustness: Inductive Biases in Vision Transformers and MLP-Mixers

  88. Martin Bauw, Santiago Velasco-Forero, Jesus Angulo, Claude Adnet and Olivier Airiau. Deep Random Projection Outlyingness for Unsupervised Anomaly Detection

  89. Jishnu Mukhoti, Joost van Amersfoort, Philip Torr and Yarin Gal. Deep Deterministic Uncertainty for Semantic Segmentation

  90. Sean Spinney, Amin Mansouri, Amin Memarian and Irina Rish. Identifying Invariant and Sparse Predictors in High-dimensional Data

  91. Athanasios Tsiligkaridis and Theodoros Tsiligkaridis. On Misclassification-Aware Smoothing for Robustness and Uncertainty Calibration

  92. Andreas Kirsch, Jishnu Mukhoti, Joost van Amersfoort, Philip H.S. Torr and Yarin Gal. On Pitfalls in OoD Detection: Entropy Considered Harmful

  93. Mrinal Rawat, Ramya Hebbalaguppe and Lovekesh Vig. PnPOOD : Out-Of-Distribution Detection for Text Classification via Plug andPlay Data Augmentation

  94. Tianjian Huang, Chinnadhurai Sankar, Pooyan Amini, Satwik Kottur, Alborz Geramifard, Meisam Razaviyayn and Ahmad Beirami. DAIR: Data Augmented Invariant Regularization

  95. Alexander Robey, Hamed Hassani and George J. Pappas. Model-Based Robust Deep Learning: Generalizing to Natural, Out-of-Distribution Data

  96. Gilberto Manunza, Matteo Pagliardini, Martin Jaggi and Tatjana Chavdarova. Improved Adversarial Robustness via Uncertainty Targeted Attacks

  97. Francesco Verdoja and Ville Kyrki. Notes on the Behavior of MC Dropout

  98. Stephen Bates, Anastasios Angelopoulos, Liha Lei, Jitendra Malik and Michael Jordan. Distribution-free Risk-controlling Prediction Sets

  99. Ethan Goan and Clinton Fookes. Stochastic Bouncy Particle Sampler for Bayesian Neural Networks

  100. Alexandru Tifrea, Eric Stavarache and Fanny Yang. Novelty detection using ensembles with regularized disagreement

  101. Daniel D'Souza, Zach Nussbaum, Chirag Agarwal and Sara Hooker. A Tale Of Two Long Tails

  102. Norman Mu and David Wagner. Defending against Adversarial Patches with Robust Self-Attention

  103. Francesco Farina, Lawrence Phillips and Nicola J. Richmond. Intrinsic uncertainties and where to find them

  104. Henry Kvinge, Colby Wight, Sarah Akers, Scott Howland, Woongjo Choi, Xiaolong Ma, Luke Gosink, Elizabeth Jurrus, Keerti Kappagantula and Tegan Emerson. Dataset to Dataspace: A Topological-Framework to Improve Analysis of Machine Learning Model Performance

  105. Lakshya Jain, Varun Chandrasekaran, Uyeong Jang, Sanjit Seshia and Somesh Jha. Analyzing And Improving Neural Networks By Generating Semantic Counterexamples Through Differentiable Rendering

  106. Lixuan Yang and Dario Rossi. Thinkback: Task-Specific Out-of-Distribution Detection

  107. David Stutz, Matthias Hein and Bernt Schiele. Relating Adversarially Robust Generalization to Flat Minima

  108. Taesup Kim, Rasool Fakoor, Jonas Mueller, Ryan Tibshirani and Alex Smola. Deep Quantile Aggregation