Accepted Papers

Note: Papers listed here do not constitute workshop proceedings.

Accepted Papers

1. Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks. David Stutz, Matthias Hein and Bernt Schiele. (Oral)

2. Improving robustness against common corruptions by covariate shift adaptation. Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel and Matthias Bethge. (Oral)

3. A Unified View of Label Shift Estimation. Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan and Zachary Lipton. (Oral)

4. A Benchmark of Medical Out of Distribution Detection. Tianshi Cao, David Yu-Tung Hui, Chin-Wei Huang and Joseph Paul Cohen. (Oral)

5. Neural Ensemble Search for Performant and Calibrated Predictions. Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris Holmes, Frank Hutter and Yee Whye Teh. (Oral)

6. Bayesian model averaging is suboptimal for generalization under model misspecification. Andres Masegosa. (Oral)

7. Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder. Zhisheng Xiao, Qing Yan and Yali Amit. (Spotlight)

8. A Closer Look at Accuracy vs. Robustness. Yao-Yuan Yang, Cyrus Rashtchian, Hongyang Zhang, Ruslan Salakhutdinov and Kamalika Chaudhuri. (Spotlight)

9. Depth Uncertainty in Neural Networks. Javier Antorán, James Urquhart Allingham and José Miguel Hernández-Lobato. (Spotlight)

10. Few-shot Out-of-Distribution Detection. Kuan-Chieh Wang, Paul Vicol, Eleni Triantafillou and Richard Zemel. (Spotlight)

11. Detecting Failure Modes in Image Reconstructions with Interval Neural Network Uncertainty. Luis Oala, Cosmas Heiß, Jan Macdonald, Maximilian März, Gitta Kutyniok and Wojciech Samek. (Spotlight)

12. On using Focal Loss for Neural Network Calibration. Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip Torr and Puneet Dokania. (Spotlight)

13. AutoAttack: reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. Francesco Croce and Matthias Hein. (Spotlight)

14. Calibrated Top-1 Uncertainty estimates for classification by score based models. Adam Oberman, Chris Finlay, Alexander Iannantuono and Tiago Salvador. (Spotlight)

15. Bayesian Deep Ensembles via the Neural Tangent Kernel. Bobby He, Balaji Lakshminarayanan and Yee Whye Teh.

16. The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization. Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt and Justin Gilmer.

17. Measuring Robustness to Natural Distribution Shifts in Image Classification. Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht and Ludwig Schmidt.

18. Redundant features can hurt robustness to distribution shift. Guillermo Ortiz-Jimenez, Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli and Pascal Frossard.

19. Scalable Training with Information Bottleneck Objectives. Andreas Kirsch, Clare Lyle and Yarin Gal.

20. How Does Early Stopping Help Generalization Against Label Noise? Hwanjun Song, Minseok Kim, Dongmin Park and Jae-Gil Lee.

21. Soft Labeling Affects Out-of-Distribution Detection of Deep Neural Networks. Doyup Lee and Yeongjae Cheon.

22. Learning Robust Representations with Score Invariant Learning. Daksh Idnani and Jonathan Kao.

23. PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction. Sangdon Park, Osbert Bastani, Nikolai Matni and Insup Lee.

24. Learned Uncertainty-Aware (LUNA) Bases for Bayesian Regression using Multi-Headed Auxiliary Networks. Sujay Thakur, Cooper Lorsung, Yaniv Yacoby, Finale Doshi-Velez and Weiwei Pan.

25. Domain Generalization using Causal Matching. Divyat Mahajan, Shruti Tople and Amit Sharma.

26. Hydra: Preserving Ensemble Diversity for Model Distillation. Linh Tran, Bastiaan S. Veeling, Kevin Roth, Jakub Swiatkowski, Joshua V. Dillon, Stephan Mandt, Jasper Snoek, Tim Salimans, Sebastian Nowozin and Rodolphe Jenatton.

27. Predicting with High Correlation Features. Devansh Arpit, Caiming Xiong and Richard Socher.

28. Consistency Regularization for Certified Robustness of Smoothed Classifiers. Jongheon Jeong and Jinwoo Shin.

29. DIBS: Diversity inducing Information Bottleneck in Model Ensembles. Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal, Hugo Larochelle, Animesh Garg and Florian Shkurti.

30. On the relationship between class selectivity, dimensionality, and robustness. Matthew Leavitt and Ari Morcos.

31. Robust Variational Autoencoder for Tabular Data with β Divergence. Haleh Akrami, Sergul Aydore, Richard Leahy and Anand Joshi.

32. Uncertainty in Structured Prediction. Andrey Malinin and Mark Gales.

33. Understanding and Improving Fast Adversarial Training. Maksym Andriushchenko and Nicolas Flammarion.

34. Predictive Complexity Priors. Eric Nalisnick, Jonathan Gordon and Jose Miguel Hernandez Lobato.

35. Provable Worst Case Guarantees for the Detection of Out-of-Distribution Data. Julian Bitterwolf, Alexander Meinke and Matthias Hein.

36. Ensemble Distribution Distillation via Regression Prior Networks. Andrey Malinin, Sergey Chervontsev, Ivan Provilkov and Mark Gales.

37. Generalizing to unseen domains via distribution matching. Isabela Albuquerque, Joao Monteiro, Mohammad Darvishi, Tiago Falk and Ioannis Mitliagkas.

38. GAN-mixup: Augmenting Across Data Manifolds for Improved Robustness. Jy-Yong Sohn, Kangwook Lee, Jaekyun Moon and Dimitris Papailiopoulos.

39. Improving out-of-distribution generalization via multi-task self-supervised pretraining. Isabela Albuquerque, Nikhil Naik, Junnan Li, Nitish Shirish Keskar and Richard Socher.

40. Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks. Shreyas Padhy, Zachary Nado, Jie Ren, Jeremiah Liu, Jasper Snoek and Balaji Lakshminarayanan.

41. Tilted Empirical Risk Minimization. Tian Li, Ahmad Beirami, Maziar Sanjabi and Virginia Smith.

42. Towards Robust Classification with Deep Generative Forests. Alvaro Henrique Chaim Correia, Robert Peharz and Cassio de Campos.

43. Riemannian Continuous Normalizing Flows. Emile Mathieu.

44. Improving Calibration of BatchEnsemble with Data Augmentation. Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael Dusenberry, Jasper Snoek, Balaji Lakshminarayanan and Dustin Tran.

45. Environment Inference for Invariant Learning. Elliot Creager, Jörn-Henrik Jacobsen and Richard Zemel.

46. Nonlinear Gradient Estimation for Query Efficient Blackbox Attack. Huichen Li, Linyi Li, Xiaojun Xu, Xiaolu Zhang, Shuang Yang and Bo Li.

47. ImageNet performance correlates with pose estimation robustness and generalization on out-of-domain data. Alexander Mathis, Thomas Biasi, Mert Yüksegönül, Byron Rogers, Matthias Bethge and Mackenzie Mathis.

48. CRUDE: Calibrating Regression Uncertainty Distributions Empirically. Eric Zelikman, Christopher Healy, Sharon Zhou and Anand Avati.

49. Positive-Unlabeled Learning with Arbitrarily Non-Representative Labeled Data. Zayd Hammoudeh and Daniel Lowd.

50. Probabilistic Robustness Estimates for Deep Neural Networks. Nicolas Couellan.

51. Estimating Risk and Uncertainty in Deep Reinforcement Learning. William Clements, Bastien Van Delft, Benoît-Marie Robaglia, Reda Bahi Slaoui and Sébastien Toth.

52. An Empirical Study of Invariant Risk Minimization. Yo Joong Choe, Jiyeon Ham and Kyubyong Park.

53. Information-Bottleneck under Mean Field Initialization. Vinayak Abrol and Jared Tanner.

54. Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift. Marvin Zhang, Henrik Marklund, Abhishek Gupta, Sergey Levine and Chelsea Finn.

55. Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift. Zachary Nado, Shreyas Padhy, D. Sculley, Alexander D’amour, Balaji Lakshminarayanan and Jasper Snoek.

56. Failures of Variational Autoencoders and their Effects on Downstream Tasks. Yaniv Yacoby, Weiwei Pan and Finale Doshi-Velez.

57. Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness. Jeremiah Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss and Balaji Lakshminarayanan.

58. Bayesian BERT for Trustful Hate Speech Detection. Kristian Miok, Blaz Skrlj, Daniela Zaharie and Marko Robnik-Sikonja.

59. On the Role of Dataset Quality and Heterogeneity in Model Confidence. Yuan Zhao, Jiasi Chen and Samet Oymak.

60. \ell_1 Adversarial Robustness Certificates: a Randomized Smoothing Approach. Jiaye Teng, Guang-He Lee and Yang Yuan.

61. QUEST for MEDISYN: Quasi-norm based Uncertainty ESTimation for MEDical Image SYNthesis. Uddeshya Upadhyay, Viswanath P. Sudarshan and Suyash P. Awate.

62. On uncertainty estimation in active learning for image segmentation. Bo Li and Tommy Sonne Alstrøm.

63. Bayesian Autoencoders: Analysing and Fixing the Bernoulli likelihood for Out-of-Distribution Detection. Bang Xiang Yong, Tim Pearce and Alexandra Brintrup.

64. A Comparison of Bayesian Deep Learning for Out of Distribution Detection and Uncertainty Estimation. John Mitros, Arjun Pakrashi and Brian Mac Namee.

65. Practical Bayesian Neural Networks via Adaptive Subgradient Optimization Methods. Samuel Kessler, Arnold Salas, Vincent Tan Weng Choon, Stefan Zohren and Stephen Roberts.

66. Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes. Jake Snell and Richard Zemel.

67. Characteristics of Monte Carlo Dropout in Wide Neural Networks. Joachim Sicking, Maram Akila, Tim Wirtz, Sebastian Houben and Asja Fischer.

68. On Power Laws in Deep Ensembles. Ekaterina Lobacheva, Nadezhda Chirkova, Maxim Kodryan and Dmitry Vetrov.

69. Outlier Detection through Null Space Analysis of Neural Networks. Matthew Cook, Alina Zare and Paul Gader.

70. In a forward direction: Analyzing distribution shifts in machine translation test sets over time. Thomas Liao, Benjamin Recht and Ludwig Schmidt.

71. Characterizing Adversarial Transferability via Gradient Orthogonality and Smoothness. Zhuolin Yang, Linyi Li, Xiaojun Xu, Kaizhao Liang, Shiliang Zuo, Qian Chen, Benjamin Rubinstein, Ce Zhang and Bo Li.

72. Untapped Potential of Data Augmentation: A Domain Generalization Viewpoint. Vihari Piratla and Shiv Shankar.

73. Classifying Perturbation Types for Adversarial Robustness Against Multiple Threat Models. Pratyush Maini, Xinyun Chen, Bo Li and Dawn Song.

74. A Critical Evaluation of Open-World Machine Learning. Liwei Song, Vikash Sehwag, Arjun Nitin Bhagoji and Prateek Mittal.

75. Learning CIFAR-10 with a Simple Entropy Estimator Using Information Bottleneck Objectives. Andreas Kirsch, Clare Lyle and Yarin Gal.

76. Principled Uncertainty Estimation for High Dimensional Data. Pascal Notin, José Miguel Hernández-Lobato and Yarin Gal.

77. Rethink Autoencoders: Robust Manifold Learning. Taihui Li, Rishabh Mehta, Zecheng Qian and Ju Sun.

78. Continuous-Depth Bayesian Neural Networks. Winnie Xu, Ricky T.Q. Chen, Xuechen Li and David Duvenaud.

79. Robust Temporal Point Event Localization through Smoothing and Counting. Julien Schroeter, Kirill Sidorov and David Marshall.

80. Chi-square Information for Invariant Learning. Prasanna Sattigeri, Soumya Ghosh and Samuel Hoffman.

81. Robust Out-of-distribution Detection via Informative Outlier Mining. Jiefeng Chen, Sharon Li, Xi Wu, Yingyu Liang and Somesh Jha.

82. An Empirical Analysis of the Impact of Data Augmentation on Distillation. Deepan Das, Haley Massa, Abhimanyu Kulkarni and Theodoros Rekatsinas.

83. It Is Likely That Your Loss Should be a Likelihood. Mark Hamilton, Evan Shelhamer and William Freeman.

84. Self-Adaptive Training: beyond Empirical Risk Minimization. Lang Huang, Chao Zhang and Hongyang Zhang.

85. BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty. Théo Guenais, Dimitris Vamvourellis, Yaniv Yacoby, Finale Doshi-Velez and Weiwei Pan.

86. Bayesian active learning for production, a systematic study and a reusable library. Parmida Atighehchian, Frédéric Branchaud-Charron and Alexandre Lacoste.

87. Certainty as Supervision for Test-Time Adaptation. Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen and Trevor Darrell.

88. Robust Deep Reinforcement Learning through Adversarial Loss. Tuomas Oikarinen, Tsui-Wei Weng and Luca Daniel.

89. Cold Posteriors and Aleatoric Uncertainty. Ben Adlam, Sam Smith and Jasper Snoek.

90. Ensemble Mean vs. Ensemble Variance: Which is a Better Uncertainty Metric for Incipient Disease Detection? Baihong Jin, Yingshui Tan, Xiangyu Yue, Yuxin Chen and Alberto Sangiovanni-Vincentelli.

91. Simplicity Bias and the Robustness of Neural Networks. Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain and Praneeth Netrapalli.

92. Exact posterior distributions of wide Bayesian neural networks. Jiri Hron, Yasaman Bahri, Roman Novak, Jeffrey Pennington and Jascha Sohl-Dickstein.

93. A Simulation-based Framework for Characterizing Predictive Distributions for Deep Learning. Jessica Ai, Beliz Gokkaya, Ilknur Kaynar Kabul, Erik Meijer, Audrey Flower, Ehsan Emamjomeh-Zadeh, Hannah Li, Li Chen, Neamah Hussein, Ousmane Dia and Sevi Baltaoglu.

94. Structured Weight Priors for Convolutional Neural Networks. Tim Pearce, Andrew Y.K. Foong and Alexandra Brintrup.

95. Learning Generative Models from Classifier Uncertainties. Siddharth Narayanaswamy and Brooks Paige.

96. DQI: A Guide to Benchmark Evaluation. Swaroop Mishra, Anjana Arunkumar, Bhavdeep Sachdeva, Chris Bryan and Chitta Baral.

97. Can Your AI Differentiate Cats from Covid-19? Sample Efficient Uncertainty Estimation for Deep Learning Safety. Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi and T. Yong-Jin Han.

98. Transferable Adversarial Examples for Atari 2600 Games. Damian Stachura and Michał Zając.

99. Robustness to Distribution Shifts using Multiple Environments. Anders Andreassen, Rebecca Roelofs and Behnam Neyshabur.

100. Our Evaluation Metric Needs an Update to Encourage Generalization. Swaroop Mishra, Anjana Arunkumar, Chris Bryan and Chitta Baral.

101. Harder or Different? A Closer Look at Distribution Shift in Dataset Reproduction. Shangyun Lu, Bradley Nott, Aaron Olson, Alberto Todeschini, Puya Vahabi, Yair Carmon and Ludwig Schmidt.

102. Empirical Scoring Rule Decomposition in Deep Learning. Tony Duan.

103. Failure Prediction by Confidence Estimation of Uncertainty-Aware Dirichlet Networks. Theodoros Tsiligkaridis.

104. RayS: A Ray Searching Method for Hard-label Adversarial Attack. Jinghui Chen and Quanquan Gu.

105. Joint Energy-Based Models for Semi-Supervised Classification. Stephen Zhao, Joern-Henrik Jacobsen and Will Grathwohl.

106. Single Shot MC Dropout Approximation. Kai Brach, Beate Sick and Oliver Duerr.

107. Reliable Uncertainties for Bayesian Neural Networks using Alpha-divergences. Hector Javier Hortua, Luigi Malago and Riccardo Volpi.

108. Evaluating Uncertainty Estimation Methods on 3D Semantic Segmentation of Point Clouds. Swaroop Bhandary, Nico Hochgeschwender, Paul Plöger and Matias Valdenegro-Toro.

109. Improving predictions of Bayesian neural networks via local linearization. Alexander Immer, Maciej Korzepa and Matthias Bauer.

110. URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks. Meet Vadera, Adam Cobb, Brian Jalaian and Benjamin Marlin.

111. Richness of Training Data Does Not Suffice: Robustness of Neural Networks Requires Richness of Hidden-Layer Activations. Kamil Nar and Shankar Sastry.

112. Robust Classification under Class-Dependent Domain Shift. Tigran Galstyan, Hrant Khachatrian, Greg Ver Steeg and Aram Galstyan.

113. You Need Only Uncertain Answers: Data Efficient Multilingual Question Answering. Zhihao Lyu, Danier Duolikun, Bowei Dai, Yuan Yao, Pasquale Minervini, Tim Z. Xiao and Yarin Gal.

114. Diverse Ensembles Improve Calibration. Asa Cooper Stickland and Iain Murray.

115. Exploring the Uncertainty Properties of Neural Networks’ Implicit Priors in the Infinite-Width Limit. Ben Adlam, Jaehoon Lee, Lechao Xiao, Jeffrey Pennington and Jasper Snoek.

116. Provable Robust Learning Based on Transformation-Specific Smoothing. Linyi Li, Maurice Weber, Xiaojun Xu, Luka Rimanic, Shuang Yang, Tao Xie, Ce Zhang and Bo Li.

117. Simple and Effective VAE Training with Calibrated Decoders. Oleh Rybkin, Kostas Daniilidis and Sergey Levine.

118. Amortized Conditional Normalized Maximum Likelihood. Aurick Zhou and Sergey Levine.

119. Neural Networks with Recurrent Generative Feedback. Yujia Huang, James Gornet, Sihui Dai, Zhiding Yu, Tan Nguyen, Doris Y. Tsao and Anima Anandkumar.

120. Robustness of Latent Representations of Variational Autoencoders. Andrea Karlova.

121. Learning approximate invariance requires far fewer data. Jean Michel Amath Sarr, Alassane Bah and Christophe Cambier.

122. MCU-Net: A framework towards uncertainty representations for decision support system patient referrals in healthcare contexts. Nabeel Seedat.

123. Uncertainty-sensitive Learning and Planning with Ensembles. Piotr Milos, Lukasz Kucinski, Konrad Czechowski, Piotr Kozakowski and Maciej Klimek.

124. Uncertainty in Multi-Interaction Trajectory Reconstruction. Vasileios Karavias, Ben Day and Pietro Lio.

125. Unsupervised Domain Adaptation in the Absence of Source Data. Roshni Sahoo, Divya Shanmugam and John Guttag.

126. A benchmark study on reliable molecular supervised learning via Bayesian learning. Doyeong Hwang, Grace Lee, Hanseok Jo, Seyoul Yoon and Seongok Ryu.

127. On the Power of Oblivious Poisoning Attacks. Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody and Abhradeep Thakurta.

128. Training Deep Neural Networks with Class-level Semantics for Explicable Classification. Alberto Olmo, Sailik Sengupta and Subbarao Kambhampati.

129. End-to-end Robustness for Sensing-Reasoning Machine Learning Pipelines. Zhuolin Yang, Zhikuan Zhao, Hengzhi Pei, Boxin Wang, Bojan Karlas, Ji Liu, Heng Guo, Bo Li and Ce Zhang.

130. Deep Robust Classification under Domain Shift with Conservative Uncertainty Estimation. Haoxuan Wang, Anqi Liu, Yisong Yue and Anima Anandkumar.

131. On Separability of Self-Supervised Representations. Vikash Sehwag, Mung Chiang and Prateek Mittal.

132. Dropout Strikes Back: Improved Uncertainty Estimation via Diversity Sampling. Evgenii Tsymbalov, Kirill Fedyanin and Maxim Panov.

133. Predictive Uncertainty for Probabilistic Novelty Detection in Text Classification. Jordy Van Landeghem, Matthew Blaschko, Bertrand Anckaert and Marie-Francine Moens.

134. Maximizing the Representation Gap between In-domain & OOD examples. Jay Nandy, Wynne Hsu and Mong Li Lee.

135. Regional Image Perturbation Reduces Lp Norms of Adversarial Examples While Maintaining Model-to-model Transferability. Utku Ozbulak, Jonathan Peck, Wesley De Neve, Bart Goossens, Yvan Saeys and Arnout Van Messem.

136. Lookahead Adversarial Semantic Segmentation. Hadi Jamali-Rad, Attila Szabo and Matteo Presutto.

137. Neural Network Calibration for Medical Imaging Classification Using DCA Regularization. Gongbo Liang, Yu Zhang and Nathan Jacobs.

138. Deep k-NN Defense Against Clean-Label Data Poisoning Attacks. Neehar Peri, Neal Gupta, Ronny Huang, Chen Zhu, Liam Fowl, Soheil Feizi, Tom Goldstein and John Dickerson.

139. Amortised Variational Inference for Hierarchical Mixture Models. Javier Antoran, Jiayu Yao, Weiwei Pan, Finale Doshi-Velez and Jose Miguel Hernandez-Lobato.

140. Benchmarking Search Methods for Generating NLP Adversarial Examples. Jin Yong Yoo, John X. Morris, Eli Lifland and Yanjun Qi.