Poster
in
Workshop: Uncertainty and Robustness in Deep Learning Workshop (UDL)

Poster Session (click to see links)


Abstract:

1 Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks David Stutz, Matthias Hein and Bernt Schiele [ protected link dropped ]

2 Improving robustness against common corruptions by covariate shift adaptation Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel and Matthias Bethge https://meet.google.com/ojt-atoh-wup

3 A Unified View of Label Shift Estimation Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan and Zachary Lipton https://cmu.zoom.us/j/94831507038?pwd=RVVyVy96YXNiaGtjMUxyZUJ5dVcvQT09

4 A Benchmark of Medical Out of Distribution Detection Tianshi Cao, David Yu-Tung Hui, Chin-Wei Huang and Joseph Paul Cohen https://meet.google.com/jxk-paui-gao

5 Neural Ensemble Search for Performant and Calibrated Predictions Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris Holmes, Frank Hutter and Yee Whye Teh https://meet.google.com/uxs-uuxr-uwo

6 Bayesian model averaging is suboptimal for generalization under model misspecification Andres Masegosa https://meet.google.com/mpf-chva-pgy

7 Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder Zhisheng Xiao, Qing Yan and Yali Amit https://uchicago.zoom.us/j/98748547880?pwd=Q3Y0dUVPUFphbGY4NmNJK2hwZndWUT09 Password: 370663

8 A Closer Look at Accuracy vs. Robustness Yao-Yuan Yang, Cyrus Rashtchian, Hongyang Zhang, Ruslan Salakhutdinov and Kamalika Chaudhuri https://ucsd.zoom.us/j/92778085557?pwd=STJVVWMza0RKSFZsOUVIS0h6bVl5QT09

9 Depth Uncertainty in Neural Networks Javier Antorán, James Urquhart Allingham and José Miguel Hernández-Lobato https://us02web.zoom.us/j/5419103161?pwd=eXdqWURLc3o4SktwQWZOU2pZNkliQT09

10 Few-shot Out-of-Distribution Detection Kuan-Chieh Wang, Paul Vicol, Eleni Triantafillou and Richard Zemel https://vectorinstitute.zoom.us/j/97199418667?pwd=NjBWUXhzZUwrOHFBMkxSZEd2MDh3Zz09

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 https://us02web.zoom.us/j/88251013741?pwd=TzlOUlJXeExzUVdSUDE5RXFkTytWZz09

12 On using Focal Loss for Neural Network Calibration Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip Torr and Puneet Dokania https://meet.google.com/ehz-rzxf-xeu

13 AutoAttack: reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks Francesco Croce and Matthias Hein [ protected link dropped ]

14 Calibrated Top-1 Uncertainty estimates for classification by score based models Adam Oberman, Chris Finlay, Alexander Iannantuono and Tiago Salvador https://mcgill.zoom.us/j/91308261627

15 Bayesian Deep Ensembles via the Neural Tangent Kernel Bobby He, Balaji Lakshminarayanan and Yee Whye Teh https://meet.google.com/exb-jkju-vbr

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 https://berkeley.zoom.us/j/4219480859?pwd=QkNIYmNCZjlobEUxQ2Q5TzR4Qm1QQT09

17 Measuring Robustness to Natural Distribution Shifts in Image Classification Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht and Ludwig Schmidt https://berkeley.zoom.us/j/97514838599?pwd=SXdiOSthMzNUV01QYU5lVElFTTM2dz09

18 Redundant features can hurt robustness to distribution shift Guillermo Ortiz-Jimenez, Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli and Pascal Frossard https://epfl.zoom.us/j/99757698661?pwd=NVc5OUVHenFYc2pXVlFYbk12U0F4dz09 548657

19 Scalable Training with Information Bottleneck Objectives Andreas Kirsch, Clare Lyle and Yarin Gal https://meet.google.com/mzn-pjuh-kia

20 How Does Early Stopping Help Generalization Against Label Noise? Hwanjun Song Hwanjun, Minseok Kim, Dongmin Park and Jae-Gil Lee [ protected link dropped ]

21 Soft Labeling Affects Out-of-Distribution Detection of Deep Neural Networks Doyup Lee and Yeongjae Cheon [ protected link dropped ] 0deFHw

22 Learning Robust Representations with Score Invariant Learning Daksh Idnani and Jonathan Kao https://ucla.zoom.us/j/98883773408?pwd=Y3M2WmxNTW1HOS9hMlBZYmJJSHZ0dz09

23 PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction Sangdon Park, Osbert Bastani, Nikolai Matni and Insup Lee https://us04web.zoom.us/j/76965715700?pwd=U2tNWVBMd0RKMHcrNGlTa0VMQkY3dz09

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 https://harvard.zoom.us/j/96201359957?pwd=N3Q3RVJJZ0R0TmRuejYxTDRDMjAxUT09

25 Domain Generalization using Causal Matching Divyat Mahajan, Shruti Tople and Amit Sharma https://us02web.zoom.us/j/87167858273?pwd=V09XWTdjMzVSTHplcnJ6dCtpUWphUT09

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 meet.google.com/vqj-zckz-hbr

27 Predicting with High Correlation Features Devansh Arpit, Caiming Xiong and Richard Socher https://us04web.zoom.us/j/3643443782

28 Consistency Regularization for Certified Robustness of Smoothed Classifiers Jongheon Jeong and Jinwoo Shin https://us02web.zoom.us/j/85431063603?pwd=MGJ4YzR4Q2FXSWM4c3c3NURiR3NqZz09

29 DIBS: Diversity inducing Information Bottleneck in Model Ensembles Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal, Hugo Larochelle, Animesh Garg and Florian Shkurti https://utoronto.zoom.us/j/6995996022?pwd=UUIxNUNnQThLVmhLV0h5RDVJRG52dz09

30 On the relationship between class selectivity, dimensionality, and robustness Matthew Leavitt and Ari Morcos https://fb.zoom.us/j/98555977785 949102

31 Robust Variational Autoencoder for Tabular Data with β Divergence Haleh Aydore, Sergul Aydore, Richard Leahy and Anand Joshi https://usc.zoom.us/j/92860013817?pwd=aG5YekNUcFBIalV4TVNJYkhyb1UzZz09 906642

32 Uncertainty in Structured Prediction Andrey Malinin and Mark Gales https://yandex.zoom.us/j/98605165946?pwd=RkJ4RTVmYmt6eU1CcWU5RGgvNEMzdz09

33 Understanding and Improving Fast Adversarial Training Maksym Andriushchenko and Nicolas Flammarion https://epfl.zoom.us/j/98065886762?pwd=dTVFMUdFZllIZFVEMllDbkdxL2QvZz09

34 Predictive Complexity Priors Eric Nalisnick, Jonathan Gordon and Jose Miguel Hernandez Lobato https://meet.google.com/jyk-qhvg-kek

35 Provable Worst Case Guarantees for the Detection of Out-of-Distribution Data Julian Bitterwolf, Alexander Meinke and Matthias Hein [ protected link dropped ]

36 Ensemble Distribution Distillation via Regression Prior Networks Andrey Malinin, Sergey Chervontsev, Ivan Provilkov and Mark Gales meet.google.com/zbn-vwtv-eda

37 Generalizing to unseen domains via distribution matching Isabela Albuquerque, Joao Monteiro, Mohammad Darvishi, Tiago Falk and Ioannis Mitliagkas [ protected link dropped ] 2U0Qsc

38 GAN-mixup: Augmenting Across Data Manifolds for Improved Robustness Jy-Yong Sohn, Kangwook Lee, Jaekyun Moon and Dimitris Papailiopoulos https://us02web.zoom.us/j/84163398388?pwd=MGFXWm9VZGhDZ01mMXFCMkNJckdRdz09 240878

39 Improving out-of-distribution generalization via multi-task self-supervised pretraining Isabela Albuquerque, Nikhil Naik, Junnan Li, Nitish Shirish Keskar and Richard Socher [ protected link dropped ] 6byCUy

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 https://meet.google.com/cym-bnec-mbb

41 Tilted Empirical Risk Minimization Tian Li, Ahmad Beirami, Maziar Sanjabi and Virginia Smith https://cmu.zoom.us/j/96622035970?pwd=M21WMTllV0tuWkpMaUlHMDlBWmpFdz09

42 Towards Robust Classification with Deep Generative Forests Alvaro Henrique Chaim Correia, Robert Peharz and Cassio de Campos [ protected link dropped ]

43 Riemannian Continuous Normalizing Flows Emile Mathieu https://meet.google.com/vts-axeh-doo

44 Improving Calibration of BatchEnsemble with Data Augmentation Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael Dusenberry, Jasper Snoek, Balaji Lakshminarayanan and Dustin Tran https://us04web.zoom.us/j/79191629966?pwd=eVNEZ2NRVGY4NEc0Uk1DK2t1Ykh6QT09.

45 Environment Inference for Invariant Learning Elliot Creager, Jörn-Henrik Jacobsen and Richard Zemel https://vectorinstitute.zoom.us/j/98611353513?pwd=Mk5xa2VqLzVBV0JzdjZINXNlWXJMdz09 470387

46 Nonlinear Gradient Estimation for Query Efficient Blackbox Attack Huichen Li, Linyi Li, Xiaojun Xu, Xiaolu Zhang, Shuang Yang and Bo Li https://illinois.zoom.us/j/92637811021?pwd=WldhQXRVaENIWXFNQjdrdXhmcG1UQT09 NLBA

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 https://harvard.zoom.us/j/96654497889?pwd=Z2NrL05lQlozZndsQ25jV0JQdkRtZz09

48 CRUDE: Calibrating Regression Uncertainty Distributions Empirically Eric Zelikman, Christopher Healy, Sharon Zhou and Anand Avati https://stanford.zoom.us/j/92845303885?pwd=Kzd6d2tjMGdXL0VkK01JM09jYkVqUT09 519357

49 Positive-Unlabeled Learning with Arbitrarily Non-Representative Labeled Data Zayd Hammoudeh and Daniel Lowd https://uoregon.zoom.us/j/99760928750?pwd=dmVCUlI3WUlxNldDRGVqQmQrSDVFQT09 359623

50 Probabilistic Robustness Estimates for Deep Neural Networks Nicolas Couellan [ protected link dropped ]

51 Estimating Risk and Uncertainty in Deep Reinforcement Learning William Clements, Bastien Van Delft, Benoît-Marie Robaglia, Reda Bahi Slaoui and Sébastien Toth https://us02web.zoom.us/j/84543888187?pwd=cVdIS1ZjQUVzaTNkR0lRT2lTVzNxZz09

52 An Empirical Study of Invariant Risk Minimization Yo Joong Choe, Jiyeon Ham and Kyubyong Park https://cmu.zoom.us/j/97184088310?pwd=MGJ0bEYzQXg3RVhHSkxTTWY2RnBHdz09

53 Information-Bottleneck under Mean Field Initialization Vinayak Abrol and Jared Tanner [ protected link dropped ]

54 Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift Marvin Zhang, Henrik Marklund, Abhishek Gupta, Sergey Levine and Chelsea Finn https://berkeley.zoom.us/j/96584472302?pwd=TXlPWHFzeHhVTzROYnB4KzJVSVJhUT09

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 https://meet.google.com/zxr-qijn-xkx

56 Failures of Variational Autoencoders and their Effects on Downstream Tasks Yaniv Yacoby, Weiwei Pan and Finale Doshi-Velez https://harvard.zoom.us/j/98541353638?pwd=QUovTjVKYm4rM1NSamw1VUJ3eVI2Zz09

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 [ protected link dropped ]

58 Bayesian BERT for Trustful Hate Speech Detection Kristian Miok, Blaz Skrlj, Daniela Zaharie and Marko Robnik-Sikonja [ protected link dropped ] 12341234

59 On the Role of Dataset Quality and Heterogeneity in Model Confidence Yuan Zhao, Jiasi Chen and Samet Oymak https://us04web.zoom.us/j/76373416589?pwd=L09lNlBDUXcvNHZ6MjV3TTJUQzUxQT0

60 Certified Adversarial Robustness via Randomized Smoothing: a Case Study for Laplace Noises Jiaye Teng, Guanghe Lee and Yang Yuan [ protected link dropped ] password 3QAa4p

61 QUEST for MEDISYN: Quasi-norm based Uncertainty ESTimation for MEDical Image SYNthesis Uddeshya Upadhyay, Viswanath P. Sudarshan and Suyash P. Awate https://monash.zoom.us/j/4586078207?pwd=OGlzeHh0bVovM3lkNCttRW40SFNMZz09 quest

62 On uncertainty estimation in active learning for image segmentation Bo Li and Tommy Sonne Alstrøm https://dtudk.zoom.us/j/65926847472?pwd=NGU0cXVlMy9oeGQ3dmp5WlFCcWgvUT09

63 Bayesian Autoencoders: Analysing and Fixing the Bernoulli likelihood for Out-of-Distribution Detection Bang Xiang Yong, Tim Pearce and Alexandra Brintrup meet.google.com/uxr-hzin-coj

64 A Comparison of Bayesian Deep Learning for Out of Distribution Detection and Uncertainty Estimation John Mitros, Arjun Pakrashi and Brian Mac Namee https://meet.google.com/puu-yysf-owt?hs=122&authuser=0

65 Practical Bayesian Neural Networks via Adaptive Subgradient Optimization Methods Samuel Kessler, Arnold Salas, Vincent Tan Weng Choon, Stefan Zohren and Stephen Roberts https://us02web.zoom.us/j/81195534206

66 Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes Jake Snell and Richard Zemel https://vectorinstitute.zoom.us/j/99320884951?pwd=aEdhbE8wUWNIS1ZuZkc4ZHFKVGNWUT09

67 Characteristics of Monte Carlo Dropout in Wide Neural Networks Joachim Sicking, Maram Akila, Tim Wirtz, Sebastian Houben and Asja Fischer https://us02web.zoom.us/j/88053290381?pwd=T2FaaXk1aUhWcS9iSFFhTW9WY1NTdz09

68 On Power Laws in Deep Ensembles Ekaterina Lobacheva, Nadezhda Chirkova, Maxim Kodryan and Dmitry Vetrov [ protected link dropped ]

69 Outlier Detection through Null Space Analysis of Neural Networks Matthew Cook, Alina Zare and Paul Gader https://ufl.zoom.us/j/94556341852?pwd=MXRoVUw5M3BUMmFYcjlGOTBVdDhIQT09

70 In a forward direction: Analyzing distribution shifts in machine translation test sets over time Thomas Liao, Benjamin Recht and Ludwig Schmidt https://berkeley.zoom.us/j/98684240218?pwd=elZ3RVVOQllkdDZGTU1PZTY0dGZPUT09

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 https://illinois.zoom.us/j/95161238471?pwd=NHRrZ1c0ZlAwMVp6U2Vwd3U2OXk1dz09 926964

72 Untapped Potential of Data Augmentation: A Domain Generalization Viewpoint Vihari Piratla and Shiv Shankar https://meet.google.com/xya-jmcm-htd

73 Classifying Perturbation Types for Adversarial Robustness Against Multiple Threat Models Pratyush Maini, Xinyun Chen, Bo Li and Dawn Song https://berkeley.zoom.us/j/96355581753?pwd=djFrc0hMVmQ1RU5rMzNsbCtjbXVQdz09

74 A Critical Evaluation of Open-World Machine Learning Liwei Song, Vikash Sehwag, Arjun Nitin Bhagoji and Prateek Mittal https://princeton.zoom.us/j/6441846264?pwd=RWgzYjA2aWJVbnJvaVcvc2FQRm1HUT09

75 You won't believe you can learn CIFAR-10 with this: Another take on Information Bottleneck Objectives Andreas Kirsch, Clare Lyle and Yarin Gal https://meet.google.com/tes-egvb-nso

76 Principled Uncertainty Estimation for High Dimensional Data Pascal Notin, José Miguel Hernández-Lobato and Yarin Gal https://meet.google.com/xte-ywjb-paj

77 Rethink Autoencoders: Robust Manifold Learning Taihui Li, Rishabh Mehta, Zecheng Qian and Ju Sun https://umn.zoom.us/j/92216511031?pwd=UjN1K2IvRFVvaDlpVUlXZWFqMURZUT09 7zcn93

78 Continuous-Depth Bayesian Neural Networks Winnie Xu, Ricky T.Q. Chen and David Duvenaud https://utoronto.zoom.us/j/7553398989 755996

79 Robust Temporal Point Event Localization through Smoothing and Counting Julien Schroeter, Kirill Sidorov and David Marshall https://cardiff.zoom.us/j/96449530903?pwd=RjBtNDFmY0ZlT1EyMFJRZjdNdDh0UT09

80 Chi-square Information for Invariant Learning Prasanna Sattigeri, Soumya Ghosh and Samuel Hoffman https://us04web.zoom.us/j/77772482601?pwd=MXdDTDRUWmFJbktYcWVkT3NRRWUxdz09

81 Robust Out-of-distribution Detection via Informative Outlier Mining Jiefeng Chen, Sharon Li, Xi Wu, Yingyu Liang and Somesh Jha https://stanford.zoom.us/j/99582468298?pwd=V1VINTI1U1JJalRBaktTQnk4c0VvQT09

82 An Empirical Analysis of the Impact of Data Augmentation on Distillation Deepan Das, Haley Massa, Abhimanyu Kulkarni and Theodoros Rekatsinas https://meet.google.com/yzd-nbjv-qyx

83 It Is Likely That Your Loss Should be a Likelihood Mark Hamilton, Evan Shelhamer and William Freeman https://mit.zoom.us/j/98388816271?pwd=cTZscGVtNzhzTXUzUGxTcUtpVkh3dz09 udl

84 Self-Adaptive Training: beyond Empirical Risk Minimization Huang, Chao Zhang and Hongyang Zhang https://meet.google.com/cek-gsff-ebv

85 BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty Théo Guenais, Dimitris Vamvourellis, Yaniv Yacoby, Finale Doshi-Velez and Weiwei Pan https://us02web.zoom.us/j/84495842174?pwd=czNvaDkrMmZGRVgvdjlEczJxWUJwUT09

86 Bayesian active learning for production, a systematic study and a reusable library Parmida Atighehchian, Frédéric Branchaud-Charron and Alexandre Lacoste https://elementai.zoom.us/j/92111241672?pwd=NVg2V2Myd1lmKzFaWXlCa1dpNHZMUT09

87 Certainty as Supervision for Test-Time Adaptation Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen and Trevor Darrell https://berkeley.zoom.us/j/92105695905?pwd=cEk0T0U5Y0RDb2ZFSGZ2b09HTStIdz09

88 Robust Deep Reinforcement Learning through Adversarial Loss Tuomas Oikarinen, Tsui-Wei Weng and Luca Daniel https://mit.zoom.us/j/92724256949?pwd=K3JRdHhhVG1xV2tRbE81WXJQUGZsdz09

89 Cold Posteriors and Aleatoric Uncertainty Ben Adlam, Sam Smith and Jasper Snoek meet.google.com/ncb-ffxx-qgc

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 https://berkeley.zoom.us/j/95691253740?pwd=SDFlR2hXYkRBQWFJZ3cydXlwRnEyZz09

91 Simplicity Bias and the Robustness of Neural Networks Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain and Praneeth Netrapalli https://meet.google.com/bdq-nckv-tyq

92 Exact posterior distributions of wide Bayesian neural networks Jiri Hron, Yasaman Bahri, Roman Novak, Jeffrey Pennington and Jascha Sohl-Dickstein https://meet.google.com/pvb-brkx-png

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 https://us04web.zoom.us/j/72581014579?pwd=MFlJeXRKZTBJdWw4eDNoeTJEcGZSUT09

94 Structured Weight Priors for Convolutional Neural Networks Tim Pearce, Andrew Y.K. Foong and Alexandra Brintrup meet.google.com/rzg-vdpa-xir

95 Learning Generative Models from Classifier Uncertainties Siddharth Narayanaswamy and Brooks Paige https://meet.google.com/gjz-innd-iyt

96 DQI: A Guide to Benchmark Evaluation Swaroop Mishra, Anjana Arunkumar, Bhavdeep Sachdeva, Chris Bryan and Chitta Baral https://asu.zoom.us/j/95489346758

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 https://cmu.zoom.us/j/95091389695?pwd=K2RXbzZVSitzRVZadHB4eHZaZmlDZz09

98 Transferable Adversarial Examples for Atari 2600 Games Damian Stachura and Michał Zając https://meet.google.com/jwf-nqyd-fty

99 Robustness to Distribution Shifts using Multiple Environments Anders Andreassen, Rebecca Roelofs and Behnam Neyshabur [ protected link dropped ]

100 Our Evaluation Metric Needs an Update to Encourage Generalization Swaroop Mishra, Anjana Arunkumar and Chris Bryan https://asu.zoom.us/j/92127915576

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 https://stanford.zoom.us/j/98068202076?pwd=LytZdjg5bTR5eldVcFFHQVpSaHRzZz09

102 Empirical Scoring Rule Decomposition in Deep Learning Tony Duan https://teams.microsoft.com/l/meetup-join/19%3ameeting_NTBkNzE2OTYtODQwOS00MjI0LTgyNjQtY2Q3YWRhN2M1ZWNj%40thread.v2/0?context=%7b%22Tid%22%3a%2272f988bf-86f1-41af-91ab-2d7cd011db47%22%2c%22Oid%22%3a%2233328e52-be2d-485a-a0c5-7a8d97aaa4e2%22%7d

103 Failure Prediction by Confidence Estimation of Uncertainty-Aware Dirichlet Networks Theodoros Tsiligkaridis https://mit.zoom.us/j/95278652551?pwd=WHNwNEsxUUVOd0FSaUVHbkJaNHp5QT09 140350

104 RayS: A Ray Searching Method for Hard-label Adversarial Attack Jinghui Chen and Quanquan Gu https://ucla.zoom.us/j/92290062678?pwd=MThrUVVDSnlEU2FNWG10Yk1CcnRlZz09 987689

105 Joint Energy-Based Models for Semi-Supervised Classification Stephen Zhao, Joern-Henrik Jacobsen and Will Grathwohl https://us02web.zoom.us/j/88327646584?pwd=ZkxBcDNRMU9WVUt0ODJ6SDhoYk5zZz09 231335

106 Single Shot MC Dropout Approximation Kai Brach, Beate Sick and Oliver Duerr https://us02web.zoom.us/j/89892013425?pwd=VDZQN2JLSy9hTVg3VGpqOFhBdVJiZz09 1yqhv2

107 Reliable Uncertainties for Bayesian Neural Networks using Alpha-divergences Hector Javier Hortua, Luigi Malago and Riccardo Volpi [ protected link dropped ] 8a3QWf

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

109 Improving predictions of Bayesian neural networks via local linearization Alexander Immer, Maciej Korzepa and Matthias Bauer https://ethz.zoom.us/j/98684846685?pwd=aVJFS3VNdSsyMGVzOFQvbW5qSisrZz09

110 URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks Meet Vadera, Adam Cobb, Brian Jalaian and Benjamin Marlin https://umass-amherst.zoom.us/j/99916090662?pwd=N1E1Mm9LcEQ4bmhjNWlWdTlyU3BGZz09

111 Richness of Training Data Does Not Suffice: Robustness of Neural Networks Requires Richness of Hidden-Layer Activations Kamil Nar and Shankar Sastry https://berkeley.zoom.us/j/98295724751?pwd=cW5QQ2EzL2RaUW15OWs3T3JrTjk2Zz09

112 Robust Classification under Class-Dependent Domain Shift Tigran Galstyan, Hrant Khachatrian, Greg Ver Steeg and Aram Galstyan https://us02web.zoom.us/j/82576216178?pwd=RzVGQlZUNE95S1NrRktaRk5WTXMrdz09 736349

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 https://us02web.zoom.us/j/88327646584?pwd=ZkxBcDNRMU9WVUt0ODJ6SDhoYk5zZz09 231335

114 Diverse Ensembles Improve Calibration Asa Cooper Stickland and Iain Murray [ protected link dropped ]

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 meet.google.com/agb-migr-qdt

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 https://ethz.zoom.us/j/98321715637?pwd=Zk1hS0J1NUlteWYxUmtUYVlXb2hOdz09

117 Simple and Effective VAE Training with Calibrated Decoders Oleh Rybkin, Kostas Daniilidis and Sergey Levine meet.google.com/hvr-sdbx-syc

118 Amortized Conditional Normalized Maximum Likelihood Aurick Zhou and Sergey Levine https://berkeley.zoom.us/j/4913207255?pwd=VGN5bXRJQi9HRXJEbFp6SjZ3a29idz09

119 Neural Networks with Recurrent Generative Feedback Yujia Huang, James Gornet, Sihui Dai, Zhiding Yu, Tan Nguyen, Doris Y. Tsao and Anima Anandkumar https://us02web.zoom.us/j/6830799787?pwd=NGU3VFRvWTdhbTNiK1YzVHgzMEs2QT09 9appDa

120 Robustness of Latent Representations of Variational Autoencoders Andrea Karlova https://meet.google.com/hmk-kory-kba

121 Learning approximate invariance requires far fewer data Jean Michel Amath Sarr, Alassane Bah and Christophe Cambier https://us04web.zoom.us/j/75476163865?pwd=Z3RES1dMWFVWZkE4YjM2RzRLWUJ6Zz09

122 MCU-Net: A framework towards uncertainty representations for decision support system patient referrals in healthcare contexts Nabeel Seedat [ protected link dropped ]

123 Uncertainty-sensitive Learning and Planning with Ensembles Piotr Milos, Lukasz Kucinski, Konrad Czechowski, Piotr Kozakowski and Maciej Klimek https://us02web.zoom.us/j/89300913614?pwd=cVJjdFRpaDFZRVRMQXBsSzZzOGVSQT09

124 Uncertainty in Multi-Interaction Trajectory Reconstruction Vasileios Karavias, Ben Day and Pietro Lio https://meet.google.com/uqx-qudh-ish

125 Unsupervised Domain Adaptation in the Absence of Source Data Roshni Sahoo, Divya Shanmugam and John Guttag https://mit.zoom.us/j/94674840454?pwd=L2NMaHBsaTRYZnE4TjZydmJERCtqUT09

126 A benchmark study on reliable molecular supervised learning via Bayesian learning Doyeong Hwang, Grace Lee, Hanseok Jo, Seyoul Yoon and Seongok Ryu https://meet.google.com/hiy-bbdm-qxo

127 On the Power of Oblivious Poisoning Attacks Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody and Abhradeep Thakurta https://virginia.zoom.us/j/97053762096

128 Training Deep Neural Networks with Class-level Semantics for Explicable Classification Alberto Olmo, Sailik Sengupta and Subbarao Kambhampati https://asu.zoom.us/j/93814557218?pwd=K1hjc3NqWXlLQm1pN0tHTk1rdUNJZz09

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 https://illinois.zoom.us/j/2530610336?pwd=WXVZYlkreDYyUjRNdFh0Qk1IMURjUT09 password 633681

130 Deep Robust Classification under Domain Shift with Conservative Uncertainty Estimation Haoxuan Wang, Anqi Liu, Yisong Yue and Anima Anandkumar https://caltech.zoom.us/j/92474618222?pwd=WS91UjhVY1p4WThHQjRRYU5LMnpJQT09

131 On Separability of Self-Supervised Representations Vikash Sehwag, Mung Chiang and Prateek Mittal https://princeton.zoom.us/j/3871410688?pwd=RldBOUNwMnhFU3d4ZGpRSVR2YWJSdz09

132 Dropout Strikes Back: Improved Uncertainty Estimation via Diversity Sampling Evgenii Tsymbalov, Kirill Fedyanin and Maxim Panov [ protected link dropped ]

133 Predictive Uncertainty for Probabilistic Novelty Detection in Text Classification Jordy Van Landeghem, Matthew Blaschko, Bertrand Anckaert and Marie-Francine Moens https://us02web.zoom.us/j/86332883100?pwd=TVd3UytVTkF1R1V1RlNpdTZqbWlaZz09

134 Maximizing the Representation Gap between In-domain & OOD examples Jay Nandy, Wynne Hsu and Mong Li Lee https://nus-sg.zoom.us/j/99627439405?pwd=WFlkNzhndlo3TTU5YytyenduN0NWQT09

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 [ protected link dropped ] 135

136 Lookahead Adversarial Semantic Segmentation Hadi Jamali-Rad, Attila Szabo and Matteo Presutto meet.google.com/jgc-nfyp-nsn

137 Neural Network Calibration for Medical Imaging Classification Using DCA Regularization Gongbo Liang, Yu Zhang and Nathan Jacobs https://uky.zoom.us/j/93398011263?pwd=RUlBYzU3N1N4ekdzWEdZZ0czSnJpQT09

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 https://umd.zoom.us/j/93698080878?pwd=S2VjZjAzZHB3RXlSUWtiRWh6K1N0dz09

139 Amortised Variational Inference for Hierarchical Mixture Models Javier Antoran, Jiayu Yao, Weiwei Pan, Finale Doshi-Velez and Jose Miguel Hernandez-Lobato https://harvard.zoom.us/j/95984006384?pwd=N09BSlRGWWR5T1czNE5vQjBGbm9jdz09

140 Benchmarking Search Methods for Generating NLP Adversarial Examples Jin Yong Yoo, John X. Morris, Eli Lifland and Yanjun Qi https://virginia.zoom.us/j/97965655398?pwd=aWd2ZW1EK2l1Y2wzL3ZMVGF0QW1rQT09

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