Uncertainty and Robustness in Deep Learning Workshop (UDL)

Sharon Yixuan Li, Balaji Lakshminarayanan, Dan Hendrycks, Tom Dietterich, Jasper Snoek

Keywords:  • Model uncertainty estimation and calibration    • Probabilistic (Bayesian and non-Bayesian) neural networks    • Anomaly detection and out-of-distribution detection    • Robustness to distribution shift    • Model misspecification    • Quantifying different types of uncertainty    • Open world recognition  


There has been growing interest in rectifying deep neural network instabilities. Challenges arise when models receive samples drawn from outside the training distribution. For example, a neural network tasked with classifying handwritten digits may assign high confidence predictions to cat images. Anomalies are frequently encountered when deploying ML models in the real world. Well-calibrated predictive uncertainty estimates are indispensable for many machine learning applications, such as self-driving vehicles and medical diagnosis systems. Generalization to unseen and worst-case inputs is also essential for robustness to distributional shift. In order to have ML models reliably predict in open environment, we must deepen technical understanding in the emerging areas of: (1) learning algorithms that can detect changes in data distribution (e.g. out-of-distribution examples); (2) mechanisms to estimate and calibrate confidence produced by neural networks in typical and unforeseen scenarios; (3) methods to improve out-of-distribution generalization, including generalization to temporal, geographical, hardware, adversarial, and image-quality changes; (4) benchmark datasets and protocols for evaluating model performance under distribution shift; and (5) key applications of robust and uncertainty-aware deep learning (e.g., computer vision, robotics, self-driving vehicles, medical imaging) as well as broader machine learning tasks.

This workshop will bring together researchers and practitioners from the machine learning communities, and highlight recent work that contributes to addressing these challenges. Our agenda will feature contributed papers with invited speakers. Through the workshop we hope to help identify fundamentally important directions on robust and reliable deep learning, and foster future collaborations.

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Fri 7:30 a.m. - 7:40 a.m. [iCal]

Sharon Yixuan Li
Fri 7:40 a.m. - 8:10 a.m. [iCal]

Matthias Hein
Fri 8:10 a.m. - 8:15 a.m. [iCal]

Zhisheng Xiao
Fri 8:15 a.m. - 8:20 a.m. [iCal]

Yao-Yuan Yang
Fri 8:20 a.m. - 8:25 a.m. [iCal]

Javier Antorán, James U Allingham
Fri 8:25 a.m. - 8:30 a.m. [iCal]

Jackson Wang
Fri 8:30 a.m. - 8:35 a.m. [iCal]

Luis Oala
Fri 8:35 a.m. - 8:40 a.m. [iCal]

Jishnu Mukhoti
Fri 8:40 a.m. - 8:45 a.m. [iCal]

Francesco Croce
Fri 8:45 a.m. - 8:50 a.m. [iCal]

Adam Oberman
Fri 9:00 a.m. - 10:00 a.m. [iCal]

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

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

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

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

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

6 Bayesian model averaging is suboptimal for generalization under model misspecification Andres Masegosa

7 Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder Zhisheng Xiao, Qing Yan and Yali Amit Password: 370663

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

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

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

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

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

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

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

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 548657

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 Hwanjun, 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 0deFHw

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 949102

31 Robust Variational Autoencoder for Tabular Data with β Divergence Haleh Aydore, Sergul Aydore, Richard Leahy and Anand Joshi 906642

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 2U0Qsc

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

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 470387

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

48 CRUDE: Calibrating Regression Uncertainty Distributions Empirically Eric Zelikman, Christopher Healy, Sharon Zhou and Anand Avati 519357

49 Positive-Unlabeled Learning with Arbitrarily Non-Representative Labeled Data Zayd Hammoudeh and Daniel Lowd 359623

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 12341234

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

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

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

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 926964

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 You won't believe you can learn CIFAR-10 with this: Another take on 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 7zcn93

78 Continuous-Depth Bayesian Neural Networks Winnie Xu, Ricky T.Q. Chen and David Duvenaud 755996

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 udl

84 Self-Adaptive Training: beyond Empirical Risk Minimization 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 and Chris Bryan

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 140350

104 RayS: A Ray Searching Method for Hard-label Adversarial Attack Jinghui Chen and Quanquan Gu 987689

105 Joint Energy-Based Models for Semi-Supervised Classification Stephen Zhao, Joern-Henrik Jacobsen and Will Grathwohl 231335

106 Single Shot MC Dropout Approximation Kai Brach, Beate Sick and Oliver Duerr 1yqhv2

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

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 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 231335

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 9appDa

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 password 633681

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 135

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

Fri 10:00 a.m. - 10:30 a.m. [iCal]
Coffee Break (Break)
Fri 10:30 a.m. - 11:00 a.m. [iCal]

Finale Doshi-Velez
Fri 11:00 a.m. - 11:30 a.m. [iCal]

Percy Liang
Fri 11:30 a.m. - 12:30 p.m. [iCal]

Fri 12:30 p.m. - 1:30 p.m. [iCal]
Lunch Break (Break)
Fri 1:30 p.m. - 2:00 p.m. [iCal]

Raquel Urtasun
Fri 2:00 p.m. - 2:10 p.m. [iCal]

David Stutz
Fri 2:10 p.m. - 2:20 p.m. [iCal]

Steffen Schneider
Fri 2:20 p.m. - 2:30 p.m. [iCal]

Saurabh Garg
Fri 2:30 p.m. - 3:00 p.m. [iCal]

Justin Gilmer
Fri 3:00 p.m. - 3:30 p.m. [iCal]
Coffee Break (Break)
Fri 3:30 p.m. - 3:40 p.m. [iCal]

Joseph Paul Cohen
Fri 3:40 p.m. - 3:50 p.m. [iCal]

Sheheryar Zaidi
Fri 3:50 p.m. - 4:00 p.m. [iCal]

Andres Arrendondo