ICMLW 2021

167 papers

$\alpha$-VAEs : Optimising Variational Inference by Learning Data-Dependent Divergence Skew Jacob Deasy, Tom Andrew McIver, Nikola Simidjievski, Pietro Lio

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A Closer Look at the Adversarial Robustness of Information Bottleneck Models Iryna Korshunova, David Stutz, Alexander A Alemi, Olivia Wiles, Sven Gowal

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A Primer on Multi-Neuron Relaxation-Based Adversarial Robustness Certification Kevin Roth

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A Resource-Efficient Method for Repeated HPO and NAS Problems Giovanni Zappella, David Salinas, Cedric Archambeau

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A Variational Perspective on Diffusion-Based Generative Models and Score Matching Chin-Wei Huang, Jae Hyun Lim, Aaron Courville

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Adaptation-Agnostic Meta-Training Jiaxin Chen, Li-Ming Zhan, Xiao-Ming Wu, Fu-lai Chung

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Adversarial EXEmples: Functionality-Preserving Optimization of Adversarial Windows Malware Luca Demetrio, Battista Biggio, Giovanni Lagorio, Alessandro Armando, Fabio Roli

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Adversarial for Good? How the Adversarial ML Community's Values Impede Socially Beneficial Uses of Attacks Kendra Albert, Maggie Delano, Bogdan Kulynych, Ram Shankar Siva Kumar

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Adversarial Interaction Attacks: Fooling AI to Misinterpret Human Intentions Nodens Koren, Xingjun Ma, Qiuhong Ke, Yisen Wang, James Bailey

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Adversarial Robustness of Streaming Algorithms Through Importance Sampling Vladimir Braverman, Avinatan Hassidim, Yossi Matias, Mariano Schain, Sandeep Silwal, Samson Zhou

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Adversarial Sample Detection via Channel Pruning Zuohui Chen, RenXuan Wang, Yao Lu, Jingyang Xiang, Qi Xuan

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Adversarial Semantic Contour for Object Detection Yichi Zhang, Zijian Zhu, Xiao Yang, Jun Zhu

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Adversarially Robust Learning via Entropic Regularization Gauri Jagatap, Ameya Joshi, Animesh Basak Chowdhury, Siddharth Garg, Chinmay Hegde

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Adversarially Trained Neural Policies in the Fourier Domain Ezgi Korkmaz

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Agent Forecasting at Flexible Horizons Using ODE Flows Alexander Radovic, Jiawei He, Janahan Ramanan, Marcus A Brubaker, Andreas Lehrmann

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AID-Purifier: A Light Auxiliary Network for Boosting Adversarial Defense Duhun Hwang, Eunjung Lee, Wonjong Rhee

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Attacking Few-Shot Classifiers with Adversarial Support Poisoning Elre Talea Oldewage, John F Bronskill, Richard E Turner

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Attacking Graph Classification via Bayesian Optimisation Xingchen Wan, Henry Kenlay, Binxin Ru, Arno Blaas, Michael Osborne, Xiaowen Dong

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Attention-Guided Black-Box Adversarial Attacks with Large-Scale Multiobjective Evolutionary Optimization Jie Wang, Zhaoxia Yin, Jing Jiang, Yang Du

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Audio Injection Adversarial Example Attack Xiaolei Liu, Xingshu Chen, Mingyong Yin, Yulong Wang, Teng Hu, Kangyi Ding

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Automated Discovery of Adaptive Attacks on Adversarial Defenses Chengyuan Yao, Pavol Bielik, Petar Tsankov, Martin Vechev

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Automated Learning Rate Scheduler for Large-Batch Training Chiheon Kim, Saehoon Kim, Jongmin Kim, Donghoon Lee, Sungwoong Kim

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AutoML Adoption in ML Software Koen Van der Blom, Alex Serban, Holger Hoos, Joost Visser

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BadNL: Backdoor Attacks Against NLP Models Xiaoyi Chen, Ahmed Salem, Michael Backes, Shiqing Ma, Yang Zhang

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Bag of Baselines for Multi-Objective Joint Neural Architecture Search and Hyperparameter Optimization Sergio Izquierdo, Julia Guerrero-Viu, Sven Hauns, Guilherme Miotto, Simon Schrodi, André Biedenkapp, Thomas Elsken, Difan Deng, Marius Lindauer, Frank Hutter

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Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization Akihiro Kishimoto, Djallel Bouneffouf, Radu Marinescu, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Paulito Pedregosa Palmes, Adi Botea

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Beta-CROWN: Efficient Bound Propagation with Per-Neuron Split Constraints for Neural Network Robustness Verification Shiqi Wang, Huan Zhang, Kaidi Xu, Xue Lin, Suman Jana, Cho-Jui Hsieh, J Zico Kolter

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Beyond Fine-Tuning: Transferring Behavior in Reinforcement Learning Víctor Campos, Pablo Sprechmann, Steven Stenberg Hansen, Andre Barreto, Steven Kapturowski, Alex Vitvitskyi, Adria Puigdomenech Badia, Charles Blundell

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Beyond In-Place Corruption: Insertion and Deletion in Denoising Probabilistic Models Daniel D. Johnson, Jacob Austin, Rianne van den Berg, Daniel Tarlow

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Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networks Xiao Yang, Yinpeng Dong, Tianyu Pang

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Certified Robustness Against Adversarial Patch Attacks via Randomized Cropping Wan-Yi Lin, Fatemeh Sheikholeslami, Jinghao Shi, Leslie Rice, J Zico Kolter

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Challenges for BBVI with Normalizing Flows Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe, Michael Riis Andersen, Jonathan H. Huggins, Aki Vehtari

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CoBERL: Contrastive BERT for Reinforcement Learning Andrea Banino, Adria Puigdomenech Badia, Jacob C Walker, Tim Scholtes, Jovana Mitrovic, Charles Blundell

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Combating Adversaries with Anti-Adversaries Motasem Alfarra, Juan Camilo Perez, Ali Thabet, Adel Bibi, Philip Torr, Bernard Ghanem

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Conformal Embedding Flows: Tractable Density Estimation on Learned Manifolds Brendan Leigh Ross, Jesse C Cresswell

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Consistency Regularization for Adversarial Robustness Jihoon Tack, Sihyun Yu, Jongheon Jeong, Minseon Kim, Sung Ju Hwang, Jinwoo Shin

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Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation Evgenii Nikishin, Romina Abachi, Rishabh Agarwal, Pierre-Luc Bacon

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Copula-Based Normalizing Flows Mike Laszkiewicz, Johannes Lederer, Asja Fischer

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Data Poisoning Won’t Save You from Facial Recognition Evani Radiya-Dixit, Florian Tramer

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Data-Efficient Exploration with Self Play for Atari Michael Laskin, Catherine Cang, Ryan Rudes, Pieter Abbeel

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Decision Transformer: Reinforcement Learning via Sequence Modeling Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch

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Decoupling Exploration and Exploitation in Reinforcement Learning Lukas Schäfer, Filippos Christianos, Josiah Hanna, Stefano V Albrecht

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Deep Signature Statistics for Likelihood-Free Time-Series Models Joel Dyer, Patrick W Cannon, Sebastian M Schmon

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Defending Adversaries Using Unsupervised Feature Clustering VAE Cheng Zhang, Pan Gao

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Defending Against Model Stealing via Verifying Embedded External Features Linghui Zhu, Yiming Li, Xiaojun Jia, Yong Jiang, Shu-Tao Xia, Xiaochun Cao

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Demystifying Adversarial Training via a Unified Probabilistic Framework Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin

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Density-Based Bonuses on Learned Representations for Reward-Free Exploration in Deep Reinforcement Learning Omar Darwiche Domingues, Corentin Tallec, Remi Munos, Michal Valko

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Detecting Adversarial Examples Is (Nearly) as Hard as Classifying Them Florian Tramer

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Detecting AutoAttack Perturbations in the Frequency Domain Peter Lorenz, Paula Harder, Dominik Straßel, Margret Keuper, Janis Keuper

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Did I Do That? Blame as a Means to Identify Controlled Effects in Reinforcement Learning Oriol Corcoll Andreu, Raul Vicente

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Diffeomorphic Explanations with Normalizing Flows Ann-Kathrin Dombrowski, Jan E Gerken, Pan Kessel

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Diffusion Priors in Variational Autoencoders Antoine Wehenkel, Gilles Louppe

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Direct Then Diffuse: Incremental Unsupervised Skill Discovery for State Covering and Goal Reaching Pierre-Alexandre Kamienny, Jean Tarbouriech, Alessandro Lazaric, Ludovic Denoyer

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Discovering and Achieving Goals with World Models Russell Mendonca, Oleh Rybkin, Kostas Daniilidis, Danijar Hafner, Deepak Pathak

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Discovering Diverse Nearly Optimal Policies with Successor Features Tom Zahavy, Brendan O'Donoghue, Andre Barreto, Sebastian Flennerhag, Volodymyr Mnih, Satinder Singh

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Discovering Weight Initializers with Meta Learning Dmitry Baranchuk, Artem Babenko

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Discrete Denoising Flows Alexandra Lindt, Emiel Hoogeboom

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Discrete Tree Flows via Tree-Structured Permutations Mai Elkady, Jim Lim, David I. Inouye

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Disentangled Predictive Representation for Meta-Reinforcement Learning Sephora Madjiheurem, Laura Toni

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Disrupting Model Training with Adversarial Shortcuts Ivan Evtimov, Ian Connick Covert, Aditya Kusupati, Tadayoshi Kohno

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Distilling the Knowledge from Conditional Normalizing Flows Dmitry Baranchuk, Vladimir Aliev, Artem Babenko

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Dynamic Pruning of a Neural Network via Gradient Signal-to-Noise Ratio Julien Niklas Siems, Aaron Klein, Cedric Archambeau, Maren Mahsereci

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Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods Marylou Gabrié, Grant M. Rotskoff, Eric Vanden-Eijnden

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Empirical Robustification of Pre-Trained Classifiers Mohammad Sadegh Norouzzadeh, Wan-Yi Lin, Leonid Boytsov, Leslie Rice, Huan Zhang, Filipe Condessa, J Zico Kolter

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Enhancing Certified Robustness via Smoothed Weighted Ensembling Chizhou Liu, Yunzhen Feng, Ranran Wang, Bin Dong

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Entropy Weighted Adversarial Training Minseon Kim, Jihoon Tack, Jinwoo Shin, Sung Ju Hwang

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Episodic Memory for Subjective-Timescale Models Alexey Zakharov, Matthew Crosby, Zafeirios Fountas

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Equivariant Manifold Flows Isay Katsman, Aaron Lou, Derek Lim, Qingxuan Jiang, Ser-Nam Lim, Christopher De Sa

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Examining the Human Perceptibility of Black-Box Adversarial Attacks on Face Recognition Benjamin Spetter-Goldstein, Nataniel Ruiz, Sarah Adel Bargal

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Exploration and Preference Satisfaction Trade-Off in Reward-Free Learning Noor Sajid, Panagiotis Tigas, Alexey Zakharov, Zafeirios Fountas, Karl Friston

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Exploration via Empowerment Gain: Combining Novelty, Surprise and Learning Progress Philip Becker-Ehmck, Maximilian Karl, Jan Peters, Patrick van der Smagt

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Exploration-Driven Representation Learning in Reinforcement Learning Akram Erraqabi, Harry Zhao, Marlos C. Machado, Yoshua Bengio, Sainbayar Sukhbaatar, Ludovic Denoyer, Alessandro Lazaric

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Explore and Control with Adversarial Surprise Arnaud Fickinger, Natasha Jaques, Samyak Parajuli, Michael Chang, Nicholas Rhinehart, Glen Berseth, Stuart Russell, Sergey Levine

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Fast Certified Robust Training with Short Warmup Zhouxing Shi, Yihan Wang, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh

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Fast Minimum-Norm Adversarial Attacks Through Adaptive Norm Constraints Maura Pintor, Fabio Roli, Wieland Brendel, Battista Biggio

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General Invertible Transformations for Flow-Based Generative Modeling Jakub Mikolaj Tomczak

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Generalization of the Change of Variables Formula with Applications to Residual Flows Niklas Koenen, Marvin N. Wright, Peter Maass, Jens Behrmann

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Generalizing Adversarial Training to Composite Semantic Perturbations Yun-Yun Tsai, Lei Hsiung, Pin-Yu Chen, Tsung-Yi Ho

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Generate More Imperceptible Adversarial Examples for Object Detection Siyuan Liang, Xingxing Wei, Xiaochun Cao

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GPy-ABCD: A Configurable Automatic Bayesian Covariance Discovery Implementation Thomas Fletcher, Alan Bundy, Kwabena Nuamah

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Helper-Based Adversarial Training: Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-Off Rahul Rade, Seyed-Mohsen Moosavi-Dezfooli

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Hidden Patch Attacks for Optical Flow Benjamin Wortman

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Hierarchical Few-Shot Imitation with Skill Transition Models Kourosh Hakhamaneshi, Ruihan Zhao, Albert Zhan, Pieter Abbeel, Michael Laskin

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Improve Generalization and Robustness of Neural Networks via Weight Scale Shifting Invariant Regularizations Ziquan Liu, Yufei Cui, Antoni B. Chan

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Improving Continuous Normalizing Flows Using a Multi-Resolution Framework Vikram Voleti, Chris Finlay, Adam M Oberman, Christopher Pal

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Improving Visual Quality of Unrestricted Adversarial Examples with Wavelet-VAE Wenzhao Xiang, Chang Liu, Shibao Zheng

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Incorporating Domain Knowledge into Neural-Guided Search via in Situ Priors and Constraints Brenden K Petersen, Claudio Santiago, Mikel Landajuela

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Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples Maura Pintor, Luca Demetrio, Angelo Sotgiu, Giovanni Manca, Ambra Demontis, Nicholas Carlini, Battista Biggio, Fabio Roli

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Interpreting Diffusion Score Matching Using Normalizing Flow Wenbo Gong, Yingzhen Li

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Intrinsic Control of Variational Beliefs in Dynamic Partially-Observed Visual Environments Nicholas Rhinehart, Jenny Wang, Glen Berseth, John D Co-Reyes, Danijar Hafner, Chelsea Finn, Sergey Levine

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Is It Time to Redefine the Classification Task for Deep Learning Systems? Keji Han, Yun Li, Songcan Chen

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Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization David Eriksson, Pierce I-Jen Chuang, Samuel Daulton, Peng Xia, Akshat Shrivastava, Arun Babu, Shicong Zhao, Ahmed A Aly, Ganesh Venkatesh, Maximilian Balandat

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Learning Task Agnostic Skills with Data-Driven Guidance Even Klemsdal, Sverre Herland, Abdulmajid Murad

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Learning Task-Relevant Representations with Selective Contrast for Reinforcement Learning in a Real-World Application Flemming Brieger, Daniel Alexander Braun, Sascha Lange

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Learning to Explore Multiple Environments Without Rewards Mirco Mutti, Mattia Mancassola, Marcello Restelli

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Learning to Represent State with Perceptual Schemata Wilka Torrico Carvalho, Murray Shanahan

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Less Is More: Feature Selection for Adversarial Robustness with Compressive Counter-Adversarial Attacks Emre Ozfatura, Muhammad Zaid Hameed, Kerem Ozfatura, Deniz Gunduz

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Leveraging Theoretical Tradeoffs in Hyperparameter Selection for Improved Empirical Performance Parikshit Ram, Alexander G. Gray, Horst Samulowitz

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Limited Budget Adversarial Attack Against Online Image Stream Hossein Mohasel Arjomandi, Mohammad Khalooei, Maryam Amirmazlaghani

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Long-Term Cross Adversarial Training: A Robust Meta-Learning Method for Few-Shot Classification Tasks Fan Liu, Shuyu Zhao, Xuelong Dai, Bin Xiao

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LRTuner: A Learning Rate Tuner for Deep Neural Networks Nikhil Iyer, Thejas Venkatesh, Nipun Kwatra, Ramachandran Ramjee, Muthian Sivathanu

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Manifold Density Estimation via Generalized Dequantization James Brofos, Marcus A Brubaker, Roy R Lederman

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MASAI: Multi-Agent Summative Assessment Improvement for Unsupervised Environment Design Yiping Wang, Michael Brandon Haworth

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Maximizing the Robust Margin Provably Overfits on Noiseless Data Konstantin Donhauser, Alexandru Tifrea, Michael Aerni, Reinhard Heckel, Fanny Yang

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Membership Inference Attacks on Lottery Ticket Networks Aadesh Mahavir Bagmar, Shishira Maiya, Shruti Bidwalkar, Amol Deshpande

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Meta Adversarial Training Against Universal Patches Jan Hendrik Metzen, Nicole Finnie, Robin Hutmacher

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Meta Learning the Step Size in Policy Gradient Methods Luca Sabbioni, Francesco Corda, Marcello Restelli

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Multimodal AutoML on Structured Tables with Text Fields Xingjian Shi, Jonas Mueller, Nick Erickson, Mu Li, Alex Smola

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Mutation Is All You Need Lennart Schneider, Florian Pfisterer, Martin Binder, Bernd Bischl

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Neural Fixed-Point Acceleration for Convex Optimization Shobha Venkataraman, Brandon Amos

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Non-Robust Feature Mapping in Deep Reinforcement Learning Ezgi Korkmaz

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On Fast Sampling of Diffusion Probabilistic Models Zhifeng Kong, Wei Ping

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On Frank-Wolfe Adversarial Training Theodoros Tsiligkaridis, Jay Roberts

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On Success and Simplicity: A Second Look at Transferable Targeted Attacks Zhengyu Zhao, Zhuoran Liu, Martha Larson

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On the Effectiveness of Poisoning Against Unsupervised Domain Adaptation Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm

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On the Expressivity of Bi-Lipschitz Normalizing Flows Alexandre Verine, Yann Chevaleyre, Fabrice Rossi, Benjamin Negrevergne

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On-the-Fly Learning of Adaptive Strategies with Bandit Algorithms Rashid Bakirov, Damien Fay, Bogdan Gabrys

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Out of Distribution Detection and Adversarial Attacks on Deep Neural Networks for Robust Medical Image Analysis Anisie Uwimana, Ransalu Senanayake

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Planning from Pixels in Environments with Combinatorially Hard Search Spaces Marco Bagatella, Miroslav Olšák, Michal Rolinek, Georg Martius

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Poisoning the Search Space in Neural Architecture Search Robert Wu, Nayan Saxena, Rohan Jain

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PonderNet: Learning to Ponder Andrea Banino, Jan Balaguer, Charles Blundell

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Pretrained Encoders Are All You Need Mina Khan, Advait Prashant Rane, Srivatsa P, Shriram Chenniappa, Rishabh Anand, Sherjil Ozair, Patricia Maes

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Query-Based Adversarial Attacks on Graph with Fake Nodes Zhengyi Wang, Hao Zhongkai, Jun Zhu

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RAD-TTS: Parallel Flow-Based TTS with Robust Alignment Learning and Diverse Synthesis Kevin J. Shih, Rafael Valle, Rohan Badlani, Adrian Lancucki, Wei Ping, Bryan Catanzaro

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Ranking Architectures by Feature Extraction Capabilities Debadeepta Dey, Shital Shah, Sebastien Bubeck

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Rectangular Flows for Manifold Learning Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John Patrick Cunningham

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Recurrent Flow Networks: A Recurrent Latent Variable Model for Density Modelling of Urban Mobility Daniele Gammelli, Filipe Rodrigues

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Red Alarm for Pre-Trained Models: Universal Vulnerability to Neuron-Level Backdoor Attacks Zhengyan Zhang, Guangxuan Xiao, Yongwei Li, Tian Lv, Fanchao Qi, Zhiyuan Liu, Yasheng Wang, Xin Jiang, Maosong Sun

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Reinforcement Learning as One Big Sequence Modeling Problem Michael Janner, Qiyang Li, Sergey Levine

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Replacing the Ex-Def Baseline in AutoML by Naive AutoML Felix Mohr, Marcel Wever

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Representation Learning for Out-of-Distribution Generalization in Reinforcement Learning Frederik Träuble, Andrea Dittadi, Manuel Wuthrich, Felix Widmaier, Peter Vincent Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer

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Representation Learning in Continuous-Time Score-Based Generative Models Korbinian Abstreiter, Stefan Bauer, Arash Mehrjou

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Representational Aspects of Depth and Conditioning in Normalizing Flows Frederic Koehler, Viraj Mehta, Andrej Risteski

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Reward Is Enough for Convex MDPs Tom Zahavy, Brendan O'Donoghue, Guillaume Desjardins, Satinder Singh

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Reward-Free Policy Space Compression for Reinforcement Learning Mirco Mutti, Stefano Del Col, Marcello Restelli

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Robust Recovery of Adversarial Examples Tejas Bana, Jatan Loya, Siddhant Ravindra Kulkarni

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ROPUST: Improving Robustness Through Fine-Tuning with Photonic Processors and Synthetic Gradients Alessandro Cappelli, Ruben Ohana, Julien Launay, Laurent Meunier, Iacopo Poli

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Self-Supervised Iterative Contextual Smoothing for Efficient Adversarial Defense Against Gray- and Black-Box Attack Sungmin Cha, Naeun Ko, YoungJoon Yoo, Taesup Moon

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Semantic Perturbations with Normalizing Flows for Improved Generalization Oğuz Kaan Yüksel, Sebastian U Stich, Martin Jaggi, Tatjana Chavdarova

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Sequential Automated Machine Learning: Bandits-Driven Exploration Using a Collaborative Filtering Representation Maxime Heuillet, Benoit Debaque, Audrey Durand

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Sliced Iterative Normalizing Flows Biwei Dai, Uros Seljak

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SmoothMix: Training Confidence-Calibrated Smoothed Classifiers for Certified Adversarial Robustness Jongheon Jeong, Sejun Park, Minkyu Kim, Heung-Chang Lee, Doguk Kim, Jinwoo Shin

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SparseDice: Imitation Learning for Temporally Sparse Data via Regularization Alberto Camacho, Izzeddin Gur, Marcin Lukasz Moczulski, Ofir Nachum, Aleksandra Faust

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Stabilizing Deep Q-Learning with ConvNets and Vision Transformers Under Data Augmentation Nicklas Hansen, Hao Su, Xiaolong Wang

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Strategically-Timed State-Observation Attacks on Deep Reinforcement Learning Agents You Qiaoben, Xinning Zhou, Chengyang Ying, Jun Zhu

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Tabular Data: Deep Learning Is Not All You Need Ravid Shwartz-Ziv, Amitai Armon

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Tangent Space Least Adaptive Clustering James Buenfil, Samson J Koelle, Marina Meila

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Task-Agnostic Continual Learning with Hybrid Probabilistic Models Polina Kirichenko, Mehrdad Farajtabar, Dushyant Rao, Balaji Lakshminarayanan, Nir Levine, Ang Li, Huiyi Hu, Andrew Gordon Wilson, Razvan Pascanu

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The DEformer: An Order-Agnostic Distribution Estimating Transformer Michael A. Alcorn, Anh Totti Nguyen

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The Effects of Invertibility on the Representational Complexity of Encoders in Variational Autoencoders Andrej Risteski, Divyansh Pareek

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The Importance of Non-Markovianity in Maximum State Entropy Exploration Mirco Mutti, Riccardo De Santi, Marcello Restelli

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The Interplay Between Distribution Parameters and the Accuracy-Robustness Tradeoff in Classification Alireza Mousavi Hosseini, Amir Mohammad Abouei, Mohammad Hossein Rohban

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Towards Achieving Adversarial Robustness Beyond Perceptual Limits Sravanti Addepalli, Samyak Jain, Gaurang Sriramanan, Shivangi Khare, Venkatesh Babu Radhakrishnan

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Towards Explaining Hyperparameter Optimization via Partial Dependence Plots Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl

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Towards Model Selection Using Learning Curve Cross-Validation Felix Mohr, Jan N. van Rijn

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Towards Safe Reinforcement Learning via Constraining Conditional Value at Risk Chengyang Ying, Xinning Zhou, Dong Yan, Jun Zhu

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Towards Transferable Adversarial Perturbations with Minimum Norm Fangcheng Liu, Chao Zhang, Hongyang Zhang

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Uncovering Universal Features: How Adversarial Training Improves Adversarial Transferability Jacob M. Springer, Melanie Mitchell, Garrett T. Kenyon

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Understanding Event-Generation Networks via Uncertainties Marco Bellagente, Michel Luchmann, Manuel Haussmann, Tilman Plehn

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Universal Adversarial Head: Practical Protection Against Video Data Leakage Jiawang Bai, Bin Chen, Dongxian Wu, Chaoning Zhang, Shu-Tao Xia

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Universal Approximation for Log-Concave Distributions Using Well-Conditioned Normalizing Flows Holden Lee, Chirag Pabbaraju, Anish Prasad Sevekari, Andrej Risteski

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Universal Approximation of Residual Flows in Maximum Mean Discrepancy Zhifeng Kong, Kamalika Chaudhuri

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Unsupervised Skill-Discovery and Skill-Learning in Minecraft Juan José Nieto, Roger Creus Castanyer, Xavier Giro-i-Nieto

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Using Anomaly Feature Vectors for Detecting, Classifying and Warning of Outlier Adversarial Examples Nelson Manohar-Alers, Ryan Feng, Sahib Singh, Jiguo Song, Atul Prakash

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Visual Adversarial Imitation Learning Using Variational Models Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn

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Visualizing MuZero Models Joery A. de Vries, Ken Voskuil, Thomas M. Moerland, Aske Plaat

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Why Be Adversarial? Let's Cooperate!: Cooperative Dataset Alignment via JSD Upper Bound Wonwoong Cho, Ziyu Gong, David I. Inouye

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