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ICML 2020 Workshop book Schedule Highlights Generated Mon Dec 21, 2020 Workshop organizers make last-minute changes to their schedule. Download this document again to get the lastest changes, or use the ICML mobile application. July 13, 2020 None WiML D&I Chairs Remarks: Sinead Williamson and Rachel Thomas July 17, 2020 None Graph Representation Learning and Beyond (GRL+) Veličković, Bronstein, Deac, Hamilton, Hamrick, Hashemi, Jegelka, Leskovec, Liao, Monti, Sun, Swersky, Ying, Žitnik None XXAI: Extending Explainable AI Beyond Deep Models and Classifiers Samek, Holzinger, Fong, Moon, Mueller None Self-supervision in Audio and Speech Ravanelli, Serdyuk, Hjelm, Ramabhadran, Parcollet None 5th ICML Workshop on Human Interpretability in Machine Learning (WHI) Weller, Xiang, Dhurandhar, Kim, Wei, Varshney, Bhatt None Law & Machine Learning Castets-Renard, Cussat-Blanc, Risser None Learning with Missing Values Josse, Frellsen, Mattei, Varoquaux None Healthcare Systems, Population Health, and the Role of Health-tech Heaukulani, Palla, Heller, Prasad, Ghassemi None Challenges in Deploying and Monitoring Machine Learning Systems Tosi, Korda, Lawrence None Workshop on AI for Autonomous Driving (AIAD) Chao, McAllister, Gaidon, Li, Kreiss None MLRetrospectives: A Venue for Self-Reflection in ML Research Forde, Dodge, Jaiswal, Lowe, Liu, Pineau, Bengio None Participatory Approaches to Machine Learning Zhou, Madras, Raji, Milli, Kulynych, Zemel None Workshop on Continual Learning Fayek, Chaudhry, Lopez-Paz, Belilovsky, Schwarz, Pickett, Aljundi, Ebrahimi, Dokania None Workshop on eXtreme Classification: Theory and Applications Choromanska, Langford, Majzoubi, Prabhu None ICML 2020 Workshop on Computational Biology Aghamirzaie, Anderson, Azizi, Diallo, Burdziak, Gallaher, Kundaje, Pe'er, Prabhakaran, Remita, Robertson-Tessi, Tansey, Vogt, Xie None Object-Oriented Learning: Perception, Representation, and Reasoning Ahn, Kosiorek, Hamrick, van Steenkiste, Bengio None Theoretical Foundations of Reinforcement Learning Brunskill, Lykouris, Simchowitz, Sun, Wang None ML Interpretability for Scientific Discovery Venugopalan, Brenner, Linderman, Kim None Uncertainty and Robustness in Deep Learning Workshop (UDL) 2 Li, Lakshminarayanan, Hendrycks, Dietterich, Snoek None Beyond first order methods in machine learning systems Berahas, Gholaminejad, Kyrillidis, Mahoney, Roosta July 18, 2020 None 4th Lifelong Learning Workshop Sodhani, Chandar, Ravindran, Precup None INNF+: Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models Huang, Krueger, Van den Berg, Papamakarios, Cremer, Chen, J. Rezende None Inductive Biases, Invariances and Generalization in Reinforcement Learning Goyal, Ke, Wang, Weber, Viola, Schölkopf, Bauer None Negative Dependence and Submodularity: Theory and Applications in Machine Learning Mariet, Derezinski, Gartrell None Federated Learning for User Privacy and Data Confidentiality Baracaldo, Choudhury, Joshi, Raskar, Wang, Yu None Machine Learning for Global Health Belgrave, Hyland, Onu, Furnham, Mwebaze, Lawrence None Bridge Between Perception and Reasoning: Graph Neural Networks & Beyond Tang, Song, Leskovec, Liao, Li, Fidler, Zemel, Salakhutdinov None Economics of privacy and data labor Vasiloglou, Cummings, Weyl, Koutris, Young, Jia, Dao, Waggoner None 7th ICML Workshop on Automated Machine Learning (AutoML 2020) Hutter, Vanschoren, Lindauer, Weill, Eggensperger, Feurer None Real World Experiment Design and Active Learning Bogunovic, Neiswanger, Yue None 1st Workshop on Language in Reinforcement Learning (LaReL) Nardelli, Luketina, Nardelli, Foerster, Zhong, Andreas, Grefenstette, Rocktäschel None Workshop on Learning in Artificial Open Worlds Szlam, Hofmann, Salakhutdinov, Kuno, Guss, Srinet, Houghton None Incentives in Machine Learning Faltings, Liu, Parkes, Radanovic, Song None Machine Learning for Media Discovery Schmidt, Nieto, Gouyon, Raimond, Kinnaird, Lanckriet None 2nd ICML Workshop on Human in the Loop Learning (HILL) Zhang, Wang, Yu, Wu, Darrell 3 July 13, 2020 July 13, 2020 4 July 17, 2020 July 17, 2020 01:30 Virtual Poster Session #1 AM 02:30 Novel Applications: Wiki-CS: A AM Wikipedia-Based Benchmark for Graph Representation Learning and Graph Neural Networks Mernyei Beyond (GRL+) 02:40 Novel Applications: Graph Neural AM Networks for Massive MIMO Petar Veličković, Michael M. Bronstein, Andreea Detection Scotti Deac, Will Hamilton, Jessica Hamrick, Milad Hashemi, Stefanie Jegelka, Jure Leskovec, Renjie Liao, Federico 02:50 Novel Applications: Embedding a Monti, Yizhou Sun, Kevin Swersky, Zhitao Ying, AM random graph via GNN: Extended Marinka Žitnik mean-field inference theory and RL applications to NP-Hard multi-robot/ Thu Jul 16, 23:40 PM machine scheduling Kang Recent years have seen a surge in research on 03:00 Update: PyTorch Geometric Fey graph representation learning, including AM techniques for deep graph embeddings, 03:10 Update: Deep Graph Library Karypis generalizations of CNNs to graph-structured data, AM and neural message-passing approaches. These 03:20 Update: Open Graph Benchmark advances in graph neural networks and related AM Leskovec techniques have led to new state-of-the-art 03:30 Lunch Break results in numerous domains: chemical synthesis, AM 3D-vision, recommender systems, question 04:30 Original Research: Get Rid of answering, continuous control, self-driving and AM Suspended Animation: Deep social network analysis. Building on the Diffusive Neural Network for Graph successes of three related workshops from last Representation Learning Zhang year (at ICML, ICLR and NeurIPS), the primary 04:45 Original Research: Learning Graph goal for this workshop is to facilitate community AM Models for Template-Free building, and support expansion of graph Retrosynthesis Somnath representation learning into more 05:00 Original Research: Frequent interdisciplinary projects with the natural and AM Subgraph Mining by Walking in social sciences. With hundreds of new Order Embedding Space Ying researchers beginning projects in this area, we 05:15 Invited Talk: Kyle Cranmer Cranmer hope to bring them together to consolidate this AM fast-growing area into a healthy and vibrant 05:45 Invited Talk: Danai Koutra Koutra subfield. Especially, we aim to strongly promote AM novel and exciting applications of graph representation learning across the sciences, 06:15 Q&A / Discussions / Coffee 2 reflected in our choices of invited speakers. AM 06:45 COVID-19 Applications: Navigating AM the Dynamics of Financial Schedule Embeddings over Time Gogoglou 06:55 COVID-19 Applications: Integrating AM Logical Rules Into Neural Multi-Hop 11:40 Opening Remarks Veličković, Deac Reasoning for Drug Repurposing Liu PM 07:05 COVID-19 Applications: Gaining 12:00 Invited Talk: Xavier Bresson Bresson AM insight into SARS-CoV-2 infection AM and COVID-19 severity using self- 12:30 Invited Talk: Thomas Kipf Kipf supervised edge features and Graph AM Neural Networks Sehanobish 01:00 Q&A / Discussions / Coffee 1 07:15 Invited Talk: Tina Eliassi-Rad Eliassi- AM AM Rad 5 July 17, 2020 07:45 Invited Talk: Raquel Urtasun Urtasun N/A (#34 / Sess. 1) Set2Graph: Learning AM Graphs From Sets Serviansky 08:15 Q&A / Discussions / Coffee 3 N/A (#96 / Sess. 1) Active Learning on AM Graphs via Meta Learning Madhawa 08:45 Virtual Poster Session #2 N/A (#40 / Sess. 2) HNHN: Hypergraph AM Networks with Hyperedge Neurons 09:45 Closing Remarks Veličković Dong AM N/A (#95 / Sess. 2) Graphein - a Python N/A (#10 / Sess. 2) Multi-Graph Neural Library for Geometric Deep Learning Operator for Parametric Partial and Network Analysis on Protein Differential Equations Li Structures Jamasb N/A (#12 / Sess. 1) Deep Graph N/A (#43 / Sess. 2) Uncovering the Contrastive Representation Learning Folding Landscape of RNA Zhu Secondary Structure with Deep N/A (#15 / Sess. 2) Learning Distributed Graph Embeddings Castro Representations of Graphs with N/A (#46 / Sess. 2) Discrete Planning Geo2DR Scherer with End-to-end Trained Neuro- N/A (#18 / Sess. 1) Hierarchical Protein algorithmic Policies Vlastelica Function Prediction with Tail-GNNs Pogancic Spalević N/A (#92 / Sess. 1) From Graph Low- N/A (#19 / Sess. 1) Neural Bipartite Rank Global Attention to 2-FWL Matching Georgiev Approximation Puny N/A (#20 / Sess. 1) Principal N/A (#90 / Sess. 1) Pointer Graph Neighbourhood Aggregation for Networks Veličković Graph Nets Corso N/A (#50 / Sess. 1) Graph Convolutional N/A (#21 / Sess. 2) Graph Neural Gaussian Processes for Link Networks for the Prediction of Prediction Opolka Substrate-Specific Organic Reaction N/A (#86 / Sess. 2) Graph Generation Conditions Maser with Energy-Based Models Liu N/A (#23 / Sess. 2) A Note on Over- N/A (#54 / Sess. 2) SE(3)-Transformers: Smoothing for Graph Neural 3D Roto-Translation Equivariant Networks Cai Attention Networks Fuchs N/A (#24 / Sess. 2) Degree-Quant: N/A (#84 / Sess. 2) UniKER: A Unified Quantization-Aware Training for Framework for Combining Graph Neural Networks Tailor Embedding and Horn Rules for N/A (#26 / Sess. 2) Message Passing Knowledge Graph Inference Cheng Query Embedding Daza N/A (#83 / Sess. 2) Connecting Graph N/A (#27 / Sess. 2) Learning Graph Convolutional Networks and Graph- Structure with A Finite-State Regularized PCA Zhao Automaton Layer Johnson N/A (#77 / Sess. 1) SIGN: Scalable N/A (#29 / Sess. 2) Few-shot link Inception Graph Neural Networks prediction via graph neural Frasca networks for Covid-19 drug- N/A (#76 / Sess. 1) Graph Clustering with repurposing Zheng Graph Neural Networks Tsitsulin N/A (#31 / Sess.
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