| 9:00 am - 9:15 am | Welcome to BayLearn 2020, BayLearn Organizers: Jerremy Holland,Jean-François Paiement, Sudarshan Lamkhede, Alice Xiang |
| 9:15 am - 10:00 am | Keynote 1: Timnit Gebru |
| 10:00 am - 10:15 am | Q&A |
| 10:15 am - 10:30 am | BREAK |
| 10:30 am - 11:00 am | Keynote 2: Sandrine Dudoit |
| 11:00 am - 11:15 am | Q&A |
11:15 am - 11:55 pm
| Keynote 3: Chelsea Finn |
| 11:55 am - 12:10 pm | Q&A |
| 12:10 pm - 1:00 pm | LUNCH BREAK |
| 1:00 pm - 1:30 pm | Keynote 4: Susan Athey |
| 1:30 pm - 1:45 pm | Q&A |
| 1:45 pm - 2:00 pm | BREAK |
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| 2:00 pm - 3:00 pm | Poster Session I |
ROOM # 1
| Cluster 1: Fairness, Explainable ML, Privacy, and Robustness |
| Neural Additive Models: Interpretable Machine Learning with Neural Nets |
| siVAE: interpreting latent dimensions within variational autoencoders |
| Selectivity considered harmful: evaluating the causal impact of class selectivity in DNNs |
| Synthetic Health Data for Fostering Reproducibility of Private Research Studies |
| Adversarial Learning for Debiasing Knowledge Base Embeddings |
| Robustness Analysis of Deep Learning via Implicit Models |
ROOM # 2
| Cluster 2: Computer Vision |
| Protecting Against Image Translation Deepfakes by Leaking Universal Perturbations from Black-Box Neural Networks |
| Anatomy of Catastrophic Forgetting: HiddenRepresentations and Task Semantics |
| CoCon: Cooperative-Contrastive Learning |
| Can Neural Networks Learn Non-Verbal Reasoning? |
| Modality-Agnostic Attention Fusion for visual search with text feedback |
ROOM # 3
| Cluster 3: Deep Learning |
| Revisiting Spatial Invariance with Low-Rank Local Connectivity |
| What is being transferred in transfer learning? |
| Neural Anisotropy Directions |
| Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration |
| What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation |
ROOM # 4
| Cluster 4: ML Methods and Tools |
| Bandit-based Monte Carlo Optimization for Nearest Neighbors |
| LassoNet: A Neural Network with Feature Sparsity |
| Temperature check: theory and practice for training models with softmax-cross-entropy losses |
| Meta-Learning Requires Meta-Augmentation |
| Energy-based View of Retrosynthesis |
ROOM # 5
| Cluster 5: Reinforcement Learning |
| Learning to grow: control of materials self-assembly using evolutionary reinforcement learning |
| Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization |
| Provably Efficient Policy Optimization via Thompson Sampling |
| Uncovering Task Clusters in Multi-Task Reinforcement Learning |
| Curriculum and Decentralized Learning in Google Research Football |
| 3:00 pm - 4:00 pm | Poster Session II |
ROOM # 5
| Cluster 6: Bayesian Learning and Uncertainty |
| Autofocused oracles for design |
| Exact posteriors of wide Bayesian neural networks |
| Deep Ensembles: a loss landscape perspective |
| Active Online Domain Adaptation |
| TSGLR: an Adaptive Thompson Sampling for the Switching Multi-Armed Bandit Problem |
| ROOM # 2 | Cluster 7: Computer Vision and Robotics |
| Interpretable Planning-Aware Representations for Multi-Agent Trajectory Forecasting |
| Learning Mixed-Integer Convex Optimization Strategies for Robot Planning and Control |
| Beyond Supervision for Monocular Depth Estimation |
| A Synthetic Data Petri Dish for Studying Mode Collapse in GANs |
| Attention-Sampling Graph Convolutional Networks |
| Towards Learning Robots Which Adapt On The Fly |
ROOM # 4
| Cluster 8: Deep ML and other topics |
| Simultaneous Learning of the Inputs and Parameters in Neural Collaborative Filtering |
| A Deep Learning Pipeline for Patient Diagnosis Prediction Using Electronic Health Records |
| Ads Clickthrough Rate Prediction Models For Multi-Datasource Tasks |
| Neural Interventional GRU-ODEs |
ROOM # 3
| Cluster 9: Optimization |
| VP-FO: A Variable Projection Method for Training Neural Networks |
| Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning |
| Neural Representations in Hybrid Recommender Systems: Prediction versus Regularization |
| ECLIPSE: An Extreme-Scale Linear Program Solver for Web-Applications |
| ROOM # 1 | Cluster 10: Reinforcement Learning |
| Safety Aware Reinforcement Learning (SARL) |
| Meta Attention Networks: Meta Learning Attention to Modulate Information Between Sparsely Interacting Recurrent Modules |
| Batch Reinforcement Learning Through Continuation Method |
| See, Hear, Explore: Curiosity via Audio-Visual Association |
| 4:00 pm - 5:00 pm | Poster Session III |
ROOM # 4
| Cluster 11: Natural Language Processing |
| Automated Utterance Generation |
| Entity Skeletons as Intermediate Representations for Visual Storytelling |
| Learning to reason by learning on rationales |
| MUFASA: Multimodal Fusion Architecture Search for Electronic Health Records |
| VirAAL: Virtual Adversarial Active Learning |
| ChemBERTa: Utilizing Transformer-Based Attention for Understanding Chemistry |
ROOM # 5
| Cluster 12: On-Device ML and Human-Computer Interaction |
| GANs for Continuous Path Keyboard Input Modeling |
| Architecture Compression |
| A flexible, extensible software framework for model compression based on the LC algorithm |
| Rotation-Invariant Gait Identification with Quaternion Convolutional Neural Networks |
| ROOM # 2 | Cluster 13: Large-Scale Learning |
| Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization |
| Self-supervised Learning for Deep Models in Recommendations |
| Learning Multi-granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems |
| Distributed Sketching Methods for Privacy Preserving Regression |
| Hamming Space Locality Preserving Neural Hashing for Similarity Search |
ROOM # 3
| Cluster 14: Optimization |
| Exact Polynomial-time Convex Optimization Formulations for Two-Layer ReLU Networks |
| DisARM: An Antithetic Gradient Estimator for Binary Latent Variables |
| Boosted Sparse Oblique Decision Trees |
| Whitening and second order optimization both destroy information about the dataset, and can make generalization impossible |
