| Room | A4 | A5 | A6 | ||
| Wed | 11:00-12:00 | Unsupervised Learning 1 | Structured Prediction 1 | Statistical Learning Theory 1 | |
| 13:30-14:30 | Gaussian Processes 1 | Sparsity and Compressed Sensing 1 | Statistical Learning Theory 2 | ||
| 14:30-15:30 | Deep Learning (Bayesian) 1 | Ranking and Preference Learning 1 | Statistical Learning Theory 3 | ||
| 16:00-17:00 | Approximate Inference 1 | Networks and Relational Learning 1 | Privacy, Anonymity, and Security 1 | ||
| 17:00-18:00 | Approximate Inference 2 | Networks and Relational Learning 2 | Society Impacts of Machine Learning 1 | ||
| Thu | 11:00-12:00 | Gaussian Processes 2 | Structured Prediction 2 | Privacy, Anonymity, and Security 2 | |
| 13:30-14:30 | Monte Carlo Methods 1 | Ranking and Preference Learning 2 | Supervised Learning 1 | ||
| 14:30-15:30 | Graphical Models 1 | Online Learning 1 | Supervised Learning 2 | ||
| 16:00-17:00 | Deep Learning (Bayesian) 2 | Online Learning 2 | Statistical Learning Theory 4 | ||
| 17:00-18:00 | Deep Learning (Bayesian) 3 | Online Learning 3 | Other Models and Methods 1 | ||
| Fri | 9:30-10:30 | Graphical Models 2 | Online Learning 4 | Society Impacts of Machine Learning 2 | |
| 11:00-12:00 | Gaussian Processes 3 | Online Learning 5 | Unsupervised Learning 2 | ||
| 16:00-17:00 | Monte Carlo Methods 2 | Causal Inference 1 | Supervised Learning 3 | ||
| 17:00-18:00 | Approximate Inference 3 | Causal Inference 2 | Statistical Learning Theory 5 | ||
| Room | K11 | A7 | A1 | ||
| Wed | 11:00-12:00 | Clustering 1 | Representation Learning 1 | Reinforcement Learning 1 | |
| 13:30-14:30 | Dimensionality Reduction 1 | Representation Learning 2 | Reinforcement Learning 2 | ||
| 14:30-15:30 | Sparsity and Compressed Sensing 2 | Representation Learning 3 | Reinforcement Learning 3 | ||
| 16:00-17:00 | Optimization (Combinatorial) 1 | Generative Models 1 | Reinforcement Learning 4 | ||
| 17:00-18:00 | Optimization (Combinatorial) 2 | Deep Learning (Adversarial) 1 | Reinforcement Learning 6 | ||
| Thu | 11:00-12:00 | Matrix Factorization 1 | Generative Models 2 | Reinforcement Learning 8 | |
| 13:30-14:30 | Large Scale Learning and Big Data 1 | Deep Learning (Adversarial) 2 | Reinforcement Learning 9 | ||
| 14:30-15:30 | Large Scale Learning and Big Data 2 | Deep Learning (Adversarial) 3 | Reinforcement Learning 10 | ||
| 16:00-17:00 | Optimization (Combinatorial) 3 | Deep Learning (Adversarial) 4 | Reinforcement Learning 11 | ||
| 17:00-18:00 | Dimensionality Reduction 2 | Deep Learning (Adversarial) 5 | Reinforcement Learning 12 | ||
| Fri | 9:30-10:30 | Dimensionality Reduction 3 | Deep Learning (Adversarial) 6 | Reinforcement Learning 14 | |
| 11:00-12:00 | Optimization (Combinatorial) 4 | Generative Models 3 | Reinforcement Learning 15 | ||
| 16:00-17:00 | Spectral Methods 1 | Generative Models 4 | Reinforcement Learning 16 | ||
| 17:00-18:00 | Matrix Factorization 2 | Generative Models 5 | Reinforcement Learning 17 | ||
| Room | A3 | A9 | Victoria | K1+K2 (day1), K1 (day2) | |
| Wed | 11:00-12:00 | Transfer and Multi-Task Learning 1 | Parallel and Distributed Learning 1 | Deep Learning (Neural Network Architectures) 1 | Feature Selection 1 |
| 13:30-14:30 | Optimization (Bayesian) 1 | Optimization (Non-convex) 1 | Deep Learning (Neural Network Architectures) 2 | Other Applications 1 | |
| 14:30-15:30 | Active Learning 1 | Optimization (Non-convex) 2 | Deep Learning (Neural Network Architectures) 3 | Computer Vision 1 | |
| 16:00-17:00 | Reinforcement Learning 5 | Optimization (Convex) 1 | Deep Learning (Neural Network Architectures) 4 | Deep Learning (Theory) 1 | |
| 17:00-18:00 | Reinforcement Learning 7 | Optimization (Convex) 2 | Deep Learning (Neural Network Architectures) 5 | Deep Learning (Theory) 2 | |
| Thu | 11:00-12:00 | Multi-Agent Learning 1 | Parallel and Distributed Learning 2 | Deep Learning (Neural Network Architectures) 6 | Other Applications 2 |
| 13:30-14:30 | Optimization (Bayesian) 2 | Optimization (Convex) 3 | Deep Learning (Neural Network Architectures) 7 | Deep Learning (Theory) 3 | |
| 14:30-15:30 | Kernel Methods 1 | Optimization (Convex) 4 | Deep Learning (Neural Network Architectures) 8 | Deep Learning (Theory) 4 | |
| 16:00-17:00 | Natural Language and Speech Processing 1 | Optimization (Non-convex) 3 | Deep Learning (Neural Network Architectures) 9 | Deep Learning (Theory) 5 | |
| 17:00-18:00 | Reinforcement Learning 13 | Optimization (Non-convex) 4 | Deep Learning (Neural Network Architectures) 10 | Transfer and Multi-Task Learning 2 | |
| Fri | 9:30-10:30 | Time-Series Analysis 1 | Optimization (Non-convex) 5 | Other Models and Methods 2 | Computer Vision 2 |
| 11:00-12:00 | Transfer and Multi-Task Learning 3 | Optimization (Convex) 5 | Deep Learning (Neural Network Architectures) 11 | Deep Learning (Theory) 6 | |
| 16:00-17:00 | Parallel and Distributed Learning 3 | Optimization (Convex) 6 | Deep Learning (Neural Network Architectures) 12 | Deep Learning (Theory) 7 | |
| 17:00-18:00 | Natural Language and Speech Processing 2 | Optimization (Convex) 7 | Deep Learning (Neural Network Architectures) 13 | Deep Learning (Theory) 8 |