See accepted papers for all details on the invited talks and papers.

 08:30 - 08:40 Welcome
 08:40 - 09:20 Invited talk: Hugo Larochelle (Google) Generalizing from Few Examples with Meta-Learning
 09:20 - 09:40 Contributed talk: Kirthevasan Kandasamy Thompson Sampling for Asynchronous Parallel Bayesian Optimisation
 09:40 - 10:00 Contributed talk: Tongzhou Wang, Yi Wu, Dave Moore and Stuart Russell Neural Block Sampling
 10:00 - 10:30 Coffee Break
 10:30 - 11:10 Invited Talk: Rob DeLine (Microsoft) Machine Learning for Makers
 11:10 - 11:30 Poster spotlights: - Neural Optimizers with Hypergradients for Tuning Parameter-Wise Learning Rates. Jie Fu, Ritchie Ng, Danlu Chen, Ilija Ilievski, Christopher Pal and Tat-Seng Chua - Towards Automated Bayesian Optimization. Gustavo Malkomes and Roman Garnett - Bayesian Multi-Hyperplane Machine. Khanh Nguyen, Trung Le, Tu Dinh Nguyen and Dinh Phung - Automatic Selection of t-SNE Perplexity. Yanshuai Cao and Luyu Wang - Promoting Diversity in Random Hyperparameter Search using Determinantal Point Processes. Jesse Dodge, Catriona Anderson and Noah A. Smith - Dealing with Integer-valued Variables in Bayesian Optimization with Gaussian Processes. Eduardo César Garrido Merchán and Daniel Hernández Lobato - An Automated Fast Learning Algorithm and its Hyperparameters Selection by Reinforcement Learning. Valeria Efimova, Andrey Filchenkov and Viacheslav Shalamov
 11:30 - 12:00 Poster session 1
 12:00 - 14:00 Lunch Break
 14:00 - 14:40 Invited Talk: Himabindu Lakkaraju (Stanford) Discovering Blind Spots of Predictive Models: Representations and Policies for Guided Exploration
 14:40 - 15:00 Contributed talk: Jian Zhang, Ioannis Mitliagkas and Christopher Re YellowFin and the Art of Momentum Tuning
 15:00 - 15:30 Coffee Break
 15:30 - 15:50 Poster spotlights: - Automating Stochastic Optimization with Gradient Variance Estimates. Lukas Balles, Maren Mahsereci and Philipp Hennig - Hyperparameter Learning for Kernel Embedding Classifiers with Rademacher complexity bounds. Yuan-Shuo Kelvin Hsu, Richard Nock and Fabio Ra
- Improving Gibbs Sampler Scan Quality with DoGS. Ioannis Mitliagkas and Lester Mackey
- NDSE: Method for Classification Instance Generation Given Meta-Feature Description. Alexey Zabashta and Andrey Filchenkov
- NEMO: Neuro-Evolution with Multiobjective Optimization of Deep Neural Network for Speed and Accuracy
 Ye-Hoon Kim, Bhargava Reddy, Sojung Yun and Chanwon Seo
- Building and Evaluating Interpretable Models using Symbolic Regression and Generalized Additive Models. Khaled Sharif
- Dynamic Input Structure and Network Assembly for Few-Shot Learning. Nathan Hilliard, Nathan Hodas and Courtney Corley
- Domain specific induction for data wrangling automation (Demo). Lidia Contreras-Ochando, Cèsar Ferri, José Hernández-Orallo, Fernando Martínez-Plumed, María José Ramírez-Quintana and Susumu Katayama
 15:50 - 16:30 Poster session 2
16:30 - 17:30 Community Discussion Post topics here: