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Schedule

Information for presenters

Each presenter has 20 minutes: 16-minute presentation and 4-minute Q&A session.

All oral presentations also have a poster presentation slot. Poster boards are 0.90m (wide) x 2.10m (high). We recommend A0 portrait as the poster size.

Registration desk hours

• Sunday April 8: 17:00 to 20:00
• Monday April 9: 7:30 to 13:30
• Tuesday April 10: 7:30 to 13:30
• Wednesday April 11: 7:30 to 10:30

April 9 (Monday)

Time	Schedule
9:00 - 10:00	Invited speaker: Jennifer Hill
10:10 - 11:30	Oral Session 1.1: Statistics Session chair: Dirk Husmeier Statistically Efficient Estimation for Non-Smooth Probability Densities Masaaki Imaizumi, Takanori Maehara, Yuichi Yoshida Stochastic Zeroth-order Optimization in High Dimensions Yining Wang, Arindam Banerjee, Simon Du, Sivaraman Balakrishnan, Aarti Singh Sparse Linear Isotonic Models Sheng Chen, Arindam Banerjee Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs Lawrence Murray, Daniel Lundén, Jan Kudlicka, David Broman, Thomas Schön
11:30 - 14:00	Poster session 1
14:00 - 15:30	Lunch (on your own)
15:30 - 16:50	Oral Session 1.2: Online learning Session chair: Mark Deisenroth Combinatorial Semi-Bandits with Knapsacks Karthik Abinav Sankararaman, Aleksandrs Slivkins Online Continuous Submodular Maximization Lin Chen, Hamed Hassani, Amin Karbasi Convergence of Value Aggregation for Imitation Learning Ching-An Cheng, Byron Boots Competing with Automata-based Expert Sequences Scott Yang, Mehryar Mohri
16:50 - 17:20	Coffee break
17:20 - 18:40	Oral Session 1.3: Learning and Estimation Session chair: Isabel Valera Martinez A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer Tianbao Yang, Zhe Li, Lijun Zhang Learning linear structural equation models in polynomial time and sample complexity Asish Ghoshal, Jean Honorio Consistent Algorithms for Classification under Complex Losses and Constraints Harikrishna Narasimhan Subsampling for Ridge Regression via Regularized Volume Sampling Michal Derezinski, Manfred Warmuth
19:30	Welcome reception in the Canary (leave at bottom of building, turn right at pool: building near the end of the pool).

April 10 (Tuesday)

Time	Schedule
9:00 - 10:00	Invited speaker: David Blei
10:10 - 11:30	Oral Session 2.1: Bayesian Methods Session chair: Barnabas Poczos Fast Threshold Tests for Detecting Discrimination Emma Pierson, Sam Corbett-Davies, Sharad Goel Parallelised Bayesian Optimisation via Thompson Sampling Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition Pavel Izmailov, Dmitry Kropotov, Alexander Novikov Factorial HMM with Collapsed Gibbs Sampling for optimizing long-term HIV Therapy Amit Gruber, Chen Yanover, Tal El-Hay, Yaara Goldschmidt, Anders Sönnerborg, Vanni Borghi, Francesca Incardona
11:30 - 14:00	Poster session 2
14:00 - 15:30	Lunch (on your own)
15:30 - 16:30	Oral Session 2.2: Large Scale learning Session chair: Adrian Weller Sketching for Kronecker Product Regression and P-splines Huaian Diao, Zhao Song, Wen Sun, David Woodruff Towards Provable Learning of Polynomial Neural Networks Using Low-Rank Matrix Estimation Mohammadreza Soltani, Chinmay Hegde Convergence diagnostics for stochastic gradient descent Jerry Chee, Panos Toulis
16:30 - 19:00	Poster session 3
19:30	Conference Dinner at Monumento al Campesino: Bus leaves at 7.30 from the front of the Hotel.

April 11 (Wednesday)

Time	Schedule
9:00 - 10:00	Invited speaker: Andreas Krause
10:10 - 11:30	Oral Session 3.1: Approximate Inference Session chair: Matt Hoffman Variational Sequential Monte Carlo Christian Naesseth, Scott Linderman, Rajesh Ranganath, David Blei VAE with a VampPrior Jakub Tomczak, Max Welling Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes Hyunjik Kim, Yee Whye Teh Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models Ardavan Saeedi, Matthew Hoffman, Matthew Hoffman, Stephen DiVerdi, Asma Ghandeharioun, Matthew Johnson, Ryan Adams
11:30 - 14:00	Poster session 4
14:00 - 15:30	Lunch (on your own)
15:30 - 16:30	Oral Session 3.2: Kernel Methods Session chair: Andrew Gordon Wilson Random Warping Series: A Random Features Method for Time-Series Embedding Lingfei Wu, Ian En-Hsu Yen, Jinfeng Yi, Fangli Xu, Qi Lei, Michael Witbrock Efficient and principled score estimation with Nyström kernel exponential families Dougal Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton Multi-scale Nystrom Method Woosang Lim, Rundong Du, Bo Dai, Kyomin Jung, Le Song, Haesun Park
16:30 - 17:00	Coffee break
17:00 - 18:40	Oral Session 3.3: Optimization Session chair: Simon Lacoste-Julien Batch-Expansion Training: An Efficient Optimization Framework Michal Derezinski, Dhruv Mahajan, Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems Sai Praneeth Reddy Karimireddy, Sebastian Stich, Martin Jaggi Frank-Wolfe Splitting via Augmented Lagrangian Method Gauthier Gidel, Fabian Pedregosa, Simon Lacoste-Julien, Structured Optimal Transport David Alvarez Melis, Tommi Jaakkola, Stefanie Jegelka Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods Robert Gower, Nicolas Le Roux, Francis Bach