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AISTATS 2019

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