Accepted Papers

  1. Rates of Convergence of Nonparametric Estimators for Model Shift. (poster)
    Simon Du, Jayanth Koushik, Aarti Singh, Barnabás Póczos.
    [paper, poster]

  2. Post Selection Inference with Kernels. (oral + poster)
    Makoto Yamada, Yuta Umezu, Kenji Fukumizu, Ichiro Takeuchi.
    [paper, poster, slides]

  3. Generalization Properties of Learning with Random Features. (oral + poster)
    Alessandro Rudi, Raffaello Camoriano, Lorenzo Rosasco.
    [paper, poster, slides]

  4. Do Nonparametric Two-sample Tests work for Small Sample Size? A Study on Random Graphs. (oral + poster)
    Debarghya Ghoshdastidar, Ulrike von Luxburg.
    [paper, poster: not disseminated (author request)]

  5. Robust non-parametric mode clustering. (poster)
    Jonas Nordhaug Myhre, Karl Øyvind Mikalsen, Sigurd Løkse, Robert Jenssen.
    [paper]

  6. Issues with hyperparameter resampling in Pitman-Yor processes. (poster)
    Kevin Löser, Alexandre Allauzen.
    [paper]

  7. A Gaussian Process Model for Non-Convex Probabilistic Clustering. (poster)
    Daniel Andrade, Kenji Fukumizu.
    [paper, poster]

  8. Multiple kernel learning with elastic-net constraints. (poster)
    Luca Citi.
    [paper, poster]

  9. Scaling Bayesian Nonparametric Factor Analysis for Neuroimaging. (poster)
    Matthew Pearce, Simon White.
    [paper, poster]

  10. Pack only the essentials: Adaptive dictionary learning for kernel ridge regression. (poster)
    Daniele Calandriello, Michal Valko, Alessandro Lazaric.
    [paper, poster]

  11. Solving the Linear Bellman Equation via Kernel Embeddings and Stochastic Gradient Descent. (oral + poster)
    Yunpeng Pan, Xinyan Yan, Bo Dai, Le Song, Evangelos Theodorou, Byron Boots.
    [paper]

  12. Finding Combinations of Binary Variables with Guaranteed Accuracy. (poster)
    Yoshito Baba, Mahito Sugiyama, Takashi Washio.
    [paper, poster]

  13. Adaptivity and Computation-Statistics Tradeoffs in High-Dimensional Two Sample Testing. (poster)
    Aaditya Ramdas, Sashank Reddi, Barnabás Póczos, Aarti Singh, Larry Wasserman.
    [paper]

  14. Adapting to Drifting Preferences in Recommendation. (poster)
    Fei Mi, Boi Faltings.
    [paper, poster]

  15. Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers. (poster)
    Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Ryan Tibshirani.
    [paper, poster]

  16. Incremental Variational Sparse Gaussian Process Regression. (poster)
    Ching-An Cheng, Byron Boots.
    [paper]

  17. Paintboxes and probability functions for edge-exchangeable graphs. (oral + poster)
    Diana Cai, Trevor Campbell, Tamara Broderick.
    [paper]

  18. AdaNet: Adaptive Structural Learning of Artifical Neural Networks (poster)
    Corinna Cortes, Xavi Gonzalvo, Vitaly Kuznetsov, Mehryar Mohri, Scott Yang.
    [paper, poster]