[AISTATS Logo]
AI & Statistics 2015

AISTATS 2015 Oral Sessions

Oral Session 1, May 10 (Sunday) 10:00 - 12:00

Session Chair: Joonseok Lee

Computational Complexity of Linear Large Margin Classification With Ramp Loss
Søren Frejstrup Maibing, Christian Igel

A la Carte -- Learning Fast Kernels
Zichao Yang, Andrew Wilson, Alex Smola, Le Song

A Scalable Algorithm for Structured Kernel Feature Selection
Shaogang Ren, Shuai Huang, John Onofrey, Xenios Papademetris, Xiaoning Qian

Learning Where to Sample in Structured Prediction
Tianlin Shi, Jacob Steinhardt, Percy Liang

Oral Session 2, May 10 (Sunday) 14:00 - 16:00

Session Chair: Swaminathan Vishwanathan

Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning
Mario Lucic, Mesrob Ohannessian, Amin Karbasi, Andreas Krause

Averaged Least-Mean-Squares: Bias-Variance Trade-offs and Optimal Sampling Distributions
Alexandre Defossez, Francis Bach

Sparsistency of \ell_1-Regularized M-Estimators
Yen-Huan Li, Jonathan Scarlett, Pradeep Ravikumar, Volkan Cevher

Two-stage sampled learning theory on distributions
Zoltan Szabo, Arthur Gretton, Barnabas Poczos, Bharath Sriperumbudur

Oral Session 3, May 10 (Sunday) 16:30 - 18:30

Session Chair: Guy Lebanon

Generalized Linear Models for Aggregated Data
Avradeep Bhowmik, Joydeep Ghosh, Oluwasanmi Koyejo

Efficient Estimation of Mutual Information for Strongly Dependent Variables
Shuyang Gao, Greg Ver Steeg, Aram Galstyan

Understanding and Evaluating Sparse Linear Discriminant Analysis
Yi Wu, David Wipf, Jeong-Min Yun

Back to the Past: Source Identification in Diffusion Networks from Partially Observed Cascades
Mehrdad Farajtabar, Manuel Gomez Rodriguez, Mohammad Zamani, Nan Du, Hongyuan Zha, Le Song

Oral Session 4, May 11 (Monday) 10:00 - 12:00

Session Chair: Houssam Nassif

Sparse Submodular Probabilistic PCA
Rajiv Khanna, Joydeep Ghosh, Russell Poldrack, Oluwasanmi Koyejo

Unifying Local Consistency and MAX SAT Relaxations for Scalable Inference with Rounding Guarantees
Stephen Bach, Bert Huang, Lise Getoor

Low-Rank Spectral Learning with Weighted Loss Functions
Alex Kulesza, Nan Jiang, Satinder Singh

A Spectral Algorithm for Inference in Hidden semi-Markov Models
Igor Melnyk, Arindam Banerjee

Oral Session 5, May 11 (Monday) 14:00 - 16:00

Session Chair: Houssam Nassif

Stochastic Spectral Descent for Restricted Boltzmann Machines
David Carlson, Volkan Cevher, Lawrence Carin

Sparse Dueling Bandits
Kevin Jamieson, Sumeet Katariya, Atul Deshpande, Robert Nowak

Online Ranking with Top-1 Feedback
Sougata Chaudhuri, Ambuj Tewari

Fast Function to Function Regression
Junier Oliva, William Neiswanger, Barnabas Poczos, Eric Xing, Hy Trac, Shirley Ho, Jeff Schneider

Oral Session 6, May 11 (Monday) 16:30 - 18:30

Session Chair: Swaminathan Vishwanathan

The Security of Latent Dirichlet Allocation
Shike Mei, Xiaojin Zhu

Modeling Skill Acquisition Over Time with Sequence and Topic Modeling
José González-Brenes

A Dirichlet Process Mixture Model for Spherical Data
Julian Straub, Jason Chang, Oren Freifeld, John Fisher III

Tensor Factorization via Matrix Factorization
Volodymyr Kuleshov, Arun Chaganty, Percy Liang

Oral Session 7, May 12 (Tuesday) 10:00 - 11:30

Session Chair: Joonseok Lee

Exact Bayesian Learning of Ancestor Relations in Bayesian Networks
Yetian Chen, Lingjian Meng, Jin Tian

Parameter Estimation of Generalized Linear Models without Assuming their Link Function
Sreangsu Acharyya, Joydeep Ghosh

Stochastic Block Transition Models for Dynamic Networks
Kevin Xu