[Artificial Intelligence and Statistics Logo] Artificial Intelligence and Statistics 2012

[edit]

AISTATS*2012 Conference Schedule

Thursday 19 April

16:00 - 21:00 Registration

Friday 20 April (AISTATS/MLSS Joint Tutorial Day)

07:45 - 09:00 Breakfast and Registration
Tutorials Jointly with MLSS
09:00 - 11:00 Nonparametric Bayesian Modelling
Zoubin Ghahramani
11:30 - 13:30 Probabilistic decision-making, data analysis, and discovery in astronomy
David W. Hogg

13:30 - 16:30 Lunch
16:30 - 19:30 Poster Session for MLSS Students

Saturday 21 April

07:45 - 08:45 Breakfast
08:45 - 09:00 Welcome
AISTATS Organizers
Invited Talk (Chair: Mark Girolami)
09:00 - 10:00 Detection of correlations in high dimension
Gábor Lugosi
Kernel Methods (Chair: Philipp Hennig)
10:00 - 10:25 Fast Learning Rate of Multiple Kernel Learning: Trade-Off between Sparsity and Smoothness
Taiji Suzuki and Masashi Sugiyama
10:25 - 10:50 Data dependent kernels in nearly-linear time
Guy Lever, Tom Diethe and John Shawe-Taylor

11:00 - 13:00 Coffee Break and Poster Session I
13:00 - 17:00 Lunch
Bayesian Inference (Chair: Zoubin Ghahramani)
17:00 - 17:25 Factorized Asymptotic Bayesian Inference for Mixture Modeling
Ryohei Fujimaki and Satoshi Morinaga
17:25 - 17:50 Adaptive MCMC with Bayesian Optimization
Nimalan Mahendran, Ziyu Wang, Firas Hamze and Nando de Freitas
17:50 - 18:15 Evaluation of marginal likelihoods via the density of states
Michael Habeck

18:15 - 18:45 Tea Break
Structure Learning and Sparsity (Chair: Jennifer Dy)
18:45 - 19:10 Lightning-speed Structure Learning of Nonlinear Continuous Networks
Gal Elidan
19:10 - 19:35 High-dimensional Sparse Inverse Covariance Estimation using Greedy Methods
Christopher Johnson, Ali Jalali, and Pradeep Ravikumar
19:35 - 20:00 Learning Fourier Sparse Set Functions
Peter Stobbe and Andreas Krause

20:00 - 22:00 Conference Banquet

Sunday 22 April

07:45 - 09:00 Breakfast
Invited Talk (Chair: Neil Lawrence)
09:00 - 10:00 Patterns, Predictions and Personalised Medicine
Sir Gordon Duff
Scientific Computing & Speed Ups (Chair: Felix Agakov)
10:00 - 10:25 A Bayesian Analysis of the Radioactive Releases of Fukushima
Ryota Tomioka and Morten Mørup
10:25 - 10:50 Using More Data to Speed-up Training Time
Shai Shalev-Shwartz, Ohad Shamir and Eran Tromer

11:00 - 13:00 Coffee Break and Poster Session II
13:00 - 17:00 Lunch
Clustering & Learning Theory (Chair: Shai Ben-David)
17:00 - 17:25 Online Clustering with Experts
Anna Choromanska and Claire Monteleoni
17:25 - 17:50 Maximum Margin Temporal Clustering
Minh Hoai and Fernando De la Torre
17:50 - 18:15 Minimax rates for homology inference
Sivaraman Balakrishnan, Alesandro Rinaldo, Don Sheehy, Aarti Singh and Larry Wasserman

18:15 - 18:45 Tea Break
Feature Extraction and Bandits (Chair: Amos Storkey)
18:45 - 19:10 Online Incremental Feature Learning with Denoising Autoencoders
Guanyu Zhou, Kihyuk Sohn and Honglak Lee
19:10 - 19:35 Classifier Cascade for Minimizing Feature Evaluation Cost
Minmin Chen, Zhixiang Xu, Kilian Weinberger, Olivier Chapelle and Dor Kedem
19:35 - 20:00 Online-to-Confidence-Set Conversions and Application to Sparse Stochastic Bandits
Yasin Abbasi-Yadkori, David Pal and Csaba Szepesvari

Monday 23 April

07:45 - 09:00 Breakfast
Invited Talk (Chair: Bernhard Schölkopf)
09:00 - 10:00 Alpha, Betti and the Megaparsec Universe: Topology of the Cosmic Web
Rien van de Weygaert
Sparse Analysis (Chair: Dirk Husmeier)
10:00 - 10:25 Minimax Rates of Estimation for Sparse PCA in High Dimensions
Vincent Vu and Jing Lei
10:25 - 10:50 Structured Sparse Canonical Correlation Analysis
Xi Chen, Liu Han and Jaime Carbonell

11:00 - 13:00 Coffee Break and Poster Session III
13:00 - 17:00 Lunch
Multitask, Multiparty and Multilabels (Chair: Miguel Carreira-Perpinan)
17:00 - 17:25 Marginal Regression For Multitask Learning
Mladen Kolar and Han Liu
17:25 - 17:50 A Differentially Private Stochastic Gradient Descent Algorithm for Multiparty Classification
Arun Rajkumar and Shivani Agarwal
17:50 - 18:15 CorrLog: Correlated Logistic Models for Joint Prediction of Multiple Labels
Wei Bian, Bo Xie and Dacheng Tao

18:15 - 18:45 Tea Break
Regression Modelling (Chair: John Winn)
18:45 - 19:10 Hierarchical Latent Dictionaries for Models of Brain Activation
Alona Fyshe, Emily Fox, David Dunson and Tom Mitchell
19:10 - 19:35 Efficient Gaussian Process Inference for Short-Scale Spatio-Temporal Modeling
Jaakko Luttinen and Alexander Ilin
19:35 - 20:00 Regression for sets of polynomial equations
Franz Király, Paul von Büenau, Jan Müller, Duncan Blythe, Frank Meinecke, and Klaus-Robert Müller
This site last compiled Mon, 10 Aug 2020 23:11:17 +0000
Github Account Copyright © AISTATS 2020. All rights reserved.