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ORCID16th AISTATS 2013: Scottsdale, AZ, USA
- Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2013, Scottsdale, AZ, USA, April 29 - May 1, 2013. JMLR Workshop and Conference Proceedings 31, JMLR.org 2013
Part I: Notable Papers
- Anjishnu Banerjee, Jared Murray
, David B. Dunson:
Bayesian learning of joint distributions of objects. 1-9 - Olivier Collier, Arnak S. Dalalyan:
Permutation estimation and minimax rates of identifiability. 10-19 - Nicholas J. Foti, Joseph D. Futoma, Daniel N. Rockmore, Sinead Williamson:
A unifying representation for a class of dependent random measures. 20-28 - James E. Johndrow, David B. Dunson, Kristian Lum:
Diagonal Orthant Multinomial Probit Models. 29-38 - Zhaoshi Meng, Dennis L. Wei, Ami Wiesel, Alfred O. Hero III:
Distributed Learning of Gaussian Graphical Models via Marginal Likelihoods. 39-47 - Zhaoran Wang, Fang Han, Han Liu:
Sparse Principal Component Analysis for High Dimensional Multivariate Time Series. 48-56
Part II: Regular Papers
- Jayadev Acharya, Ashkan Jafarpour, Alon Orlitsky, Ananda Theertha Suresh:
A Competitive Test for Uniformity of Monotone Distributions. 57-65 - Margareta Ackerman, Shai Ben-David, David Loker, Sivan Sabato:
Clustering Oligarchies. 66-74 - Andrej Aderhold, Dirk Husmeier, V. Anne Smith:
Reconstructing ecological networks with hierarchical Bayesian regression and Mondrian processes. 75-84 - Raja Hafiz Affandi, Alex Kulesza, Emily B. Fox, Ben Taskar:
Nystrom Approximation for Large-Scale Determinantal Processes. 85-98 - Shipra Agrawal, Navin Goyal:
Further Optimal Regret Bounds for Thompson Sampling. 99-107 - Sungjin Ahn, Yutian Chen, Max Welling:
Distributed and Adaptive Darting Monte Carlo through Regenerations. 108-116 - Raman Arora, Marina Meila:
Consensus Ranking with Signed Permutations. 117-125 - Krishnakumar Balasubramanian, Bharath K. Sriperumbudur, Guy Lebanon:
Ultrahigh Dimensional Feature Screening via RKHS Embeddings. 126-134 - Elias Bareinboim, Judea Pearl:
Meta-Transportability of Causal Effects: A Formal Approach. 135-143 - Guillaume Bouchard, Dawei Yin, Shengbo Guo:
Convex Collective Matrix Factorization. 144-152 - Arun Tejasvi Chaganty, Aditya V. Nori, Sriram K. Rajamani:
Efficiently Sampling Probabilistic Programs via Program Analysis. 153-160 - Chao Chen, Vladimir Kolmogorov, Yan Zhu, Dimitris N. Metaxas, Christoph H. Lampert:
Computing the M Most Probable Modes of a Graphical Model. 161-169 - Eunice Yuh-Jie Chen, Judea Pearl:
A simple criterion for controlling selection bias. 170-177 - Yutian Chen, Max Welling:
Evidence Estimation for Bayesian Partially Observed MRFs. 178-186 - Mung Chiang, Henry Lam, Zhenming Liu, H. Vincent Poor:
Why Steiner-tree type algorithms work for community detection. 187-195 - Peter Clifford, Ioana Cosma:
A simple sketching algorithm for entropy estimation over streaming data. 196-206 - Andreas C. Damianou, Neil D. Lawrence:
Deep Gaussian Processes. 207-215 - Frank Dondelinger, Dirk Husmeier, Simon Rogers, Maurizio Filippone:
ODE parameter inference using adaptive gradient matching with Gaussian processes. 216-228 - Nan Du, Le Song, Hyenkyun Woo, Hongyuan Zha:
Uncover Topic-Sensitive Information Diffusion Networks. 229-237 - Christopher DuBois, Carter T. Butts, Padhraic Smyth:
Stochastic blockmodeling of relational event dynamics. 238-246 - Elad Eban, Gideon Rothschild, Adi Mizrahi, Israel Nelken, Gal Elidan:
Dynamic Copula Networks for Modeling Real-valued Time Series. 247-255 - Doris Entner, Patrik O. Hoyer, Peter Spirtes:
Data-driven covariate selection for nonparametric estimation of causal effects. 256-264 - Brian Eriksson:
Learning to Top-K Search using Pairwise Comparisons. 265-273 - Hamed Firouzi, Bala Rajaratnam, Alfred O. Hero III:
Predictive Correlation Screening: Application to Two-stage Predictor Design in High Dimension. 274-288 - Georg M. Goerg, Cosma Rohilla Shalizi:
Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction. 289-297 - Quanquan Gu, Charu C. Aggarwal, Jiawei Han:
Unsupervised Link Selection in Networks. 298-306 - Quanquan Gu, Jiawei Han:
Clustered Support Vector Machines. 307-315 - Abner Guzmán-Rivera, Pushmeet Kohli, Dhruv Batra:
DivMCuts: Faster Training of Structural SVMs with Diverse M-Best Cutting-Planes. 316-324 - Jeffrey Ho, Guang Cheng, Hesamoddin Salehian, Baba C. Vemuri:
Recursive Karcher Expectation Estimators And Geometric Law of Large Numbers. 325-332 - Joyce C. Ho, Yubin Park, Carlos Carvalho, Joydeep Ghosh:
DYNACARE: Dynamic Cardiac Arrest Risk Estimation. 333-341 - Tomoharu Iwata, Neil Houlsby, Zoubin Ghahramani:
Active Learning for Interactive Visualization. 342-350 - Prabhanjan Kambadur, Aurélie C. Lozano:
A Parallel, Block Greedy Method for Sparse Inverse Covariance Estimation for Ultra-high Dimensions. 351-359 - Seungyeon Kim, Fuxin Li, Guy Lebanon, Irfan A. Essa:
Beyond Sentiment: The Manifold of Human Emotions. 360-369 - Janne H. Korhonen, Pekka Parviainen:
Exact Learning of Bounded Tree-width Bayesian Networks. 370-378 - Nevena Lazic, Christopher M. Bishop, John M. Winn:
Structural Expectation Propagation (SEP): Bayesian structure learning for networks with latent variables. 379-387 - Jason D. Lee, Trevor Hastie:
Structure Learning of Mixed Graphical Models. 388-396 - Lei Li, Bharath Ramsundar, Stuart Russell:
Dynamic Scaled Sampling for Deterministic Constraints. 397-405 - Daniel Lowd, Amirmohammad Rooshenas:
Learning Markov Networks With Arithmetic Circuits. 406-414 - Heng Luo, Pierre Luc Carrier, Aaron C. Courville, Yoshua Bengio:
Texture Modeling with Convolutional Spike-and-Slab RBMs and Deep Extensions. 415-423 - Yuancheng Luo, Ramani Duraiswami:
Fast Near-GRID Gaussian Process Regression. 424-432 - Jianzhu Ma, Jian Peng, Sheng Wang, Jinbo Xu:
Estimating the Partition Function of Graphical Models Using Langevin Importance Sampling. 433-441 - Joseph Charles Mellor, Jonathan Shapiro:
Thompson Sampling in Switching Environments with Bayesian Online Change Detection. 442-450 - Edward Moroshko, Koby Crammer:
A Last-Step Regression Algorithm for Non-Stationary Online Learning. 451-462 - Phuong Nguyen, Odalric-Ambrym Maillard, Daniil Ryabko, Ronald Ortner:
Competing with an Infinite Set of Models in Reinforcement Learning. 463-471 - Trung V. Nguyen, Edwin V. Bonilla:
Efficient Variational Inference for Gaussian Process Regression Networks. 472-480 - Zheng Pan, Changshui Zhang:
High-dimensional Inference via Lipschitz Sparsity-Yielding Regularizers. 481-488 - Mijung Park, Oluwasanmi Koyejo, Joydeep Ghosh, Russell A. Poldrack, Jonathan W. Pillow:
Bayesian Structure Learning for Functional Neuroimaging. 489-497 - Saurabh Paul, Christos Boutsidis, Malik Magdon-Ismail, Petros Drineas:
Random Projections for Support Vector Machines. 498-506 - Barnabás Póczos, Aarti Singh, Alessandro Rinaldo, Larry A. Wasserman:
Distribution-Free Distribution Regression. 507-515 - Alexander Rakhlin, Ohad Shamir, Karthik Sridharan:
Localization and Adaptation in Online Learning. 516-526 - James Scott, Jason Baldridge:
A recursive estimate for the predictive likelihood in a topic model. 527-535 - James Sharpnack, Aarti Singh, Akshay Krishnamurthy:
Detecting Activations over Graphs using Spanning Tree Wavelet Bases. 536-544 - James Sharpnack, Aarti Singh, Alessandro Rinaldo:
Changepoint Detection over Graphs with the Spectral Scan Statistic. 545-553 - Mathieu Sinn, Bei Chen:
Central Limit Theorems for Conditional Markov Chains. 554-562 - Greg Ver Steeg, Aram Galstyan:
Statistical Tests for Contagion in Observational Social Network Studies. 563-571 - Nima Taghipour, Daan Fierens, Guy Van den Broeck, Jesse Davis, Hendrik Blockeel:
Completeness Results for Lifted Variable Elimination. 572-580 - Kirill Trapeznikov, Venkatesh Saligrama:
Supervised Sequential Classification Under Budget Constraints. 581-589 - Sebastian Tschiatschek, Franz Pernkopf:
On the Asymptotic Optimality of Maximum Margin Bayesian Networks. 590-598 - Pengyu Wang, Phil Blunsom:
Collapsed Variational Bayesian Inference for Hidden Markov Models. 599-607 - Weiguang Wang, Yingbin Liang, Eric P. Xing:
Block Regularized Lasso for Multivariate Multi-Response Linear Regression. 608-617 - Adrian Weller, Tony Jebara:
Bethe Bounds and Approximating the Global Optimum. 618-631 - Christopher Zach:
Dual Decomposition for Joint Discrete-Continuous Optimization. 632-640 - Ke Zhou, Hongyuan Zha, Le Song:
Learning Social Infectivity in Sparse Low-rank Networks Using Multi-dimensional Hawkes Processes. 641-649 - Tianyi Zhou, Dacheng Tao:
Greedy Bilateral Sketch, Completion & Smoothing. 650-658 - Stéphan Clémençon, Jérémie Jakubowicz:
Scoring anomalies: a M-estimation formulation. 659-667
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