AISTATS 2012

157 papers

Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation

J. Zico Kolter, Tommi Jaakkola

Beta-Negative Binomial Process and Poisson Factor Analysis

Mingyuan Zhou, Lauren Hannah, David Dunson, Lawrence Carin

Online Incremental Feature Learning with Denoising Autoencoders

Guanyu Zhou, Kihyuk Sohn, Honglak Lee

Multi-label Subspace Ensemble

Tianyi Zhou, Dacheng Tao

Sparse Additive Machine

Tuo Zhao, Han Liu

Scaling up Kernel SVM on Limited Resources: A Low-rank Linearization Approach

Kai Zhang, Liang Lan, Zhuang Wang, Fabian Moerchen

An Autoregressive Approach to Nonparametric Hierarchical Dependent Modeling

Zhihua Zhang, Dakan Wang, Edward Chang

A Composite Likelihood View for Multi-Label Classification

Yi Zhang, Jeff Schneider

Efficient Distributed Linear Classification Algorithms via the Alternating Direction Method of Multipliers

Caoxie Zhang, Honglak Lee, Kang Shin

Quilting Stochastic Kronecker Product Graphs to Generate Multiplicative Attribute Graphs

Hyokun Yun, S V N Vishwanathan

Forward Basis Selection for Sparse Approximation over Dictionary

Xiaotong Yuan, Shuicheng Yan

Transductive Learning of Structural SVMs via Prior Knowledge Constraints

Chun-Nam Yu

Perturbation based Large Margin Approach for Ranking

Eunho Yang, Ambuj Tewari, Pradeep Ravikumar

Active Learning from Multiple Knowledge Sources

Yan Yan, Rómer Rosales, Glenn Fung, Faisal Farooq, Bharat Rao

Robust Multi-task Regression with Grossly Corrupted Observations

Huan Xu, Chenlei Leng

Statistical Optimization in High Dimensions

Huan Xu, Constantine Caramanis, Shie Mannor

Primal-Dual methods for sparse constrained matrix completion

Yu Xin, Tommi Jaakkola

Causality with Gates

John Winn

Generalized Optimal Reverse Prediction

Martha White, Dale Schuurmans

Nonlinear low-dimensional regression using auxiliary coordinates

Weiran Wang, Miguel Carreira-Perpinan

A Hybrid Neural Network-Latent Topic Model

Li Wan, Leo Zhu, Rob Fergus

Minimax Rates of Estimation for Sparse PCA in High Dimensions

Vincent Vu, Jing Lei

Bayesian Group Factor Analysis

Seppo Virtanen, Arto Klami, Suleiman Khan, Samuel Kaski

Krylov Subspace Descent for Deep Learning

Oriol Vinyals, Daniel Povey

Learning from Weak Teachers

Ruth Urner, Shai Ben David, Ohad Shamir

A Bayesian Analysis of the Radioactive Releases of Fukushima

Ryota Tomioka, Morten Mrup

Fast Variational Mode-Seeking

Bo Thiesson, Jingu Kim

Randomized Optimum Models for Structured Prediction

Daniel Tarlow, Ryan Adams, Richard Zemel

Structured Output Learning with High Order Loss Functions

Daniel Tarlow, Richard Zemel

Multiresolution Deep Belief Networks

Yichuan Tang, Abdel-rahman Mohamed

Lifted Variable Elimination with Arbitrary Constraints

Nima Taghipour, Daan Fierens, Jesse Davis, Hendrik Blockeel

On Estimation and Selection for Topic Models

Matt Taddy

Fast Learning Rate of Multiple Kernel Learning: Trade-Off between Sparsity and Smoothness

Taiji Suzuki, Masashi Sugiyama

Constrained 1-Spectral Clustering

Syama Sundar Rangapuram, Matthias Hein

Efficient and Exact MAP-MRF Inference using Branch and Bound

Min Sun, Murali Telaprolu, Honglak Lee, Silvio Savarese

Learning Fourier Sparse Set Functions

Peter Stobbe, Andreas Krause

Bayesian Inference for Change Points in Dynamical Systems with Reusable States - a Chinese Restaurant Process Approach

Florian Stimberg, Andreas Ruttor, Manfred Opper

Flexible Martingale Priors for Deep Hierarchies

Jacob Steinhardt, Zoubin Ghahramani

Testing for Membership to the IFRA and the NBU Classes of Distributions

Radhendushka Srivastava, Ping Li, Debasis Sengupta

Consistency and Rates for Clustering with DBSCAN

Bharath Sriperumbudur, Ingo Steinwart

Joint Estimation of Structured Sparsity and Output Structure in Multiple-Output Regression via Inverse-Covariance Regularization

Kyung-ah Sohn, Seyoung Kim

On Nonparametric Guidance for Learning Autoencoder Representations

Jasper Snoek, Ryan Adams, Hugo Larochelle

Low rank continuous-space graphical models

Carl Smith, Frank Wood, Liam Paninski

On Bisubmodular Maximization

Ajit Singh, Andrew Guillory, Jeff Bilmes

Multi-armed Bandit Problems with History

Pannagadatta Shivaswamy, Thorsten Joachims

Complexity of Bethe Approximation

Jinwoo Shin

Sparsistency of the Edge Lasso over Graphs

James Sharpnack, Aarti Singh, Alessandro Rinaldo

Using More Data to Speed-up Training Time

Shai Shalev-shwartz, Ohad Shamir, Eran Tromer

Fast Variational Bayesian Inference for Non-Conjugate Matrix Factorization Models

Matthias Seeger, Guillaume Bouchard

Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression Data

Martin Schiegg, Marion Neumann, Kristian Kersting

Infinite-Dimensional Kalman Filtering Approach to Spatio-Temporal Gaussian Process Regression

Simo Särkkä, Jouni Hartikainen

Age-Layered Expectation Maximization for Parameter Learning in Bayesian Networks

Avneesh Saluja, Priya Krishnan Sundararajan, Ole J Mengshoel

Local Anomaly Detection

Venkatesh Saligrama, Manqi Zhao

Domain Adaptation: A Small Sample Statistical Approach

Ruslan Salakhutdinov, Sham Kakade, Dean Foster

Bernardino Romera Paredes, Andreas Argyriou, Nadia Berthouze, Massimiliano Pontil

Universal Measurement Bounds for Structured Sparse Signal Recovery

Nikhil Rao, Ben Recht, Robert Nowak

A Differentially Private Stochastic Gradient Descent Algorithm for Multiparty Classification

Arun Rajkumar, Shivani Agarwal

Deep Learning Made Easier by Linear Transformations in Perceptrons

Tapani Raiko, Harri Valpola, Yann LeCun

Nonparametric Estimation of Conditional Information and Divergences

Barnabás Póczos, Jeff Schneider

Informative Priors for Markov Blanket Discovery

Adam Pocock, Mikel Lujan, Gavin Brown

Fast interior-point inference in high-dimensional sparse, penalized state-space models

Eftychios Pnevmatikakis, Liam Paninski

Learning Low-order Models for Enforcing High-order Statistics

Patrick Pletscher, Pushmeet Kohli

Part & Clamp: Efficient Structured Output Learning

Patrick Pletscher, Cheng Soon Ong

Approximate Inference by Intersecting Semidefinite Bound and Local Polytope

Jian Peng, Tamir Hazan, Nathan Srebro, Jinbo Xu

On a Connection between Maximum Variance Unfolding, Shortest Path Problems and IsoMap

Alexander Paprotny, Jochen Garcke

Stick-Breaking Beta Processes and the Poisson Process

John Paisley, David Blei, Michael Jordan

Probabilistic acoustic tube: a probabilistic generative model of speech for speech analysis/synthesis

Zhijian Ou, Yang Zhang

Bayesian Quadrature for Ratios

Michael Osborne, Roman Garnett, Stephen Roberts, Christopher Hart, Suzanne Aigrain

Beyond Logarithmic Bounds in Online Learning

Francesco Orabona, Nicolò Cesa-bianchi, Claudio Gentile

A Nonparametric Bayesian Model for Multiple Clustering with Overlapping Feature Views

Donglin Niu, Jennifer Dy, Zoubin Ghahramani

The adversarial stochastic shortest path problem with unknown transition probabilities

Gergely Neu, András György, Csaba Szepesvári

Deep Boltzmann Machines as Feed-Forward Hierarchies

Grégoire Montavon, Mikio Braun, Klaus-Robert Müller

Lifted Linear Programming

Martin Mladenov, Babak Ahmadi, Kristian Kersting

Max-Margin Min-Entropy Models

Kevin Miller, M. Pawan Kumar, Ben Packer, Danny Goodman, Daphne Koller

Globally Optimizing Graph Partitioning Problems Using Message Passing

Elad Mezuman, Yair Weiss

Movement Segmentation and Recognition for Imitation Learning

Franziska Meier, Evangelos Theodorou, Stefan Schaal

Adaptive MCMC with Bayesian Optimization

Nimalan Mahendran, Ziyu Wang, Firas Hamze, Nando de Freitas

Efficient Gaussian Process Inference for Short-Scale Spatio-Temporal Modeling

Jaakko Luttinen, Alexander Ilin

Closed-Form Entropy Limits - A Tool to Monitor Likelihood Optimization of Probabilistic Generative Models

Jörg Lücke, Marc Henniges

A Simple Geometric Interpretation of SVM using Stochastic Adversaries

Roi Livni, Koby Crammer, Amir Globerson

High-Dimensional Structured Feature Screening Using Binary Markov Random Fields

Jie Liu, Chunming Zhang, Catherine Mccarty, Peggy Peissig, Elizabeth Burnside

Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation

Guangcan Liu, Huan Xu, Shuicheng Yan

Efficient Sampling from Combinatorial Space via Bridging

Dahua Lin, John Fisher

Data dependent kernels in nearly-linear time

Guy Lever, Tom Diethe, John Shawe-Taylor

Efficient Hypergraph Clustering

Marius Leordeanu, Cristian Sminchisescu

Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets

Alexandre Lacoste, François Laviolette, Mario Marchand

Message-Passing Algorithms for MAP Estimation Using DC Programming

Akshat Kumar, Shlomo Zilberstein, Marc Toussaint

Marginal Regression For Multitask Learning

Mladen Kolar, Han Liu

Multiple Texture Boltzmann Machines

Jyri Kivinen, Christopher Williams

Regression for sets of polynomial equations

Franz Kiraly, Paul Von Buenau, Jan Muller, Duncan Blythe, Frank Meinecke

Bayesian Classifier Combination

Hyun-Chul Kim, Zoubin Ghahramani

A Stick-Breaking Likelihood for Categorical Data Analysis with Latent Gaussian Models

Mohammad Khan, Shakir Mohamed, Benjamin Marlin, Kevin Murphy

Online Clustering of Processes

Azadeh Khaleghi, Daniil Ryabko, Jeremie Mary, Philippe Preux

On Bayesian Upper Confidence Bounds for Bandit Problems

Emilie Kaufmann, Olivier Cappe, Aurélien Garivier

Random Feature Maps for Dot Product Kernels

Purushottam Kar, Harish Karnick

High-dimensional Sparse Inverse Covariance Estimation using Greedy Methods

Christopher Johnson, Ali Jalali, Pradeep Ravikumar

Detecting Network Cliques with Radon Basis Pursuit

Xiaoye Jiang, Yuan Yao, Han Liu, Leonidas Guibas

A Variance Minimization Criterion to Active Learning on Graphs

Ming Ji, Jiawei Han

Subset Infinite Relational Models

Katsuhiko Ishiguro, Naonori Ueda, Hiroshi Sawada

Variable Selection for Gaussian Graphical Models

Jean Honorio, Dimitris Samaras, Irina Rish, Guillermo Cecchi