AISTATS 2014

121 papers

Heterogeneous Domain Adaptation for Multiple Classes

Joey Tianyi Zhou, Ivor W.Tsang, Sinno Jialin Pan, Mingkui Tan

Accelerated Stochastic Gradient Method for Composite Regularization

Wenliang Zhong, James Kwok

Efficient Transfer Learning Method for Automatic Hyperparameter Tuning

Dani Yogatama, Gideon Mann

Generating Efficient MCMC Kernels from Probabilistic Programs

Lingfeng Yang, Patrick Hanrahan, Noah Goodman

Nonparametric estimation and testing of exchangeable graph models

Justin Yang, Christina Han, Edoardo Airoldi

Context Aware Group Nearest Shrunken Centroids in Large-Scale Genomic Studies

Juemin Yang, Fang Han, Rafael Irizarry, Han Liu

Mixed Graphical Models via Exponential Families

Eunho Yang, Yulia Baker, Pradeep Ravikumar, Genevera Allen, Zhandong Liu

Dynamic Resource Allocation for Optimizing Population Diffusion

Shan Xue, Alan Fern, Daniel Sheldon

A New Approach to Probabilistic Programming Inference

Frank Wood, Jan Willem Meent, Vikash Mansinghka

Accelerating ABC methods using Gaussian processes

Richard Wilkinson

Bayesian Multi-Scale Optimistic Optimization

Ziyu Wang, Babak Shakibi, Lin Jin, Nando Freitas

Efficient Algorithms and Error Analysis for the Modified Nystrom Method

Shusen Wang, Zhihua Zhang

An LP for Sequential Learning Under Budgets

Joseph Wang, Kirill Trapeznikov, Venkatesh Saligrama

Gaussian Copula Precision Estimation with Missing Values

Huahua Wang, Farideh Fazayeli, Soumyadeep Chatterjee, Arindam Banerjee

Linear-time training of nonlinear low-dimensional embeddings

Max Vladymyrov, Miguel Carreira-Perpinan

Active Learning for Undirected Graphical Model Selection

Divyanshu Vats, Robert Nowak, Richard Baraniuk

Path Thresholding: Asymptotically Tuning-Free High-Dimensional Sparse Regression

Divyanshu Vats, Richard Baraniuk

Scaling Graph-based Semi Supervised Learning to Large Number of Labels Using Count-Min Sketch

Partha Talukdar, William Cohen

Adaptive Variable Clustering in Gaussian Graphical Models

Siqi Sun, Yuancheng Zhu, Jinbo Xu

SMERED: A Bayesian Approach to Graphical Record Linkage and De-duplication

Rebecca Steorts, Rob Hall, Stephen Fienberg

Bat Call Identification with Gaussian Process Multinomial Probit Regression and a Dynamic Time Warping Kernel

Vassilios Stathopoulos, Veronica Zamora-Gutierrez, Kate Jones, Mark Girolami

Explicit Link Between Periodic Covariance Functions and State Space Models

Arno Solin, Simo Särkkä

Loopy Belief Propagation in the Presence of Determinism

David Smith, Vibhav Gogate

In Defense of Minhash over Simhash

Anshumali Shrivastava, Ping Li

Student-t Processes as Alternatives to Gaussian Processes

Amar Shah, Andrew Wilson, Zoubin Ghahramani

Estimating Dependency Structures for non-Gaussian Components with Linear and Energy Correlations

Hiroaki Sasaki, Michael Gutmann, Hayaru Shouno, Aapo Hyvärinen

Lifted MAP Inference for Markov Logic Networks

Somdeb Sarkhel, Deepak Venugopal, Parag Singla, Vibhav Gogate

Class Proportion Estimation with Application to Multiclass Anomaly Rejection

Tyler Sanderson, Clayton Scott

Learning Structured Models with the AUC Loss and Its Generalizations

Nir Rosenfeld, Ofer Meshi, Danny Tarlow, Amir Globerson

Sequential crowdsourced labeling as an epsilon-greedy exploration in a Markov Decision Process

Vikas Raykar, Priyanka Agrawal

Cluster Canonical Correlation Analysis

Nikhil Rasiwasia, Dhruv Mahajan, Vijay Mahadevan, Gaurav Aggarwal

Black Box Variational Inference

Rajesh Ranganath, Sean Gerrish, David Blei

An Analysis of Active Learning with Uniform Feature Noise

Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry Wasserman

Connected Sub-graph Detection

Jing Qian, Venkatesh Saligrama, Yuting Chen

A Stepwise uncertainty reduction approach to constrained global optimization

Victor Picheny

An inclusion optimal algorithm for chain graph structure learning

Jose Peña, Dag Sonntag, Jens Nielsen

Expectation Propagation for Likelihoods Depending on an Inner Product of Two Multivariate Random Variables

Tomi Peltola, Pasi Jylänki, Aki Vehtari

An Efficient Algorithm for Large Scale Compressive Feature Learning

Hristo Paskov, John Mitchell, Trevor Hastie

Learning Bounded Tree-width Bayesian Networks using Integer Linear Programming

Pekka Parviainen, Hossein Shahrabi Farahani, Jens Lagergren

Spoofing Large Probability Mass Functions to Improve Sampling Times and Reduce Memory Costs

Jon Parker, Hans Engler

LAMORE: A Stable, Scalable Approach to Latent Vector Autoregressive Modeling of Categorical Time Series

Yubin Park, Carlos Carvalho, Joydeep Ghosh

To go deep or wide in learning?

Gaurav Pandey, Ambedkar Dukkipati

FuSSO: Functional Shrinkage and Selection Operator

Junier Oliva, Barnabás Póczos, Timothy Verstynen, Aarti Singh, Jeff Schneider

Fast Distribution To Real Regression

Junier Oliva, Willie Neiswanger, Barnabás Póczos, Jeff Schneider, Eric Xing

Scaling Nonparametric Bayesian Inference via Subsample-Annealing

Fritz Obermeyer, Jonathan Glidden, Eric Jonas

Joint Structure Learning of Multiple Non-Exchangeable Networks

Chris Oates, Sach Mukherjee

Robust Forward Algorithms via PAC-Bayes and Laplace Distributions

Asaf Noy, Koby Crammer

Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence

Yung-kyun Noh, Masashi Sugiyama, Song Liu, Marthinus C. Plessis, Frank Chongwoo Park

The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling

Willie Neiswanger, Frank Wood, Eric Xing

Selective Sampling with Drift

Edward Moroshko, Koby Crammer

On the Testability of Models with Missing Data

Karthika Mohan, Judea Pearl

A Geometric Algorithm for Scalable Multiple Kernel Learning

John Moeller, Parasaran Raman, Suresh Venkatasubramanian, Avishek Saha

Efficient Lifting of MAP LP Relaxations Using k-Locality

Martin Mladenov, Kristian Kersting, Amir Globerson

Interpretable Sparse High-Order Boltzmann Machines

Martin Renqiang Min, Xia Ning, Chao Cheng, Mark Gerstein

Active Boundary Annotation using Random MAP Perturbations

Subhransu Maji, Tamir Hazan, Tommi Jaakkola

Active Area Search via Bayesian Quadrature

Yifei Ma, Roman Garnett, Jeff Schneider

PAC-Bayesian Collective Stability

Ben London, Bert Huang, Ben Taskar, Lise Getoor

Learning Heterogeneous Hidden Markov Random Fields

Jie Liu, Chunming Zhang, Elizabeth Burnside, David Page

Jointly Informative Feature Selection

Leonidas Lefakis, Francois Fleuret

Incremental Tree-Based Inference with Dependent Normalized Random Measures

Juho Lee, Seungjin Choi

Towards building a Crowd-Sourced Sky Map

Dustin Lang, David Hogg, Bernhard Schölkopf

Computational Education using Latent Structured Prediction

Tanja Käser, Alexander Schwing, Tamir Hazan, Markus Gross

Fugue: Slow-Worker-Agnostic Distributed Learning for Big Models on Big Data

Abhimanu Kumar, Alex Beutel, Qirong Ho, Eric Xing

Low-Rank Spectral Learning

Alex Kulesza, N. Raj Rao, Satinder Singh

Visual Boundary Prediction: A Deep Neural Prediction Network and Quality Dissection

Jyri Kivinen, Chris Williams, Nicolas Heess

Algebraic Reconstruction Bounds and Explicit Inversion for Phase Retrieval at the Identifiability Threshold

Franz Kiraly, Martin Ehler

Scalable Variational Bayesian Matrix Factorization with Side Information

Yong-Deok Kim, Seungjin Choi

A Gaussian Latent Variable Model for Large Margin Classification of Labeled and Unlabeled Data

Do-kyum Kim, Matthew Der, Lawrence Saul

Scalable Collaborative Bayesian Preference Learning

Mohammad Emtiyaz Khan, Young Jun Ko, Matthias Seeger

Collaborative Ranking for Local Preferences

Berk Kapicioglu, David Rosenberg, Robert Schapire, Tony Jebara

Recovering Distributions from Gaussian RKHS Embeddings

Motonobu Kanagawa, Kenji Fukumizu

New Bounds on Compressive Linear Least Squares Regression

Ata Kaban

A Level-set Hit-and-run Sampler for Quasi-Concave Distributions

Shane Jensen, Dean Foster

Near Optimal Bayesian Active Learning for Decision Making

Shervin Javdani, Yuxin Chen, Amin Karbasi, Andreas Krause, Drew Bagnell

High-Dimensional Density Ratio Estimation with Extensions to Approximate Likelihood Computation

Rafael Izbicki, Ann Lee, Chad Schafer

Global Optimization Methods for Extended Fisher Discriminant Analysis

Satoru Iwata, Yuji Nakatsukasa, Akiko Takeda

A Finite-Sample Generalization Bound for Semiparametric Regression: Partially Linear Models

Ruitong Huang, Csaba Szepesvári

Latent Gaussian Models for Topic Modeling

Changwei Hu, Eunsu Ryu, David Carlson, Yingjian Wang, Lawrence Carin

Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees

Jean Honorio, Tommi Jaakkola

Optimality of Thompson Sampling for Gaussian Bandits Depends on Priors

Junya Honda, Akimichi Takemura

On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning

Matthew Hoffman, Bobak Shahriari, Nando Freitas

Tilted Variational Bayes

James Hensman, Max Zwiessele, Neil D. Lawrence

Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics

Philipp Hennig, Søren Hauberg

A Statistical Model for Event Sequence Data

Kevin Heins, Hal Stern

Exploiting the Limits of Structure Learning via Inherent Symmetry

Peng He, Changshui Zhang

Non-Asymptotic Analysis of Relational Learning with One Network

Peng He, Changshui Zhang

On Estimating Causal Effects based on Supplemental Variables

Takahiro Hayashi, manabu kuroki

Analytic Long-Term Forecasting with Periodic Gaussian Processes

Nooshin HajiGhassemi, Marc Deisenroth

Learning and Evaluation in Presence of Non-i.i.d. Label Noise

Nico Görnitz, Anne Porbadnigk, Alexander Binder, Claudia Sannelli, Mikio Braun

Efficiently Enforcing Diversity in Multi-Output Structured Prediction

Abner Guzmán-rivera, Pushmeet Kohli, Dhruv Batra, Rob Rutenbar

Bayesian Nonparametric Poisson Factorization for Recommendation Systems

Prem Gopalan, Francisco J. Ruiz, Rajesh Ranganath, David Blei

Robust Stochastic Principal Component Analysis

John Goes, Teng Zhang, Raman Arora, Gilad Lerman

Sketching the Support of a Probability Measure

Joachim Giesen, Soeren Laue, Lars Kuehne

Learning with Maximum A-Posteriori Perturbation Models

Andreea Gane, Tamir Hazan, Tommi Jaakkola

Fully-Automatic Bayesian Piecewise Sparse Linear Models

Riki Eto, Ryohei Fujimaki, Satoshi Morinaga, Hiroshi Tamano

Robust learning of inhomogeneous PMMs

Ralf Eggeling, Teemu Roos, Petri Myllymäki, Ivo Grosse

Bayesian Switching Interaction Analysis Under Uncertainty

Zoran Dzunic, John Fisher III

Efficient Inference for Complex Queries on Complex Distributions

Lili Dworkin, Michael Kearns, Lirong Xia

Avoiding pathologies in very deep networks

David Duvenaud, Oren Rippel, Ryan Adams, Zoubin Ghahramani

Bayesian Logistic Gaussian Process Models for Dynamic Networks

Daniele Durante, David Dunson