Accepted Papers
Scoring anomalies: a M-estimation formulation
Stephan Clemencon, Telecom ParisTech; Jeremie Jakubowicz, Telecom Sud
Management
Bayesian Estimation for Partially Observed MRFs
Yutian Chen, UC Irvine; Max Welling, University of California,
Irvine
High-dimensional Inference via Lipschitz Sparsity-Yielding
Regularizers
Zheng Pan, Tsinghua Univ.; Changshui Zhang, Tsinghua Univ.
Learning to Top-K Search using Pairwise Comparisons
Brian Eriksson, Technicolor
Stochastic blockmodeling of relational event dynamics
Christopher DuBois, UC Irvine; Carter Butts, UC Irvine; Padhraic Smyth,
University of California Irvine
Unsupervised Link Selection in Networks
Quanquan Gu, CS, UIUC; Charu Aggarwal, IBM Research; Jiawei Han, UIUC
Beyond Sentiment: The Manifold of Human Emotions
Seungyeon Kim, Georgia Institute of Technolog; Fuxin Li, Georgia
Institute of Technology; Guy Lebanon, Georgia Institute of Technology; Irfan
Essa, Georgia Institute of Technology
Greedy Bilateral Sketch, Completion and Smoothing
Tianyi Zhou, Universityof Technology Sydney; Dacheng Tao, University of
Technology, Sydney
Further Optimal Regret Bounds for Thompson Sampling
Shipra Agrawal, MSR India; Navin Goyal, MSR India
Faster Training of Structural SVMs with Diverse M-Best
Cutting-Planes
Abner Guzman-Rivera, University of Illinois; Pushmeet Kohli, Microsoft
Research Cambridge; Dhruv Batra, Virginia Tech
Structural Expectation Propagation (SEP): Bayesian structure
learning for networks with latent variables
Nevena Lazic, Microsoft Research; Christopher Bishop, ; John Winn,
DYNA-CARE: Dynamic Cardiac Arrest Risk Estimation
Joyce Ho, University of Texas at Austin; Yubin Park, University of Texas
at Austin; Carlos Carvalho, University of Texas at Austin; Joydeep Ghosh,
University of Texas at Austin
Competing with an Infinite Set of Models in Reinforcement
Learning
Phuong Nguyen, Australian National University; Odalric-Ambrym Maillard,
Montanuniversitaet Leoben; Daniil Ryabko, INRIA, Lille; Ronald Ortner,
Montanuniversitaet Leoben
Central Limit Theorems for Conditional Markov Chains
Mathieu Sinn, IBM Research; Bei Chen, IBM Research - Ireland
Efficient Variational Inference for Gaussian Process Regression
Networks
Trung Nguyen, ANU and NICTA; Edwin Bonilla, NICTA and ANU
Active Learning for Interactive Visualization
Tomoharu Iwata, University of Cambridge; Neil Houlsby, University of
Cambridge; Zoubin Ghahramani, University of Cambridge
ODE parameter inference using adaptive gradient matching with
Gaussian processes
Frank Dondelinger, Biomathematics and Statistics Scotland; Dirk
Husmeier, University of Glasgow; Simon Rogers, University of Glasgow;
Maurizio Filippone, University of Glasgow
Exact Learning of Bounded Tree-width Bayesian Networks
Janne Korhonen, University of Helsinki; Pekka Parviainen,
Completeness Results for Lifted Variable Elimination
Nima Taghipour, KU Leuven; Daan Fierens, KU LEUVEN; Guy Van den Broeck,
UCLA; Jesse Davis, KU LEUVEN; Hendrik Blockeel, KU LEUVEN
Fast Near-GRID Gaussian Process Regression
Yuancheng Luo, University of Maryland; Ramani Duraiswami, University of
Maryland
Convex Collective Matrix Factorization
Guillaume Bouchard, "Xerox Research Centre, Europe"; Dawei Yin,
Lehigh University; Shengbo Guo, Xerox Research Centre Europe
Meta-Transportability of Causal Effects: A Formal Approach
Elias Bareinboim, UCLA; Judea Pearl, UCLA
Why Steiner-tree type algorithms work for community detection
Mung Chiang, Princeton University; Henry Lam, Boston University; Zhenming
Liu, Princeton University; Harold Poor, Princeton University
Clustering Oligarchies
Margareta Ackerman, Caltech; Shai Ben David, ; David Loker, University
of Waterloo; Sivan Sabato, Microsoft Research
Structure Learning of Mixed Graphical Models
Jason Lee, Computational Math & Engineeri; Trevor Hastie, Stanford
University
A Simple Criterion for Controlling Selection Bias
Eunice Yuh-Jie Chen, UCLA; Judea Pearl, UCLA
Clustered Support Vector Machine
Quanquan Gu, CS, UIUC; Jiawei Han, UIUC
A Competitive Test for Uniformity of Monotone Distributions
Ashkan Jafarpour, Univ. of California, San Diego; Jayadev
Acharya, University of California, San Diego; Alon Orlitsky, University of
California, San Diego; Ananda Suresh, University of California, San Diego
Deep Gaussian Processes
Andreas Damianou, University of Sheffield; Neil Lawrence, University of
Sheffield
Permutation estimation and minimax rates of identifiability
Olivier Collier, IMAGINE-ENPC / CREST-ENSAE; Arnak Dalalyan, Ecole des
Ponts ParisTech
Bayesian Structure Learning for Functional Neuroimaging
Oluwasanmi Koyejo, University of Texas at Austin; Mijung Park,
UT Austin; Russell Poldrack, University of Texas at Austin; Joydeep Ghosh,
University of Texas at Austin; Jonathan Pillow, The University of Texas at
Austin
Dual Decomposition for Joint Discrete-Continuous Optimization
Christopher Zach, Microsoft Research
Distribution-Free Distribution Regression
Barnabas Poczos, Carnegie Mellon University; Aarti Singh, Carnegie
Mellon University; Alessandro Rinaldo, Carnegie Mellon University; Larry
Wasserman,
A Last-Step Regression Algorithm for Non-Stationary Online
Learning
Edward Moroshko, Technion; Koby Crammer, Technion University
Efficiently Sampling Probabilistic Programs via Program Analysis
Arun Chaganty, ; Aditya Nori, Microsoft Research India; Sriram Rajamani,
On the Asymptotic Optimality of Maximum Margin Bayesian
Networks
Sebastian Tschiatschek, TU Graz; Franz Pernkopf, TU Graz
Ultrahigh Dimensional Feature Screening via RKHS Embeddings
Krishnakumar Balasubramanian, Gatech; Bharath Sriperumbudur, Cambridge
University ; Guy Lebanon, Georgia Institute of Technology
Data-driven covariate selection for nonparametric estimation of
causal effects
Doris Entner, University of Helsinki; Patrik Hoyer, ; Peter Spirtes,
Mixed LICORS: A Nonparametric Algorithm for Predictive State
Reconstruction
Georg Goerg, Carnegie Mellon University; Cosma Shalizi, Carnegie Mellon
University
Thompson Sampling in Switching Environments with Bayesian
Online Change Detection
Joseph Mellor, University of Manchester; Jonathan Shapiro, University of
Manchester
Collapsed Variational Bayesian Inference for Hidden Markov
Models
Pengyu Wang, University of Oxford; Phil Blunsom, University of Oxford
Supervised Sequential Classification Under Budget Constraints
Kirill Trapeznikov, Boston University; Venkatesh Saligrama, Boston
University; david Castanon, Boston University
Computing the M Most Probable Modes of a Graphical Model
Chao Chen, Rutgers University; Vladimir Kolmogorov, IST Austria; Yan
Zhu, Rutgers University; Dimitris Metaxas, Rutgers University; Christoph
Lampert, IST Austria
Estimating the Partition Function of Graphical Models Using
Langevin Importance Sampling
Jianzhu Ma, TTIC; Jian Peng, ; Sheng Wang, TTIC; Jinbo Xu, TTIC
Random Projections for Support Vector Machines
Saurabh Paul, Rensselaer Polytechnic Inst; Christos Boutsidis, IBM;
Malik Magdon-Ismail, ; Petros Drineas, RPI
A unifying representation for a class of dependent random
measures
Nicholas Foti, Dartmouth College; Sinead Williamson, Carnegie Mellon
University; Daniel Rockmore, Dartmouth College; Joseph Futoma, Dartmouth
College
Dynamic Copula Networks for Modeling Real-valued Time Series
Elad Eban, Hebrew University; gideon Rothschild, Hebrew University; Adi
Mizrahi, Hebrew University; Israel Nelken, Hebrew University; Gal Elidan,
Hebrew University
A Parallel, Block Greedy Method for Sparse Inverse Covariance
Estimation for Ultra-high Dimensions
Prabhanjan Kambadur, IBM TJ Watson Research Center; Aurelie Lozano,
A recursive estimate for the predictive likelihood in a topic
model
James Scott, ; Jason Baldridge, University of Texas at Austin
Nystrom Approximation for Large-Scale Determinantal Processes
Raja Hafiz Affandi, University of Pennsylvania; Emily Fox, ; Ben Taskar,
University of Pennsylvania; Alex Kulesza,
A simple sketching algorithm for entropy estimation over
streaming data
Ioana Cosma, University of Ottawa; Peter Clifford, University of Oxford
Detecting Activations over Graphs using Spanning Tree Wavelet
Bases
James Sharpnack, Carnegie Mellon University; Aarti Singh, Carnegie
Mellon University; Akshay Krishnamurthy, CMU
Learning Social Infectivity in Sparse Low-rank Networks Using
Multi-dimensional Hawkes Processes
Ke Zhou, Georgia institute of technolog; Le Song, Georgia institute of
technology; Hongyuan Zha, Georgia institute of technology
Changepoint Detection over Graphs with the Spectral Scan
Statistic
James Sharpnack, Carnegie Mellon University; Aarti Singh, Carnegie
Mellon University; Alessandro Rinaldo, Carnegie Mellon University
Statistical Tests for Contagion in Observational Social Network
Studies
Greg Ver Steeg, Information Sciences Institute; Aram Galstyan,
Information Sciences Institute, USC
Diagonal Orthant Multinomial Probit Models
James Johndrow, Duke University; Kristian Lum, Virginia Tech; David
Dunson, Duke University
Reconstructing ecological networks with hierarchical Bayesian
regression and Mondrian processes
Andrej Aderhold, University of St Andrews; Dirk Husmeier, University of
Glasgow; V. Anne Smith, University of St Andrews
Bayesian learning of joint distributions of objects
Anjishnu Banerjee, Duke University; Jared Murray, Duke University;
David Dunson, Duke University
Consensus Ranking with Signed Permutations
Raman Arora, TTIC; Marina Meila, University of Washington
Sparse Principal Component Analysis for High Dimensional
Multivariate Time Series
Zhaoran Wang, Princeton University; Fang Han, Johns Hopkins University;
Han Liu,
Texture Modeling with Convolutional Spike-and-Slab RBMs and
Deep Extensions
Heng Luo, Universite de Montreal; Pierre Luc Carrier, Universite de
Montreal; Aaron Courville, Universite de Montreal; Yoshua Bengio, Universite
de Montreal
Block Regularized Lasso for Multivariate Multi-Response Linear
Regression
Weiguang Wang, Syracuse University; Yingbin Liang, Syracuse University;
Eric Xing, Carnegie Mellon University
Predictive Correlation Screening: Application to Two-stage
Predictor Design in High Dimension
Hamed Firouzi, University of Michigan; Alfred Hero III, University of
Michigan
Distributed Learning of Gaussian Graphical Models via Marginal
Likelihoods
Zhaoshi Meng, University of Michigan; Dennis Wei, University of
Michigan; Ami Wiesel, The Hebrew University of Jerusalem ; Alfred Hero III,
University of Michigan
Dynamic Scaled Sampling for Deterministic Constraints
Lei Li, UC Berkeley; Bharath Ramsundar, ; Stuart Russell, UC Berkeley
Localization and Adaptation in Online Learning
Alexander (Sasha) Rakhlin, University of Pennsylvania; Ohad Shamir, ;
Karthik Sridharan, University of Pennsylvania
Recursive Karcher Expectation Estimators And Geometric Law of
Large Numbers
Hesamoddin Salehian, University of Florida; Guang Cheng, ; Jeffrey Ho,
UFL; Baba Vemuri, University of Florida
Distributed and Adaptive Darting Monte Carlo through
Regenerations
Sungjin Ahn, UCI; Yutian Chen, UC Irvine; Max Welling, "University
of California, Irvine"
Uncover Topic-Sensitive Information Diffusion Networks
NAN DU, GATECH; Le Song, Georgia institute of technology; Hyenkyun Woo,
; Hongyuan Zha, Georgia institute of technology
Learning Markov Networks With Arithmetic Circuits
Daniel Lowd, University of Oregon; Amirmohammad Rooshenas, University of
Oregon
Bethe Bounds and Approximating the Global Optimum
Adrian Weller, Columbia University; Tony Jebara, Columbia University