Poster instructions: Each paper is expected to also have a poster presentation in the evening of June 22 or June 24, about equally split according to the oral presentation dates. The June 22 poster session is for all papers with oral presentations on Sunday or on Monday before lunch. The June 24 poster session is for all papers with oral presentations on Monday after lunch or on Tuesday. Poster sessions start at 7:00pm. We will provide 181cm wide by 101cm tall boards. Authors can use either full size or half the size (e.g., 121cm x 91cm). Poster boards will be numbered. Please see the program booklet (distributed at the registration desk) to know your poster board number. We will provide thumbtacks to enable you to attach your poster to the board. Please remove your poster after the session. Any posters that remain after 12pm on the next day will be discarded.

Videos are available on TechTalks.

Saturday, June 21th

09:30 - 11:30am Tutorial 1 and 2 in parallel Location Details
Bayesian Posterior Inference in the Big Data Arena Room 201
Frank-Wolfe and Greedy Optimization for Learning with Big Data Room 305
13:00 - 15:00pm Tutorial 3 and 4 in parallel Location Details
Finding Structure with Randomness: Stochastic Algorithms for Numerical Linear Algebra Room 201
Emerging Systems for Large-Scale Machine Learning Room 305
15:30 - 17:30pm Tutorial 5 and 6 in parallel Location Details
An introduction to probabilistic programming Room 201
Deep Learning: from Speech Analysis and Recognition to Language and Multi-modal Processing Room 305

Sunday, June 22th

08:30 - 10:00am Welcome and Keynote by Eric Horvitz -
People, Decisions, and Cognition: On Deeper Engagements with Machine Learning
Convention Hall No. 1
10:30 - 12:30pm 6x20 minute talks, 6 sessions in parallel Location Details
Networks and Graph-Based Learning I 305-1
Reinforcement Learning I 201-1
Bayesian Optimization and Gaussian Processes 201-2
Pca and Subspace Models 307
Supervised Learning 201-3
Neural Networks and Deep Learning I 305-2
14:00 - 16:00pm 6x20 minute talks, 6 sessions in parallel Location Details
Graphical Models I 305-1
Bandits I 201-1
Monte Carlo 307
Statistical Methods 201-2
Structured Prediction 201-3
Deep Learning and Vision 305-2
16:20 - 18:20pm 6x20 minute talks, 6 sessions in parallel Location Details
Matrix Completion and Graphs 305-1
Learning Theory I 201-1
Clustering and Nonparametrics 307
Active Learning 201-2
Optimization I 201-3
Large-Scale Learning 305-2
19:00 - 23:00pm Poster session I BICC Room #4

Monday, June 23th

08:30 - 10:00am Keynote by Michael Kearns -
Algorithmic Trading and Machine Learning
Convention Hall No. 1
10:30 - 12:30pm 6x20 minute talks, 6 sessions in parallel Location Details
Latent Variable Models 305-1
Online Learning and Planning 307
Clustering 201-1
Metric Learning and Feature Selection 201-2
Optimization II 201-3
Neural Language and Speech 305-2
14:00 - 16:00pm 6x20 minute talks, 6 sessions in parallel Location Details
Graphical Models and Approximate Inference 305-1
Online Learning I 307
Monte Carlo and Approximate Inference 201-1
Method-Of-Moments and Spectral Methods 201-2
Boosting and Ensemble Methods 201-3
Neural Networks and Deep Learning II 305-2
16:20 - 18:00pm 5x20 minute talks, 6 sessions in parallel Location Details
Matrix Factorization I 305-1
Learning Theory II 201-1
Nonparametric Bayes I 305-2
Manifolds 201-2
Kernel Methods I 307
Unsupervised Learning and Detection 201-3
18:10pm Buses begin to load
18:10 - 19:30pm Go to the Banquet and Museum
19:30 - 21:00pm Banquet
21:00 - 21:30pm Go back to BICC

Tuesday, June 24th

08:30 - 08:50am Awards (Hosted by Baidu)
08:50 - 10:20am Keynote by Michael Jordan (Hosted by Tencent) -
On the Computational and Statistical Interface and "Big Data"
Convention Hall No. 1
10:50 - 12:30pm 5x20 minute talks, 6 sessions in parallel Location Details
Matrix Factorization II 305-1
Bandits II 201-1
Crowd-Sourcing 307
Manifolds and Graphs 201-2
Regularization and Lasso 201-3
Nearest-Neighbors and Large-Scale Learning 305-2
14:00 - 16:00pm 6x20 minute talks, 6 sessions in parallel Location Details
Graphical Models II 305-1
Reinforcement Learning II 201-1
Topic Models 305-2
Sparsity 201-2
Kernel Methods II 201-3
Neural Theory and Spectral Methods 307
16:20 - 18:20pm 6x20 minute talks, 6 sessions in parallel Location Details
Networks and Graph-Based Learning II 305-1
Online Learning II 307
Nonparametric Bayes II 305-2
Features and Feature Selection 201-1
Optimization III 201-2
Time Series and Sequences 201-3
18:20 - 19:20pm IMLS Business Meeting (Open to ALL Attendees) Room 307
19:00 - 23:00pm Poster session II BICC Room #4

Wednesday, June 25th

09:00 - 10:20am Workshops Location Details
10:40 - 12:00pm Workshops Location Details
14:00 - 15:20pm Workshops Location Details
15:40 - 17:00pm Workshops Location Details
Topological Methods for Machine Learning Convention Hall No.4A
Workshop on Crowdsourcing and Human Computing Convention Hall No.4B
The 3rd Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM) Convention Hall No.4C
Learning, Security & Privacy Convention Hall No.4D
Designing Machine Learning Platforms for Big Data Room 201A
New Learning Frameworks and Models for Big Data Room 201B
Deep Learning Models for Emerging Big Data Applications Room 201C
Workshop on the Method of Moments and Spectral Learning Convention Hall No.4E
Causal Modeling & Machine Learning Convention Hall No.4F
Machine Learning in China Room 308

Thursday, June 26th

09:00 - 10:20am Workshops Location Details
10:40 - 12:00pm Workshops Location Details
14:00 - 15:20pm Workshops Location Details
15:40 - 17:00pm Workshops Location Details
The AutoML Convention Hall No.4A
Unsupervised Learning for Bioacoustic Big Data Convention Hall No.4B
Knowledge-Powered Deep Learning for Text Mining Convention Hall No.4C
Covariance Selection & Graphical Model Structure Learning Convention Hall No.4D
Optimizing Customer Lifetime Value in Online Marketing Convention Hall No.4E
Learning Tractable Probabilistic Models Convention Hall No.4F
Divergence Methods for Probabilistic Inference Room 308
ML Meets Systems Room 307

Detailed Schedule

Sunday June 22, 10:30

- Track A - Networks and Graph-Based Learning I (Location: 305-1, Chair: Le Song)

10:30 Joint Inference of Multiple Label Types in Large Networks
Deepayan Chakrabarti; Stanislav Funiak; Jonathan Chang; Sofus Macskassy
10:50 Learning the Consistent Behavior of Common Users for Target Node Prediction across Social Networks
Shan-Hung Wu; Hao-Heng Chien; Kuan-Hua Lin; Philip Yu
11:10 Learning Modular Structures from Network Data and Node Variables
Elham Azizi; Edoardo Airoldi; James Galagan
11:30 Weighted Graph Clustering with Non-Uniform Uncertainties
Yudong Chen; Shiau Hong Lim; Huan Xu
11:50 Efficient Dimensionality Reduction for High-Dimensional Network Estimation
Safiye Celik; Benjamin Logsdon; Su-In Lee
12:10 Discovering Latent Network Structure in Point Process Data
Scott Linderman; Ryan Adams

- Track B - Reinforcement Learning I (Location: 201-1, Chair: Thomas Dietterich)

10:30 PAC-inspired Option Discovery in Lifelong Reinforcement Learning
Emma Brunskill; Lihong Li
10:50 Time-Regularized Interrupting Options (TRIO)
Daniel Mankowitz; Timothy Mann; Shie Mannor
11:10 Approximate Policy Iteration Schemes: A Comparison
Bruno Scherrer
11:30 Model-Based Relational RL When Object Existence is Partially Observable
Vien Ngo; Marc Toussaint
11:50 GeNGA: A Generalization of Natural Gradient Ascent with Positive and Negative Convergence Results
Philip Thomas
12:10 Scaling Up Robust MDPs using Function Approximation
Aviv Tamar; Shie Mannor; Huan Xu

- Track C - Bayesian Optimization and Gaussian Processes (Location: 201-2, Chair: Max Welling)

10:30 Agnostic Bayesian Learning of Ensembles
Alexandre Lacoste; Mario Marchand; Francois Laviolette; Hugo Larochelle
10:50 Robust RegBayes: Selectively Incorporating First-Order Logic Domain Knowledge into Bayesian Models
Shike Mei; Jun Zhu; Jerry Zhu
11:10 An Efficient Approach for Assessing Hyperparameter Importance
Frank Hutter; Holger Hoos; Kevin Leyton-Brown
11:30 Bayesian Optimization with Inequality Constraints
Jacob Gardner; Matt Kusner; Zhixiang; Xu; Kilian Weinberger; John Cunningham
11:50 A PAC-Bayesian bound for Lifelong Learning
Anastasia Pentina; Christoph Lampert
12:10 Gaussian Processes for Bayesian Estimation in Ordinary Differential Equations
David Barber; Yali Wang

- Track D - PCA and Subspace Models (Location: 307, Chair: Anima Anandkumar)

10:30 Robust Principal Component Analysis with Complex Noise
Qian Zhao; Deyu Meng; Zongben Xu; Wangmeng Zuo; Lei Zhang
10:50 Multivariate Maximal Correlation Analysis
Hoang Vu Nguyen; Emmanuel Müller; Jilles Vreeken; Pavel Efros; Klemens Böhm
11:10 Discriminative Features via Generalized Eigenvectors
Nikos Karampatziakis; Paul Mineiro
11:30 Randomized Nonlinear Component Analysis
David Lopez-Paz; Suvrit Sra; Alex Smola; Zoubin Ghahramani; Bernhard Schoelkopf
11:50 Memory and Computation Efficient PCA via Very Sparse Random Projections
Farhad Pourkamali Anaraki; Shannon Hughes
12:10 Optimal Mean Robust Principal Component Analysis
Feiping Nie; Jianjun Yuan; Heng Huang

- Track E - Supervised Learning (Location: 201-3, Chair: Zhi-Hua Zhou)

10:30 The Coherent Loss Function for Classification
Wenzhuo Yang; Melvyn Sim; Huan Xu
10:50 Condensed Filter Tree for Cost-Sensitive Multi-Label Classification
Chun-Liang Li; Hsuan-Tien Lin
11:10 Robust Learning under Uncertain Test Distributions: Relating Covariate Shift to Model Misspecification
Junfeng Wen; Chun-Nam Yu; Russell Greiner
11:30 A Statistical Convergence Perspective of Algorithms for Rank Aggregation from Pairwise Data
Arun Rajkumar; Shivani Agarwal
11:50 GEV-Canonical Regression for Accurate Binary Class Probability Estimation when One Class is Rare
Arpit Agarwal; Harikrishna Narasimhan; Shivaram Kalyanakrishnan; Shivani Agarwal
12:10 Guess-Averse Loss Functions For Cost-Sensitive Multiclass Boosting
Oscar Beijbom; Mohammad Saberian; David Kriegman; Nuno Vasconcelos

- Track F - Neural Networks and Deep Learning I (Location: 305-2, Chair: Yoshua Bengio)

10:30 Structured Recurrent Temporal Restricted Boltzmann Machines
Roni Mittelman; Benjamin Kuipers; Silvio Savarese; Honglak Lee
10:50 A Deep and Tractable Density Estimator
Benigno Uria; Iain Murray; Hugo Larochelle
11:10 Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction
Jian Zhou; Olga Troyanskaya
11:30 Deep AutoRegressive Networks
Karol Gregor; Ivo Danihelka; Andriy Mnih; Charles Blundell; Daan Wierstra
11:50 Stochastic Backpropagation and Approximate Inference in Deep Generative Models
Danilo Jimenez Rezende; Shakir Mohamed; Daan Wierstra
12:10 Neural Variational Inference and Learning in Belief Networks
Andriy Mnih; Karol Gregor

Sunday June 22, 14:00

- Track A - Graphical Models I (Location: 305-1, Chair: David Sontag)

14:00 Linear and Parallel Learning of Markov Random Fields
Yariv Mizrahi; Misha Denil; Nando De Freitas
14:20 Putting MRFs on a Tensor Train
Alexander Novikov; Anton Rodomanov; Anton Osokin; Dmitry Vetrov
14:40 Gaussian Approximation of Collective Graphical Models
Liping Liu; Daniel Sheldon; Thomas Dietterich
15:00 Scalable Semidefinite Relaxation for Maximum A Posterior Estimation
Qixing Huang; Yuxin Chen; Leonidas Guibas
15:20 Globally Convergent Parallel MAP LP Relaxation Solver using the Frank-Wolfe Algorithm
Alexander Schwing; Tamir Hazan; Marc Pollefeys; Raquel Urtasun
15:40 Inferning with High Girth Graphical Models
Uri Heinemann; Amir Globerson

- Track B - Bandits I (Location: 201-1, Chair: Daniel Hsu)

14:00 Thompson Sampling for Complex Online Problems
Aditya Gopalan; Shie Mannor; Yishay Mansour
14:20 Unimodal Bandits: Regret Lower Bounds and Optimal Algorithms
Richard Combes; Alexandre Proutiere
14:40 Reducing Dueling Bandits to Cardinal Bandits
Nir Ailon; Zohar Karnin; Thorsten Joachims
15:00 Combinatorial Partial Monitoring Game with Linear Feedback and Its Applications
Tian Lin; Bruno Abrahao; Robert Kleinberg; John Lui; Wei Chen
15:20 Online Stochastic Optimization under Correlated Bandit Feedback
Mohammad Gheshlaghi Azar; Alessandro Lazaric; Emma Brunskill
15:40 Adaptive Monte Carlo via Bandit Allocation
James Neufeld; Andras Gyorgy; Csaba Szepesvari; Dale Schuurmans

- Track C - Monte Carlo (Location: 307, Chair: Jun Zhu)

14:00 Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget
Anoop Korattikara; Yutian Chen; Max Welling
14:20 Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach
Rémi Bardenet; Arnaud Doucet; Chris Holmes
14:40 Distributed Stochastic Gradient MCMC
Sungjin Ahn; Babak Shahbaba; Max Welling
15:00 Kernel Adaptive Metropolis-Hastings
Dino Sejdinovic; Heiko Strathmann; Maria Lomeli Garcia; Christophe Andrieu; Arthur Gretton
15:20 Stochastic Gradient Hamiltonian Monte Carlo
Tianqi Chen; Emily Fox; Carlos Guestrin
15:40 A Compilation Target for Probabilistic Programming Languages
Brooks Paige; Frank Wood

- Track D - Statistical Methods (Location: 201-2, Chair: Jingrui He)

14:00 Generalized Exponential Concentration Inequality for Renyi Divergence Estimation
Shashank Singh; Barnabas Poczos
14:20 Consistency of Causal Inference under the Additive Noise Model
Samory Kpotufe; Eleni Sgouritsa; Dominik Janzing; Bernhard Schoelkopf
14:40 The Falling Factorial Basis and Its Statistical Applications
Yu-Xiang Wang; Alex Smola; Ryan Tibshirani
15:00 Concept Drift Detection Through Resampling
Maayan Harel; Shie Mannor; Ran El-Yaniv; Koby Crammer
15:20 A Bayesian Wilcoxon signed-rank test based on the Dirichlet process
Alessio Benavoli; Giorgio Corani; Francesca Mangili; Marco Zaffalon; Fabrizio Ruggeri

- Track E - Structured Prediction (Location: 201-3, Chair: Martin Jaggi)

14:00 Marginal Structured SVM with Hidden Variables
Wei Ping; Qiang Liu; Alex Ihler
14:20 Scalable Gaussian Process Structured Prediction for Grid Factor Graph Applications
Sebastien Bratieres; Novi Quadrianto; Sebastian Nowozin; Zoubin Ghahramani
14:40 High Order Regularization for Semi-Supervised Learning of Structured Output Problems
Yujia Li; Rich Zemel
15:00 Spectral Regularization for Max-Margin Sequence Tagging
Ariadna Quattoni; Borja Balle; Xavier Carreras; Amir Globerson
15:20 On Robustness and Regularization of Structural Support Vector Machines
Mohamad Ali Torkamani; Daniel Lowd
15:40 Structured Prediction of Network Response
Hongyu Su; Aristides Gionis; Juho Rousu

- Track F - Deep Learning and Vision (Location: 305-2, Chair: Trevor Darrell)

14:00 Recurrent Convolutional Neural Networks for Scene Labeling
Pedro Pinheiro; Ronan Collobert
14:20 Latent Semantic Representation Learning for Scene Classification
Xin Li; Yuhong Guo
14:40 DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Jeff Donahue; Yangqing Jia; Oriol Vinyals; Judy Hoffman; Ning Zhang; Eric Tzeng; Trevor Darrell
15:00 Hierarchical Conditional Random Fields for Outlier Detection: An Application to Detecting Epileptogenic Cortical Malformations
Bilal Ahmed; Thomas Thesen; Karen Blackmon; Yijun Zhao; Orrin Devinsky; Ruben Kuzniecky; Carla Brodley
15:20 Stable and Efficient Representation Learning with Nonnegativity Constraints
Tsung-Han Lin; H. T. Kung
15:40 Learning by Stretching Deep Networks
Gaurav Pandey; Ambedkar Dukkipati

Sunday June 22, 16:20

- Track A - Matrix Completion and Graphs (Location: 305-1, Chair: Sanjiv Kumar)

16:20 Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery
Cun Mu; Bo Huang; John Wright; Donald Goldfarb
16:40 Near-Optimal Joint Object Matching via Convex Relaxation
Yuxin Chen; Leonidas Guibas; Qixing Huang
17:00 Coherent Matrix Completion
Yudong Chen; Srinadh Bhojanapalli; Sujay Sanghavi; Rachel Ward
17:20 Universal Matrix Completion
Srinadh Bhojanapalli; Prateek Jain
17:40 Exponential Family Matrix Completion under Structural Constraints
Suriya Gunasekar; Pradeep Ravikumar; Joydeep Ghosh
18:00 A Consistent Histogram Estimator for Exchangeable Graph Models
Stanley Chan; Edoardo Airoldi

- Track B - Learning Theory I (Location: 201-1, Chair: Sanjoy Dasgupta)

16:20 Concentration in unbounded metric spaces and algorithmic stability
Aryeh Kontorovich
16:40 Heavy-tailed regression with a generalized median-of-means
Daniel Hsu; Sivan Sabato
17:00 Learnability of the Superset Label Learning Problem
Liping Liu; Thomas Dietterich
17:20 Maximum Margin Multiclass Nearest Neighbors
Aryeh Kontorovich; Roi Weiss
17:40 Sample Efficient Reinforcement Learning with Gaussian Processes
Robert Grande; Thomas Walsh; Jonathan How
18:00 Scaling Up Approximate Value Iteration with Options: Better Policies with Fewer Iterations
Timothy Mann; Shie Mannor

- Track C - Clustering and Nonparametrics (Location: 307, Chair: John Paisley)

16:20 Von Mises-Fisher Clustering Models
Siddharth Gopal; Yiming Yang
16:40 Online Bayesian Passive-Aggressive Learning
Tianlin Shi; Jun Zhu
17:00 Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts
Tien Vu Nguyen; Dinh Phung; Xuanlong Nguyen; Swetha Venkatesh; Hung Bui
17:20 Hierarchical Dirichlet Scaling Process
Dongwoo Kim; Alice Oh
17:40 Fast Computation of Wasserstein Barycenters
Marco Cuturi; Arnaud Doucet
18:00 Max-Margin Infinite Hidden Markov Models
Aonan Zhang; Jun Zhu; Bo Zhang

- Track D - Active Learning (Location: 201-2, Chair: Zhi-Hua Zhou)

16:20 Diagnosis determination: decision trees optimizing simultaneously worst and expected testing cost
Ferdinando Cicalese; Eduardo Laber; Aline Medeiros Saettler
16:40 Nonmyopic $\epsilon$-Bayes-Optimal Active Learning of Gaussian Processes
Trong Nghia Hoang; Bryan Kian Hsiang Low; Patrick Jaillet; Mohan Kankanhalli
17:00 Hard-Margin Active Linear Regression
Zohar Karnin; Elad Hazan
17:20 Active Transfer Learning under Model Shift
Xuezhi Wang; Tzu-Kuo Huang; Jeff Schneider
17:40 Gaussian Process Optimization with Mutual Information
Emile Contal; Vianney Perchet; Nicolas Vayatis

- Track E - Optimization I (Location: 201-3, Chair: Tong Zhang)

16:20 An Adaptive Accelerated Proximal Gradient Method and its Homotopy Continuation for Sparse Optimization
Qihang Lin; Lin Xiao
16:40 Finito: A faster, permutable incremental gradient method for big data problems
Aaron Defazio; Justin Domke; Tiberio Caetano
17:00 Asynchronous Distributed ADMM for Consensus Optimization
Ruiliang Zhang; James Kwok
17:20 Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods
Jascha Sohl-Dickstein; Ben Poole; Surya Ganguli
17:40 Least Squares Revisited: Scalable Approaches for Multi-class Prediction
Alekh Agarwal; Sham Kakade; Nikos Karampatziakis; Le Song; Gregory Valiant
18:00 A Statistical Perspective on Algorithmic Leveraging
Ping Ma; Michael Mahoney; Bin Yu

- Track F - Large-Scale Learning (Location: 305-2, Chair: Joseph Gonzalez)

16:20 Large-scale Multi-label Learning with Missing Labels
Hsiang-Fu Yu; Prateek Jain; Purushottam Kar; Inderjit Dhillon
16:40 Dual Query: Practical Private Query Release for High Dimensional Data
Marco Gaboardi; Emilio Jesus Gallego Arias; Justin Hsu; Aaron Roth; Zhiwei Steven Wu
17:00 A Highly Scalable Parallel Algorithm for Isotropic Total Variation Models
Jie Wang; Qingyang Li; Sen Yang; Wei Fan; Peter Wonka; Jieping Ye
17:20 Buffer k-d Trees: Processing Massive Nearest Neighbor Queries on GPUs
Fabian Gieseke; Justin Heinermann; Cosmin Oancea; Christian Igel
17:40 Fast Multi-stage Submodular Maximization
Kai Wei; Rishabh Iyer; Jeff Bilmes
18:00 Multi-label Classification via Feature-aware Implicit Label Space Encoding
Zijia Lin; Guiguang Ding; Mingqing Hu; Jianmin Wang

Monday June 23, 10:30

- Track A - Latent Variable Models (Location: 305-1, Chair: Pedro Domingos)

10:30 A Discriminative Latent Variable Model for Online Clustering
Rajhans Samdani; Kai-Wei Chang; Dan Roth
10:50 Exchangeable Variable Models
Mathias Niepert; Pedro Domingos
11:10 Learning Latent Variable Gaussian Graphical Models
Zhaoshi Meng; Brian Eriksson; Al Hero
11:30 Latent Variable Copula Inference for Bundle Pricing from Retail Transaction Data
Benjamin Letham; Wei Sun; Anshul Sheopuri
11:50 Affinity Weighted Embedding
Jason Weston; Ron Weiss; Hector Yee
12:10 Learning the Irreducible Representations of Commutative Lie Groups
Taco Cohen; Max Welling

- Track B - Online Learning and Planning (Location:307, Chair: Thomas Dietterich)

10:30 Covering Number for Efficient Heuristic-based POMDP Planning
Zongzhang Zhang; David Hsu; Wee Sun Lee
10:50 Learning Complex Neural Network Policies with Trajectory Optimization
Sergey Levine; Vladlen Koltun
11:10 A Physics-Based Model Prior for Object-Oriented MDPs
Jonathan Scholz; Martin Levihn; Charles Isbell
11:30 Online Multi-Task Learning for Policy Gradient Methods
Haitham Bou Ammar; Eric Eaton; Paul Ruvolo; Matthew Taylor
11:50 Pursuit-Evasion Without Regret, with an Application to Trading
Lili Dworkin; Michael Kearns; Yuriy Nevmyvaka
12:10 Adaptivity and Optimism: An Improved Exponentiated Gradient Algorithm
Jacob Steinhardt; Percy Liang

- Track C - Clustering (Location: 201-1, Chair: Sanjoy Dasgupta)

10:30 Demystifying Information-Theoretic Clustering
Greg Ver Steeg; Aram Galstyan; Fei Sha; Simon DeDeo
10:50 Clustering in the Presence of Background Noise
Shai Ben-David; Nika Haghtalab
11:10 Hierarchical Quasi-Clustering Methods for Asymmetric Networks
Gunnar Carlsson; Facundo Mémoli; Alejandro Ribeiro; Santiago Segarra
11:30 Local algorithms for interactive clustering
Pranjal Awasthi; Maria Balcan; Konstantin Voevodski
11:50 Standardized Mutual Information for Clustering Comparisons: One Step Further in Adjustment for Chance
Simone Romano; James Bailey; Vinh Nguyen; Karin Verspoor
12:10 A Single-Pass Algorithm for Efficiently Recovering Sparse Cluster Centers of High-dimensional Data
Jinfeng Yi; Lijun Zhang; Jun Wang; Rong Jin; Anil Jain

- Track D - Metric Learning and Feature Selection (Location: 201-2, Chair: Kilian Weinberger)

10:30 Large-Margin Metric Learning for Constrained Partitioning Problems
Rémi Lajugie; Francis Bach; Sylvain Arlot
10:50 Robust Distance Metric Learning via Simultaneous L1-Norm Minimization and Maximization
Hua Wang; Feiping Nie; Heng Huang
11:10 Efficient Learning of Mahalanobis Metrics for Ranking
Daryl Lim; Gert Lanckriet
11:30 Stochastic Neighbor Compression
Matt Kusner; Stephen Tyree; Kilian Weinberger; Kunal Agrawal
11:50 Large-margin Weakly Supervised Dimensionality Reduction
Chang Xu; Dacheng Tao; Chao Xu; Yong Rui
12:10 Sparse meta-Gaussian information bottleneck
Melanie Rey; Volker Roth; Thomas Fuchs

- Track E - Optimization II (Location: 201-3, Chair: Jieping Ye)

10:30 Fast Stochastic Alternating Direction Method of Multipliers
Wenliang Zhong; James Kwok
10:50 Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization
Shai Shalev-Shwartz; Tong Zhang
11:10 An Asynchronous Parallel Stochastic Coordinate Descent Algorithm
Ji Liu; Steve Wright; Christopher Re; Victor Bittorf; Srikrishna Sridhar
11:30 Towards an optimal stochastic alternating direction method of multipliers
Samaneh Azadi; Suvrit Sra
11:50 Stochastic Dual Coordinate Ascent with Alternating Direction Method of Multipliers
Taiji Suzuki
12:10 Communication-Efficient Distributed Optimization using an Approximate Newton-type Method
Ohad Shamir; Nati Srebro; Tong Zhang

- Track F - Neural Language and Speech (Location 305-2, Chair: Yoshua Bengio)

10:30 Multimodal Neural Language Models
Ryan Kiros; Ruslan Salakhutdinov; Rich Zemel
10:50 Distributed Representations of Sentences and Documents
Quoc Le; Tomas Mikolov
11:10 Learning Character-level Representations for Part-of-Speech Tagging
Cicero Dos Santos; Bianca Zadrozny
11:30 Compositional Morphology for Word Representations and Language Modelling
Jan Botha; Phil Blunsom
11:50 Towards End-To-End Speech Recognition with Recurrent Neural Networks
Alex Graves; Navdeep Jaitly
12:10 A Clockwork RNN
Jan Koutnik; Klaus Greff; Faustino Gomez; Juergen Schmidhuber

Monday June 23, 14:00

- Track A - Graphical Models and Approximate Inference (Location: 305-1, Chair: Amir Globerson)

14:00 Probabilistic Partial Canonical Correlation Analysis
Yusuke Mukuta; Tatsuya Harada
14:20 Min-Max Problems on Factor Graphs
Siamak Ravanbakhsh; Christopher Srinivasa; Brendan Frey; Russell Greiner
14:40 Skip Context Tree Switching
Marc Bellemare; Joel Veness; Erik Talvitie
15:00 Learning the Parameters of Determinantal Point Process Kernels
Raja Hafiz Affandi; Emily Fox; Ryan Adams; Ben Taskar
15:20 Deterministic Anytime Inference for Stochastic Continuous-Time Markov Processes
E. Busra Celikkaya; Christian Shelton
15:40 Doubly Stochastic Variational Bayes for non-Conjugate Inference
Michalis Titsias; Miguel Lázaro-Gredilla

- Track B - Online Learning I (Location: 307, Chair: Ohad Shamir)

14:00 On the convergence of no-regret learning in selfish routing
Walid Krichene; Benjamin Drighès; Alexandre Bayen
14:20 Optimal PAC Multiple Arm Identification with Applications to Crowdsourcing
Yuan Zhou; Xi Chen; Jian Li
14:40 Prediction with Limited Advice and Multiarmed Bandits with Paid Observations
Yevgeny Seldin; Peter Bartlett; Koby Crammer; Yasin Abbasi-Yadkori
15:00 One Practical Algorithm for Both Stochastic and Adversarial Bandits
Yevgeny Seldin; Aleksandrs Slivkins
15:20 A Bayesian Framework for Online Classifier Ensemble
Qinxun Bai; Henry Lam; Stan Sclaroff
15:40 Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits
Alekh Agarwal; Daniel Hsu; Satyen Kale; John Langford; Lihong Li; Robert Schapire

- Track C - Monte Carlo and Approximate Inference (Location: 201-1, Chair: Max Welling)

14:00 Memory (and Time) Efficient Sequential Monte Carlo
Seong-Hwan Jun; Alexandre Bouchard-Côté
14:20 Efficient Continuous-Time Markov Chain Estimation
Monir Hajiaghayi; Bonnie Kirkpatrick; Liangliang Wang; Alexandre Bouchard-Côté
14:40 Filtering with Abstract Particles
Jacob Steinhardt; Percy Liang
15:00 Spherical Hamiltonian Monte Carlo for Constrained Target Distributions
Shiwei Lan; Bo Zhou; Babak Shahbaba
15:20 Hamiltonian Monte Carlo Without Detailed Balance
Jascha Sohl-Dickstein; Mayur Mudigonda; Michael DeWeese
15:40 Approximation Analysis of Stochastic Gradient Langevin Dynamics by using Fokker-Planck Equation and Ito Process
Issei Sato; Hiroshi Nakagawa

- Track D - Method-Of-Moments and Spectral Methods (Location: 201-2, Chair: David Sontag)

14:00 Computing Parametric Ranking Models via Rank-Breaking
Hossein Azari Soufiani; David Parkes; Lirong Xia
14:20 Learning Mixtures of Linear Classifiers
Yuekai Sun; Stratis Ioannidis; Andrea Montanari
14:40 Methods of Moments for Learning Stochastic Languages: Unified Presentation and Empirical Comparison
Borja Balle; William Hamilton; Joelle Pineau
15:00 Estimating Latent-Variable Graphical Models using Moments and Likelihoods
Arun Tejasvi Chaganty; Percy Liang
15:20 Alternating Minimization for Mixed Linear Regression
Xinyang Yi; Constantine Caramanis; Sujay Sanghavi
15:40 Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning
François Denis; Mattias Gybels; Amaury Habrard

- Track E - Boosting and Ensemble Methods (Location: 201-3, Chair: Tong Zhang)

14:00 Boosting with Online Binary Learners for the Multiclass Bandit Problem
Shang-Tse Chen; Hsuan-Tien Lin; Chi-Jen Lu
14:20 Narrowing the Gap: Random Forests In Theory and In Practice
Misha Denil; David Matheson; Nando De Freitas
14:40 Ensemble Methods for Structured Prediction
Corinna Cortes; Vitaly Kuznetsov; Mehryar Mohri
15:00 Deep Boosting
Corinna Cortes; Mehryar Mohri; Umar Syed
15:20 Dynamic Programming Boosting for Discriminative Macro-Action Discovery
Leonidas Lefakis; Francois Fleuret
15:40 A Convergence Rate Analysis for LogitBoost, MART and Their Variant
Peng Sun; Tong Zhang; Jie Zhou

- Track F - Neural Networks and Deep Learning II (Location: 305-2, Chair: Trevor Darrell)

14:00 Learning to Disentangle Factors of Variation with Manifold Interaction
Scott Reed; Kihyuk Sohn; Yuting Zhang; Honglak Lee
14:20 Marginalized Denoising Auto-encoders for Nonlinear Representations
Minmin Chen; Kilian Weinberger; Fei Sha; Yoshua Bengio
14:40 Deep Generative Stochastic Networks Trainable by Backprop
Yoshua Bengio; Eric Laufer; Guillaume Alain; Jason Yosinski
15:00 Learning Ordered Representations with Nested Dropout
Oren Rippel; Michael Gelbart; Ryan Adams
15:20 Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets
Diederik Kingma; Max Welling
15:40 Signal recovery from Pooling Representations
Joan Bruna Estrach; Arthur Szlam; Yann LeCun

Monday June 23, 16:20

- Track A - Matrix Factorization I (Location: 305-1, Chair: Thorsten Joachims)

16:20 Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices
Jose Miguel Hernandez-Lobato; Neil Houlsby; Zoubin Ghahramani
16:40 Cold-start Active Learning with Robust Ordinal Matrix Factorization
Neil Houlsby; Jose Miguel Hernandez-Lobato; Zoubin Ghahramani
17:00 Probabilistic Matrix Factorization with Non-random Missing Data
Jose Miguel Hernandez-Lobato; Neil Houlsby; Zoubin Ghahramani
17:20 A Deep Semi-NMF Model for Learning Hidden Representations
George Trigeorgis; Konstantinos Bousmalis; Stefanos Zafeiriou; Bjoern Schuller
17:40 Structured Low-Rank Matrix Factorization: Optimality, Algorithm, and Applications to Image Processing
Benjamin Haeffele; Eric Young; Rene Vidal

- Track B - Learning Theory II (Location: 201-1, Chair: Ohad Shamir)

16:20 Lower Bounds for the Gibbs Sampler over Mixtures of Gaussians
Christopher Tosh; Sanjoy Dasgupta
16:40 (Near) Dimension Independent Risk Bounds for Differentially Private Learning
Prateek Jain; Abhradeep Guha Thakurta
17:00 Learning Theory and Algorithms for revenue optimization in second price auctions with reserve
Mehryar Mohri; Andres Munoz Medina
17:20 Multi-period Trading Prediction Markets with Connections to Machine Learning
Jinli Hu; Amos Storkey
17:40 Towards Minimax Online Learning with Unknown Time Horizon
Haipeng Luo; Robert Schapire

- Track C - Nonparametric Bayes I (Location: 305-2, Chair: Alexandre Bouchard-Cote)

16:20 Rectangular Tiling Process
Masahiro Nakano; Katsuhiko Ishiguro; Akisato Kimura; Takeshi Yamada; Naonori Ueda
16:40 A reversible infinite HMM using normalised random measures
David Knowles; Zoubin Ghahramani; Konstantina Palla
17:00 Scalable Bayesian Low-Rank Decomposition of Incomplete Multiway Tensors
Piyush Rai; Yingjian Wang; Shengbo Guo; Gary Chen; David Dunson; Lawrence Carin
17:20 Input Warping for Bayesian Optimization of Non-Stationary Functions
Jasper Snoek; Kevin Swersky; Rich Zemel; Ryan Adams
17:40 Beta Diffusion Trees
Creighton Heaukulani; David Knowles; Zoubin Ghahramani

- Track D - Manifolds (Location: 201-2, Chair: Laurens Van Der Maaten)

16:20 An Information Geometry of Statistical Manifold Learning
Ke Sun; Stéphane Marchand-Maillet
16:40 Geodesic Distance Function Learning via Heat Flow on Vector Fields
Binbin Lin; Ji Yang; Xiaofei He; Jieping Ye
17:00 Two-Stage Metric Learning
Jun Wang; Ke Sun; Fei Sha; Stéphane Marchand-Maillet; Alexandros Kalousis
17:20 Transductive Learning with Multi-class Volume Approximation
Gang Niu; Bo Dai; Christoffel du Plessis; Masashi Sugiyama
17:40 Convergence rates for persistence diagram estimation in Topological Data Analysis
Frédéric Chazal; Marc Glisse; Catherine Labruère; Bertrand Michel

- Track E - Kernel Methods I (Location: 307, Chair: Le Song)

16:20 On p-norm Path Following in Multiple Kernel Learning for Non-linear Feature Selection
Pratik Jawanpuria; Manik Varma; Saketha Nath
16:40 A Divide-and-Conquer Solver for Kernel Support Vector Machines
Cho-Jui Hsieh; Si Si; Inderjit Dhillon
17:00 Memory Efficient Kernel Approximation
Si Si; Cho-Jui Hsieh; Inderjit Dhillon
17:20 Maximum Mean Discrepancy for Class Ratio Estimation: Convergence Bounds and Kernel Selection
Arun Iyer; Saketha Nath; Sunita Sarawagi
17:40 Robust and Efficient Kernel Hyperparameter Paths with Guarantees
Joachim Giesen; Soeren Laue; Patrick Wieschollek
18:00 Nonparametric Estimation of Multi-View Latent Variable Models
Le Song; Animashree Anandkumar; Bo Dai; Bo Xie

- Track F - Unsupervised Learning and Detection (Location: 201-3, Chair: Jerry Zhu)

16:20 Anomaly Ranking as Supervised Bipartite Ranking
Stephan Clémençon; Sylvain Robbiano
16:40 On learning to localize objects with minimal supervision
Hyun Oh Song; Ross Girshick; Stefanie Jegelka; Julien Mairal; Zaid Harchaoui; Trevor Darrell
17:00 Active Detection via Adaptive Submodularity
Yuxin Chen; Hiroaki Shioi; Cesar Fuentes Montesinos; Lian Pin Koh; Serge Wich; Andreas Krause
17:20 Structured Generative Models of Natural Source Code
Chris Maddison; Daniel Tarlow
17:40 Coordinate-descent for learning orthogonal matrices through Givens rotations
Uri Shalit; Gal Chechik

Tuesday June 24, 10:50

- Track A - Matrix Factorization II (Location: 305-1, Chair: Joel Tropp)

10:50 Rank-One Matrix Pursuit for Matrix Completion
Zheng Wang; Ming-Jun Lai; Zhaosong Lu; Wei Fan; Hasan Davulcu; Jieping Ye
11:10 Convex Total Least Squares
Dmitry Malioutov; Nikolai Slavov
11:30 Nuclear Norm Minimization via Active Subspace Selection
Cho-Jui Hsieh; Peder Olsen
11:50 Riemannian Pursuit for Big Matrix Recovery
Mingkui Tan; Ivor W. Tsang; Li Wang; Bart Vandereycken; Sinno Jialin Pan
12:10 Multiresolution Matrix Factorization
Risi Kondor; Nedelina Teneva; Vikas Garg

- Track B - Bandits II (Location: 201-1, Chair: Miroslav Dudik)

10:50 Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques
Jérémie Mary; Philippe Preux; Olivier Nicol
11:10 Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem
Masrour Zoghi; Shimon Whiteson; Remi Munos; Maarten de Rijke
11:30 Spectral Bandits for Smooth Graph Functions
Michal Valko; Remi Munos; Branislav Kveton; Tomáš Kocák
11:50 Online Clustering of Bandits
Claudio Gentile; Shuai Li; Giovanni Zappella
12:10 Latent Bandits
Odalric-Ambrym Maillard; Shie Mannor

- Track C - Crowd-Sourcing (Location: 307, Chair: Ralf Herbrich)

10:50 Near-Optimally Teaching the Crowd to Classify
Adish Singla; Ilija Bogunovic; Gabor Bartok; Amin Karbasi; Andreas Krause
11:10 Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy
Dengyong Zhou; Qiang Liu; John Platt; Christopher Meek
11:30 Gaussian Process Classification and Active Learning with Multiple Annotators
Filipe Rodrigues; Francisco Pereira; Bernardete Ribeiro
11:50 Ensemble-Based Tracking: Aggregating Crowdsourced Structured Time Series Data
Naiyan Wang; Dit-Yan Yeung
12:10 Latent Confusion Analysis by Normalized Gamma Construction
Issei Sato; Hisashi Kashima; Hiroshi Nakagawa

- Track D - Manifolds and Graphs (Location: 201-2, Chair: Inderjit Dhillon)

10:50 Graph-based Semi-supervised Learning: Realizing Pointwise Smoothness Probabilistically
Yuan Fang; Kevin Chang; Hady Lauw
11:10 Wasserstein Propagation for Semi-Supervised Learning
Justin Solomon; Raif Rustamov; Leonidas Guibas; Adrian Butscher
11:30 Optimization Equivalence of Divergences Improves Neighbor Embedding
Zhirong Yang; Jaakko Peltonen; Samuel Kaski
11:50 Local Ordinal Embedding
Yoshikazu Terada; Ulrike von Luxburg
12:10 The f-Adjusted Graph Laplacian: a Diagonal Modification with a Geometric Interpretation
Sven Kurras; Ulrike von Luxburg; Gilles Blanchard

- Track E - Regularization and Lasso (Location: 201-3, Chair: Jieping Ye)

10:50 A Unified Framework for Consistency of Regularized Loss Minimizers
Jean Honorio; Tommi Jaakkola
11:10 Making the Most of Bag of Words: Sentence Regularization with Alternating Direction Method of Multipliers
Dani Yogatama; Noah Smith
11:30 Coupled Group Lasso for Web-Scale CTR Prediction in Display Advertising
Ling Yan; Wu-Jun Li; Gui-Rong Xue; Dingyi Han
11:50 Sample-based approximate regularization
Philip Bachman; Amir-Massoud Farahmand; Doina Precup
12:10 Safe Screening with Variational Inequalities and Its Application to Lasso
Jun Liu; Zheng Zhao; Jie Wang; Jieping Ye

- Track F - Nearest-Neighbors and Large-Scale Learning (Location: 305-2, Chair: Kilian Weinberger)

10:50 Densifying One Permutation Hashing via Rotation for Fast Near Neighbor Search
Anshumali Shrivastava; Ping Li
11:10 Coding for Random Projections
Ping Li; Michael Mitzenmacher; Anshumali Shrivastava
11:30 Nearest Neighbors Using Compact Sparse Codes
Anoop Cherian
11:50 Composite Quantization for Approximate Nearest Neighbor Search
Ting Zhang; Chao Du; Jingdong Wang
12:10 Circulant Binary Embedding
Felix Yu; Sanjiv Kumar; Yunchao Gong; Shih-Fu Chang

Tuesday June 24, 14:00

- Track A - Graphical Models II (Location: 305-1, Chair: Amir Globerson)

14:00 Low-density Parity Constraints for Hashing-Based Discrete Integration
Stefano Ermon; Carla Gomes; Ashish Sabharwal; Bart Selman
14:20 On Measure Concentration of Random Maximum A-Posteriori Perturbations
Francesco Orabona; Tamir Hazan; Anand Sarwate; Tommi Jaakkola
14:40 Learning Sum-Product Networks with Direct and Indirect Variable Interactions
Amirmohammad Rooshenas; Daniel Lowd
15:00 Multiple Testing under Dependence via Semiparametric Graphical Models
Jie Liu; Chunming Zhang; Elizabeth Burnside; David Page
15:20 Discrete Chebyshev Classifiers
Elad Eban; Elad Mezuman; Amir Globerson
15:40 Preserving Modes and Messages via Diverse Particle Selection
Jason Pacheco; Silvia Zuffi; Michael Black; Erik Sudderth

- Track B - Reinforcement Learning II (Location: 201-1, Chair: Emma Brunskill)

14:00 A new Q(lambda) with interim forward view and Monte Carlo equivalence
Rich Sutton; Ashique Rupam Mahmood; Doina Precup; Hado van Hasselt
14:20 True Online TD(lambda)
Harm van Seijen; Rich Sutton
14:40 Bias in Natural Actor-Critic Algorithms
Philip Thomas
15:00 Deterministic Policy Gradient Algorithms
David Silver; Guy Lever; Nicolas Heess; Thomas Degris; Daan Wierstra; Martin Riedmiller
15:20 Programming by Feedback
Riad Akrour; Marc Schoenauer; Jean-Christophe Souplet; Michele Sebag
15:40 Active Learning of Parameterized Skills
Bruno Da Silva; George Konidaris; Andrew Barto

- Track C - Topic Models (Location: 305-2, Chair: Jun Zhu)

14:00 Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis
Jian Tang; Zhaoshi Meng; Xuanlong Nguyen; Qiaozhu Mei; Ming Zhang
14:20 The Inverse Regression Topic Model
Maxim Rabinovich; David Blei
14:40 On Modelling Non-linear Topical Dependencies
Zhixing Li; Siqiang Wen; Juanzi Li; Peng Zhang; Jie Tang
15:00 Admixture of Poisson MRFs: A Topic Model with Word Dependencies
David Inouye; Pradeep Ravikumar; Inderjit Dhillon
15:20 Topic Modeling using Topics from Many Domains, Lifelong Learning and Big Data
Zhiyuan Chen; Bing Liu
15:40 Automated inference of point of view from user interactions in collective intelligence venues
Sanmay Das; Allen Lavoie

- Track D - Sparsity (Location: 201-2, Chair: Yan Liu)

14:00 Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization
Xiaotong Yuan; Ping Li; Tong Zhang
14:20 Forward-Backward Greedy Algorithms for General Convex Smooth Functions over A Cardinality Constraint
Ji Liu; Jieping Ye; Ryohei Fujimaki
14:40 Efficient Algorithms for Robust One-bit Compressive Sensing
Lijun Zhang; Jinfeng Yi; Rong Jin
15:00 Nonlinear Information-Theoretic Compressive Measurement Design
Liming Wang; Abolfazl Razi; Miguel Rodrigues; Robert Calderbank; Lawrence Carin
15:20 Elementary Estimators for High-Dimensional Linear Regression
Eunho Yang; Aurelie Lozano; Pradeep Ravikumar
15:40 Statistical-Computational Phase Transitions in Planted Models: The High-Dimensional Setting
Yudong Chen; Jiaming Xu

- Track E - Kernel Methods II (Location: 201-3, Chair: Le Song)

14:00 Kernel Mean Estimation and Stein Effect
Krikamol Muandet; Kenji Fukumizu; Bharath Sriperumbudur; Arthur Gretton; Bernhard Schoelkopf
14:20 A Kernel Independence Test for Random Processes
Kacper Chwialkowski; Arthur Gretton
14:40 Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels
Jiyan Yang; Vikas Sindhwani; Haim Avron; Michael Mahoney
15:00 A Unifying View of Representer Theorems
Andreas Argyriou; Francesco Dinuzzo
15:20 Efficient Approximation of Cross-Validation for Kernel Methods using Bouligand Influence Function
Yong Liu; Shali Jiang; Shizhong Liao

- Track F - Neural Theory and Spectral Methods (Location: 307, Chair: Anima Anandkumar)

14:00 Provable Bounds for Learning Some Deep Representations
Sanjeev Arora; Aditya Bhaskara; Rong Ge; Tengyu Ma
14:20 K-means recovers ICA filters when independent components are sparse
Alon Vinnikov; Shai Shalev-Shwartz
14:40 Learning Polynomials with Neural Networks
Alexandr Andoni; Rina Panigrahy; Gregory Valiant; Li Zhang
15:00 Anti-differentiating approximation algorithms:A case study with min-cuts, spectral, and flow
David Gleich; Michael Mahoney
15:20 Nonnegative Sparse PCA with Provable Guarantees
Megasthenis Asteris; Dimitris Papailiopoulos; Alexandros Dimakis
15:40 Finding Dense Subgraphs via Low-Rank Bilinear Optimization
Dimitris Papailiopoulos; Ioannis Mitliagkas; Alexandros Dimakis; Constantine Caramanis

Tuesday June 24, 16:20

- Track A - Networks and Graph-Based Learning II (Location: 305-1, Chair: Jerry Zhu)

16:20 Learning Graphs with a Few Hubs
Rashish Tandon; Pradeep Ravikumar
16:40 Global graph kernels using geometric embeddings
Fredrik Johansson; Vinay Jethava; Devdatt Dubhashi; Chiranjib Bhattacharyya
17:00 Efficient Label Propagation
Yasuhiro Fujiwara; Go Irie
17:20 Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm
Hadi Daneshmand; Manuel Gomez-Rodriguez; Le Song; Bernhard Schoelkopf
17:40 Learning from Contagion (Without Timestamps)
Kareem Amin; Hoda Heidari; Michael Kearns
18:00 Influence Function Learning in Information Diffusion Networks
Nan Du; Yingyu Liang; Maria Balcan; Le Song

- Track B - Online Learning II (Location: 307, Chair: Thorsten Joachims)

16:20 Tracking Adversarial Targets
Yasin Abbasi-Yadkori; Peter Bartlett; Varun Kanade
16:40 Sparse Reinforcement Learning via Convex Optimization
Zhiwei Qin; Weichang Li; Firdaus Janoos
17:00 Online Learning in Markov Decision Processes with Changing Cost Sequences
Travis Dick; Andras Gyorgy; Csaba Szepesvari
17:20 Linear Programming for Large-Scale Markov Decision Problems
Alan Malek; Yasin Abbasi-Yadkori; Peter Bartlett
17:40 Statistical analysis of stochastic gradient methods for generalized linear models
Panagiotis Toulis; Edoardo Airoldi; Jason Rennie
18:00 Preference-Based Rank Elicitation using Statistical Models: The Case of Mallows
Robert Busa-Fekete; Eyke Huellermeier; Balázs Szörényi

- Track C - Nonparametric Bayes II (Location: 305-2, Chair: John Paisley)

16:20 Bayesian Max-margin Multi-Task Learning with Data Augmentation
Chengtao Li; Jun Zhu; Jianfei Chen
16:40 Variational Inference for Sequential Distance Dependent Chinese Restaurant Process
Sergey Bartunov; Dmitry Vetrov
17:00 Pitfalls in the use of Parallel Inference for the Dirichlet Process
Yarin Gal; Zoubin Ghahramani
17:20 Fast Allocation of Gaussian Process Experts
Trung Nguyen; Edwin Bonilla
17:40 Scalable and Robust Bayesian Inference via the Median Posterior
Stanislav Minsker; Sanvesh Srivastava; Lizhen Lin; David Dunson
18:00 Nonparametric Estimation of Renyi Divergence and Friends
Akshay Krishnamurthy; Kirthevasan Kandasamy; Barnabas Poczos; Larry Wasserman

- Track D - Features and Feature Selection (Location: 201-1, Chair: Ruslan Salakhutdinov)

16:20 Elementary Estimators for Sparse Covariance Matrices and other Structured Moments
Eunho Yang; Aurelie Lozano; Pradeep Ravikumar
16:40 Robust Inverse Covariance Estimation under Noisy Measurements
Jun-Kun Wang; Shou-de Lin
17:00 Making Fisher Discriminant Analysis Scalable
Bojun Tu; Zhihua Zhang; Shusen Wang; Hui Qian
17:20 An Analysis of State-Relevance Weights and Sampling Distributions on L1-Regularized Approximate Linear Programming Approximation Accuracy
Gavin Taylor; Connor Geer; David Piekut
17:40 Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball
Andrew Miller; Luke Bornn; Ryan Adams; Kirk Goldsberry
18:00 Compact Random Feature Maps
Raffay Hamid; Ying Xiao; Alex Gittens; Dennis Decoste

- Track E - Optimization III (Location: 201-2, Chair: Zaid Harchaoui)

16:20 New Primal SVM Solver with Linear Computational Cost for Big Data Classifications
Feiping Nie; Yizhen Huang; Xiaoqian Wang; Heng Huang
16:40 Scaling SVM and Least Absolute Deviations via Exact Data Reduction
Jie Wang; Peter Wonka; Jieping Ye
17:00 Margins, Kernels and Non-linear Smoothed Perceptrons
Aaditya Ramdas; Javier Peña
17:20 Saddle Points and Accelerated Perceptron Algorithms
Adams Wei Yu; Fatma Kilinc-Karzan; Jaime Carbonell
17:40 Outlier Path: A Homotopy Algorithm for Robust SVM
Shinya Suzumura; Kohei Ogawa; Masashi Sugiyama; Ichiro Takeuchi
18:00 Optimal Budget Allocation: Theoretical Guarantee and Efficient Algorithm
Tasuku Soma; Naonori Kakimura; Kazuhiro Inaba; Ken-ichi Kawarabayashi

- Track F - Time Series and Sequences (Location: 201-3, Chair: Yan Liu)

16:20 Boosting multi-step autoregressive forecasts
Souhaib Ben Taieb; Rob Hyndman
16:40 Modeling Correlated Arrival Events with Latent Semi-Markov Processes
Wenzhao Lian; Vinayak Rao; Brian Eriksson; Lawrence Carin
17:00 Asymptotically consistent estimation of the number of change points in highly dependent time series
Azadeh Khaleghi; Daniil Ryabko
17:20 Effective Bayesian Modeling of Groups of Related Count Time Series
Nicolas Chapados
17:40 Stochastic Variational Inference for Bayesian Time Series Models
Matthew Johnson; Alan Willsky
18:00 Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models
Robert McGibbon; Bharath Ramsundar; Mohammad Sultan; Gert Kiss; Vijay Pande
2013-2014 ICML | International Conference on Machine Learning