Videos are available on TechTalks.
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |