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UAI 2016 - Program Overview

Saturday June 25th, 2016 (Tutorials)
08:30 - 10:20Discrete Sampling and Integration in High Dimensional Spaces
Supratik Chakraborty, Kuldeep S. Meel, and Moshe Y. Vardi.
10:20 - 10:40coffee break
10:40 - 12:30Parallel and High-performance Computing for Speeding up Machine Learning Algorithms
Anshul Gupta, IBM T.J. Watson Research Center; Prabhanjan Kambadur, Bloomberg L.P.
12:30 - 14:30Lunch break
14:30 - 16:20Integrative Logic-Based Causal Discovery
Sofia Triantafillou and Ioannis Tsamardinos
16:20 - 16:40break
16:40 - 18:30Reasoning Under Uncertainty with Subjective Logic
Audun Jøsang

See tutorials for more details.

Sunday June 26th, 2016
07:30 - 08:30Opening hours registration desk
08:30 - 08:40welcome
08:40 - 09:40keynote talk: Steven Low
09:40 - 10:30Oral session: Bayesian estimation
10:30 - 10:50coffee break
10:50 - 12:05Oral Session: Reinforcement learning
12:04 - 14:05Lunch break
14:05 - 15:45Oral Session: optimization
15:45 - 16:00coffee break
16:00 - 17:15Oral Session: crowdsourcing
17:15 - 17:45Poster Spotlights
17:45 - 19:45Poster Session

Monday June 27th, 2016
08:30 - 09:30Keynote talk: Steffen Lauritzen
09:30 - 10:20Oral Session: causality I
10:20 - 10:50coffee break
10:50 - 11:40Oral Session: graphical models
11:40 - 12:05Inference challenge
12:04 - 14:05Lunch break
14:05 - 14:55Oral Session: causality II
14:55 - 15:15coffee break
15:15 - 15:45Poster Spotlights
15:45 - 17:45Poster Session
18:45 - 22:30Banquet, talk: Farhan Feroz
(Same room as main conference)

Tuesday June 28th, 2016
08:30 - 09:30Keynote talk: Andrew McCallum
09:30 - 10:20Oral Session: kernel methods
10:20 - 10:50coffee break
10:50 - 12:05Oral Session: unsupervised learning and density estimation
12:04 - 14:05Lunch break
14:05 - 15:20Oral Session: supervised learning
15:20 - 15:50coffee break
15:50 - 16:20Poster spotlights
16:20 - 16:50Business meeting
16:20 - 18:20Poster Session

Wednesday June 29th, 2016 (Workshops)
See workshops for more details.

Detailed Program

Sunday June 26th, 2016
07:30 - 08:30Opening hours registration desk
08:30 - 08:40welcome
08:40 - 09:40keynote talk: Steven Low
Online optimization of power networks
09:40 - 10:30Oral Session: Bayesian estimation
  1. ID: 242 | Bayesian Estimators As Voting Rules | Lirong Xia, (Slides)
  2. ID: 45 | On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis | James Foulds, UC San Diego; Joseph Geumlek, UC San Diego; Max Welling, University of Amsterdam; Kamalika Chaudhuri, (Slides)
10:30 - 10:50coffee break
10:50 - 12:05Oral Session: Reinforcement learning
  1. ID: 91 | Model-Free Reinforcement Learning with Skew-Symmetric Bilinear Utilities | Hugo Gilbert, LIP6-UPMC; Bruno Zanuttini, University of Caen, France; Paul Weng, SYSU-CMU JIE; Paolo Viappiani, Lip6, Paris; Esther Nicart, Cordon Electronics DS2i
  2. ID: 219 | Taming the Noise in Reinforcement Learning via Soft Updates | Roy Fox, HUJI; Ari Pakman, Columbia University; Naftali Tishby, HUJI
  3. ID: 20 | (Best student paper award) Thompson Sampling is Asymptotically Optimal in General Environments | Jan Leike, Australian National University; Tor Lattimore, University of Alberta; Laurent Orseau, Google DeepMind; Marcus Hutter, (Slides)
12:04 - 14:05Lunch break
14:05 - 15:45Oral Session: Optimization
  1. ID: 269 | Sparse Gaussian Processes for Bayesian Optimization | Mitchell McIntire, Stanford University; Daniel Ratner, SLAC National Accelerator Laboratory; Stefano Ermon,
  2. ID: 90 | Convex Relaxation Regression: Black-Box Optimization of Smooth Functions by Learning Their Convex Envelopes | Mohamm Gheshlaghi Azar, Northwestern University; Eva Dyer, Northwestern University; Konrad Kording, Northwestern University (Slides)
  3. ID: 168 | Optimal Stochastic Strongly Convex Optimization with a Logarithmic Number of Projections | Jianhui Chen, Yahoo; Tianbao Yang, University of Iowa; Lijun Zhang, Nanjing University; Qihang Lin, ; Yi Chang, Yahoo! (Slides)
  4. ID: 246 | Budget Allocation using Weakly Coupled, Constrained Markov Decision Processes | Craig Boutilier, Google; Tyler Lu, Google
15:45 - 16:00coffee break
16:00 - 17:15Oral Session: Crowdsourcing
  1. ID: 99 | Training Neural Nets to Aggregate Crowdsourced Responses | Alex Gaunt, Microsoft Research; Diana Borsa, UCL; Yoram Bachrach, Microsoft Research
  2. ID: 235 | A Correlated Worker Model for Grouped, Imbalanced and Multitask Data | An Nguyen, University of Texas at Austin; Byron Wallace, University of Texas at Austin; Matthew Lease, University of Texas at Austin
  3. ID: 185 | Bounded Rationality in Wagering Mechanisms | David Pennock, Microsoft Research; Vasilis Syrgkanis, Microsoft Research; Jennifer Wortman Vaughan, Microsoft Research
17:15 - 17:45Poster Spotlight
  1. ID: 120 | Markov Beta Processes for Time Evolving Dictionary Learning | Amar Shah, University of Cambridge; Zoubin Ghahramani, Cambridge University
  2. ID: 14 | Alternative Markov and Causal Properties for Acyclic Directed Mixed Graphs | Jose Pena,
  3. ID: 66 | Pruning Rules for Learning Parsimonious Context Trees | Ralf Eggeling, University of Helsinki; Mikko Koivisto,
  4. ID: 71 | Merging Strategies for Sum-Product Networks: From Trees to Graphs | Tahrima Rahman, University of Texas at Dallas; Vibhav Gogate, The University of Texas at Dallas
  5. ID: 77 | Convergence Rates for Greedy Kaczmarz Algorithms, and Randomized Kaczmarz Rules Using the Orthogonality Graph | Julie Nutini, University of British Columbia; Behrooz Sepehry, University of British Columbia; Issam Laradji, University of British Columbia; Alim Virani, ; Mark Schmidt, University of British Columbia; Hoyt Koepke, Dato
  6. ID: 86 | Efficient Feature Group Sequencing for Anytime Linear Prediction | Hanzhang Hu, Carnegie Mellon University; Alexander Grubb, ; J. Andrew Bagnell, ; Martial Hebert,
  7. ID: 110 | Budgeted Semi-supervised Support Vector Machine | Mi Dinh, HCMc University of Pedagogy, Vietnam; Phuong Duong, HCMc University of Pedagogy, Vietnam; Trung Le, HCMc University of Pedagogy; Tu Nguyen, PRaDA, Deakin university, Australia; Vu Nguyen, PRaDA, Deakin university, Australia; Dinh Phung, PRaDA, Deakin university, Australia
  8. ID: 124 | Content-based Modeling of Reciprocal Relationships using Hawkes and Gaussian Processes | Xi Tan, Purdue University; Syed Naqvi, Purdue University; Yuan (Alan) Qi, Purdue University; Katherine Heller, Duke University; Vinayak Rao, Purdue University;
  9. ID: 157 | Elliptical Slice Sampling with Expectation Propagation | Francois Fagan, Columbia University; Jalaj Bhandari, Columbia University; John Cunningham, Columbia University
  10. ID: 163 | Active Uncertainty Calibration in Bayesian ODE Solvers | Hans Kersting, MPI for Intelligent Systems; Philipp Hennig, MPI for Intelligent Systems
  11. ID: 183 | Subspace Clustering with a Twist | David Wipf, Microsoft Research; Yue Dong, Microsoft Research; Bo Xin, Peking University
  12. ID: 193 | A General Statistical Framework for Designing Strategy-proof Assignment Mechanisms | Harikrishna Narasimhan, Harvard University; David Parkes, Harvard University
  13. ID: 212 | Online learning with Erdos-Renyi side-observation graphs | Tomáš Kocák, Inria Lille - Nord Europe; gergely Neu, ; Michal Valko, Inria Lille - Nord Europe
  14. ID: 218 | Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions | Reza Babanezhad Harikandeh, UBC; Mark Schmidt, University of British Columbia; Mohammad Emtiyaz Khan, ; Wu Lin, ; Masashi Sugiyama,
  15. ID: 225 | Large-scale Submodular Greedy Exemplar Selection with Structured Similarity Matrices | Dmitry Malioutov, IBM Research; Abhishek Kumar, IBM Research; Ian Yen, University of Texas at Austin
  16. ID: 229 | Importance Weighted Consensus Monte Carlo for Distributed Bayesian Inference | Qiang Liu, Dartmouth College
  17. ID: 284 | Stochastic Portfolio Theory: A Machine Learning Approach | Yves-Laurent Kom Samo, University of Oxford; Alexander Vervuurt, University of Oxford
  18. ID: 294 | MDPs with Unawareness in Robotics | Nan Rong, Cornell University; Joseph Halpern, Cornell University; Ashutosh Saxena, Cornell University
  19. ID: 319 | The deterministic information bottleneck | DJ Strouse, Princeton University; david Schwab, Northwestern University
17:45 - 19:45Poster Session
Will include all papers presented today

Monday June 27th, 2016
08:30 - 09:30Keynote talk: Steffen Lauritzen
Total positivity and Markov structures
09:30 - 10:20Oral Session: Causality I
  1. ID: 11 | Unsupervised Discovery of El Nino Using Causal Feature Learning on Microlevel Climate Data | Krzysztof Chalupka, Caltech; Tobias Bischoff, Caltech; Frederick Eberhardt, ; Pietro Perona, Caltech
  2. ID: 305 | On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection | Kun Zhang, CMU; Jiji Zhang, ; Biwei Huang, MPI; Bernhard Schoelkopf, ; Clark Glymour, CMU
10:20 - 10:50coffee break
10:50 - 11:40Oral Session: Graphical models
  1. ID: 83 | Efficient Observation Selection in Probabilistic Graphical Models Using Bayesian Lower Bounds | Dilin Wang, Dartmouth College; John Fisher III, MIT; Qiang Liu, Dartmouth College
  2. ID: 15 | Overdispersed Black-Box Variational Inference | Francisco Ruiz, Columbia University; Michalis Titsias, Athens University of Economics and Business; david Blei, Columbia University (Slides)
11:40 - 12:05Inference challenge
12:04 - 14:05Lunch break
14:05 - 14:55Oral Session: Causality II
  1. ID: 214 | (Best paper award) Stability of Causal Inference | Leonard Schulman, California Institute of Technology; Piyush Srivastava, California Institute of Techno (Slides)
  2. ID: 46 | A Characterization of Markov Equivalence Classes for Relational Causal Model with Path Semantics | Sanghack Lee, Penn State University; Vasant Honavar, Penn State University (Slides)
14:55 - 15:15coffee break
15:15 - 15:45Poster Spotlight
  1. ID: 1 | Characterizing Tightness of LP Relaxations by Forbidding Signed Minors | Adrian Weller, University of Cambridge
  2. ID: 13 | Online Bayesian Multiple Kernel Bipartite Ranking | Changde Du, ; Changying Du, Institute of Software, CAS; Ali Luo, ; Guoping Long, ; Qing He, ; Yucheng Li,
  3. ID: 16 | Optimal Denoising Matrix in Dantzig Selector | Bo Liu, Auburn University; Luwan Zhang, Department of Statistics, University of Wisconsin-Madison; Ji Liu, University of Rochester
  4. ID: 34 | An Efficient Multi-Class Selective Sampling Algorithm on Graphs | Peng Yang, Institute for Infocomm Researc; Peilin Zhao, ; Zhen Hai, Institute for Infocomm Research; Wei Liu, ; Xiao-Li Li, ; Steven C.H. Hoi,
  5. ID: 61 | Political Dimensionality Estimation Using a Probabilistic Graphical Model | Yoad Lewenberg, The Hebrew University of Jerus; Yoram Bachrach, Microsoft Research ; Lucas Bordeaux, ; Pushmeet Kohli,
  6. ID: 67 | Improving Imprecise Compressive Sensing Models | Dongeun Lee, UNIST; Rafael de Lima, ; Jaesik Choi,
  7. ID: 73 | Bayesian Hyperparameter Optimization for Ensemble Learning | Julien-Charles Levesque, Universite Laval; Christian Gagne, Universite Laval; Robert Sabourin, Ecole de Technologie Superieure
  8. ID: 87 | A Formal Solution to the Grain of Truth Problem | Jan Leike, Australian National University; Jessica Taylor, Machine Intelligence Research Institute; Benya Fallenstein, Machine Intelligence Research Institute
  9. ID: 96 | Cascading Bandits for Large-Scale Recommendation Problems | Shi Zong, CMU; Hao Ni, CMU; Kenny Sung, CMU; Rosemary Ke, University of Montreal; Zheng Wen, Adobe Research; Branislav Kveton, Adobe Research
  10. ID: 102 | Quasi-Newton Hamiltonian Monte Carlo | Tianfan Fu, Shanghai Jiao Tong University; Luo Luo, Shanghai Jiao Tong University; Zhihua Zhang, Shanghai Jiao Tong University
  11. ID: 105 | Utilize Old Coordinates: Faster Doubly Stochastic Gradients for Kernel Methods | Chun-Liang Li, Carnegie Mellon University; Barnabas Poczos, Carnegie Mellon University
  12. ID: 135 | Forward Backward Greedy Algorithms for Multi-Task Learning with Faster Rates | Lu Tian, University of Virginia; Pan Xu, University of Virginia; Quanquan Gu,
  13. ID: 160 | Scalable Joint Modeling of Longitudinal and Point Process Data for Disease Trajectory Prediction and Improving Management of Chronic Kidney Disease | Joseph Futoma, Duke University; Mark Sendak, Duke University; Blake Cameron, Duke University; Katherine Heller, Duke University
  14. ID: 167 | Gradient Methods for Stackelberg Games | Kareem Amin, University of Michigan; Michael Wellman, ; Satinder Singh,
  15. ID: 202 | Accelerated Stochastic Block Coordinate Gradient Descent for Sparsity Constrained Nonconvex Optimization | Jinghui Chen, University of Virginia; Quanquan Gu,
  16. ID: 253 | Context-dependent feature analysis with random forests | Antonio Sutera, University of Liège; Gilles Louppe, CERN; Vân Anh Huynh-Thu, University of Liège; Louis Wehenkel, University of Liège; Pierre Geurts, University of Liège
  17. ID: 262 | Scalable Nonparametric Bayesian Multilevel Clustering | Viet Huynh, Deakin University; Dinh Phung, PRaDA, Deakin university, Australia; Svetha Venkatesh, Deakin University; Long Nguyen, ; Matthew Hoffman, ; Hung Bui, Adobe Research
  18. ID: 286 | Individual Planning in Open and Typed Agent Systems | Muthukumaran Chandrasekaran, University of Georgia; Adam Eck, University of Nebraska; Prashant Doshi, University of Georgia; Leenkiat Soh, University of Nebraska
  19. ID: 239 | Learning Network of Multivariate Hawkes Processes: A Time Series Approach | Jalal Etesami, UIUC; negar kiyavash, uiuc; Kun Zhang, cmu; Kushagra Singhal, uiuc
  20. ID: 247 | A Kernel Test for Three-Variable Interactions with Random Processes | Paul Rubenstein, Cambridge/MPI Tuebingen; Kacper Chwialkowski, UCL / Gatsby Unit; Arthur Gretton, Gatsby Unit, University College London
  21. ID: 257 | Degrees of Freedom in Deep Neural Networks | Tianxiang Gao, University of North Carolina a; Vladimir Jojic, University of North Carolina at Chapel Hill
  22. ID: 268 | Modeling Transitivity in Complex Networks | Morteza Haghir Chehreghani, Xerox Research Centre Europe; Mostafa Haghir Chehreghani, KU Leuven
15:45 - 17:45Poster Session
Will include all papers presented today
18:45 - 22:30Banquet talk: Farhan Feroz
Statistics, Machine Learning & the Detection of Gravitational Waves
Note: the banquet will be held in the same room as the main conference talks.

Tuesday June 28th, 2016
08:30 - 09:30Keynote talk: Andrew McCallum
Structured Prediction and Deep Learning
09:30 - 10:20Oral Session: Kernel methods
  1. ID: 236 | The Mondrian Kernel | Matej Balog, University of Cambridge; Balaji Lakshminarayanan, ; Daniel Roy, University of Toronto; Yee Whye Teh, University of Oxford (Slides)
  2. ID: 145 | Bayesian Learning of Kernel Embeddings | Seth Flaxman, Oxford; Dino Sejdinovic, University of Oxford; John Cunningham, Columbia University; Sarah Filippi, Oxford (Slides)
10:20 - 10:50coffee break
10:50 - 12:05Oral Session: Unsupervised learning and density estimation
  1. ID: 226 | Finite Sample Complexity of Rare Pattern Anomaly Detection | Md Amran Siddiqui, Oregon Sate University; Alan Fern, ; Thomas Dietterich, Oregon State University; Shubhomoy Das, Oregon State University (Slides)
  2. ID: 116 | Online Forest Density Estimation | Frederic Koriche, CRIL (Slides)
  3. ID: 155 | A Generative Block-Diagonal Model for Clustering | Junxiang Chen, Northeastern University; Jennifer Dy,
12:04 - 14:05Lunch break
14:05 - 15:20Oral Session: Supervised learning
  1. ID: 31 | Bounded Rational Decision-Making in Feedforward Neural Networks | Felix Leibfried, Max Planck Society; Daniel Braun, Max Planck Society
  2. ID: 186 | Bridging Heterogeneous Domains With Parallel Transport For Vision and Multimedia Applications | Raghuraman Gopalan, AT&T Research
  3. ID: 79 | Structured Prediction: From Gaussian Perturbations to Linear-Time Principled Algorithms | Jean Honorio, Purdue University; Tommi Jaakkola, (Slides)
15:20 - 15:50coffee break
15:50 - 16:20Poster Spotlight
  1. ID: 5 | Correlated Tag Learning in Topic Model | Shuangyin Li, HKUST; Rong Pan, Sun Yat-sen University; Yu Zhang, ; Qiang Yang, Hong Kong University of Science and Technology
  2. ID: 10 | Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting | Pengtao Xie, Carnegie Mellon University; Jin Kyu Kim, Carnegie Mellon University; Yi Zhou, Syracuse University; Qirong Ho, Institute for Infocomm Research, ASTAR; Abhimanu Kumar, Groupon Inc.; Yaoliang Yu, ; Eric Xing, Carnegie Mellon University
  3. ID: 64 | Hierarchical learning of grids of microtopics | Alessandro Perina, Microsoft; Nebojsa Jojic,
  4. ID: 68 | Safely Interruptible Agents | Laurent Orseau, Google DeepMind; Stuart Armstrong, Future of Humanity Institute, Oxford, UK
  5. ID: 74 | Interpretable Policies for Dynamic Product Recommendations | Marek Petrik, IBM; Ronny Luss, IBM
  6. ID: 106 | Adversarial Inverse Optimal Control for General Imitation Learning Losses and Embodiment Transfer | Xiangli Chen, UIC; Mathew Monfort, ; Brian Ziebart, ; Peter Carr,
  7. ID: 138 | Learning to Smooth with Bidirectional Predictive State Inference Machines | Wen Sun, Carnegie Mellon University; Roberto Capobianco, Sapienza University of Rome; J. Andrew Bagnell, ; Byron Boots, Georgia Institute of Technology; Geoffrey J. Gordon, Carnegie Mellon University
  8. ID: 143 | Adaptive Algorithms and Data-Dependent Guarantees for Bandit Convex Optimization | Scott Yang, Courant Institute of Mathemati; Mehryar Mohri, Courant Institute of Mathematical Sciences
  9. ID: 161 | Probabilistic Size-constrained Microclustering | Arto Klami, ; Aditya Jitta, University of Helsinki
  10. ID: 204 | Incremental Preference Elicitation for Decision Making Under Risk with the Rank-Dependent Utility Model | Patrice Perny, LIP6; Paolo Viappiani, Lip6, Paris; abdellah Boukhatem, LIP6
  11. ID: 217 | Inferring Causal Direction from Relational Data | David Arbour, University of Massachusetts Am; Katerina Marazopoulou, University of Massachusetts Amhe; David Jensen,
  12. ID: 223 | Conjugate Conformal Prediction for Online Binary Classification | Mustafa Kocak, NYU Tandon SoE; Dennis Shasha, Courant Institute of Mathematical Sciences New York University; Elza Erkip, NYU Tandon School of Engineering
  13. ID: 227 | Towards a Theoretical Understanding of Negative Transfer in Collective Matrix Factorization | Chao Lan, University of Kansas; Jun Huan, University of Kansas
  14. ID: 255 | Analysis of Nystrom method with sequential ridge leverage scores | Daniele Calandriello, INRIA Lille - Nord Europe; Alessandro Lazaric, ; Michal Valko, Inria Lille - Nord Europe
  15. ID: 267 | On Hyper-Parameter Estimation In Empirical Bayes: A Revisit of The MacKay Algorithm | Chune Li, Beihang University; Yongyi Mao, University of Ottawa; Richong Zhang, Beihang University; Jinpeng Huai, Beihang University
  16. ID: 270 | Sequential Nonparametric Testing with the Law of the Iterated Logarithm | Akshay Balsubramani, UC San Diego; Aaditya Ramdas, UC Berkeley
  17. ID: 293 | Random Features for Online Sampling with a Reservoir | Brooks Paige, University of Oxford; Dino Sejdinovic, University of Oxford; Frank Wood, University of Oxford
  18. ID: 308 | Non-parametric Domain Approximation for Scalable Gibbs Sampling in MLNs | Deepak Venugopal, The University of Memphis; Somdeb Sarkhel, ; Kyle Cherry,
16:20 - 16:50Business meeting
16:20 - 18:20Poster Session
Will include all papers presented today


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