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ORCID32nd UAI 2016: New York City, NY, USA
- Alexander Ihler, Dominik Janzing:
Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, UAI 2016, June 25-29, 2016, New York City, NY, USA. AUAI Press 2016, ISBN 978-0-9966431-1-5 - Adrian Weller:
Characterizing Tightness of LP Relaxations by Forbidding Signed Minors. - Shuangyin Li, Rong Pan, Yu Zhang, Qiang Yang:
Correlated Tag Learning in Topic Model. - Pengtao Xie, Jin Kyu Kim, Yi Zhou, Qirong Ho, Abhimanu Kumar, Yaoliang Yu, Eric P. Xing:
Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting. - Krzysztof Chalupka, Tobias Bischoff, Frederick Eberhardt, Pietro Perona:
Unsupervised Discovery of El Nino Using Causal Feature Learning on Microlevel Climate Data. - Changying Du, Changde Du, Guoping Long, Qing He, Yucheng Li:
Online Bayesian Multiple Kernel Bipartite Ranking. - José M. Peña:
Alternative Markov and Causal Properties for Acyclic Directed Mixed Graphs. - Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei:
Overdispersed Black-Box Variational Inference. - Bo Liu, Luwan Zhang, Ji Liu:
Dantzig Selector with an Approximately Optimal Denoising Matrix and its Application in Sparse Reinforcement Learning. - Jan Leike, Tor Lattimore, Laurent Orseau, Marcus Hutter:
Thompson Sampling is Asymptotically Optimal in General Environments. - Felix Leibfried, Daniel A. Braun:
Bounded Rational Decision-Making in Feedforward Neural Networks. - Peng Yang, Peilin Zhao, Zhen Hai, Wei Liu, Steven C. H. Hoi, Xiaoli Li:
Efficient Multi-Class Selective Sampling on Graphs. - James R. Foulds, Joseph Geumlek, Max Welling, Kamalika Chaudhuri:
On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis. - Sanghack Lee, Vasant G. Honavar:
A Characterization of Markov Equivalence Classes of Relational Causal Models under Path Semantics. - Yoad Lewenberg, Yoram Bachrach, Lucas Bordeaux, Pushmeet Kohli:
Political Dimensionality Estimation Using a Probabilistic Graphical Model. - Nebojsa Jojic, Alessandro Perina, Dongwoo Kim:
Hierarchical learning of grids of microtopics. - Ralf Eggeling, Mikko Koivisto:
Pruning Rules for Learning Parsimonious Context Trees. - Dongeun Lee, Rafael Lima, Jaesik Choi:
Improving Imprecise Compressive Sensing Models. - Laurent Orseau, Stuart Armstrong:
Safely Interruptible Agents. - Tahrima Rahman, Vibhav Gogate
:
Merging Strategies for Sum-Product Networks: From Trees to Graphs. - Julien-Charles Levesque, Christian Gagné, Robert Sabourin:
Bayesian Hyperparameter Optimization for Ensemble Learning. - Marek Petrik, Ronny Luss:
Interpretable Policies for Dynamic Product Recommendations. - Julie Nutini, Behrooz Sepehry, Issam H. Laradji, Mark Schmidt, Hoyt A. Koepke, Alim Virani:
Convergence Rates for Greedy Kaczmarz Algorithms, and Randomized Kaczmarz Rules Using the Orthogonality Graph. - Jean Honorio, Tommi S. Jaakkola:
Structured Prediction: From Gaussian Perturbations to Linear-Time Principled Algorithms. - Dilin Wang, John W. Fisher III, Qiang Liu:
Efficient Observation Selection in Probabilistic Graphical Models Using Bayesian Lower Bounds. - Hanzhang Hu, Alexander Grubb, J. Andrew Bagnell, Martial Hebert:
Efficient Feature Group Sequencing for Anytime Linear Prediction. - Jan Leike, Jessica Taylor, Benya Fallenstein:
A Formal Solution to the Grain of Truth Problem. - Mohammad Gheshlaghi Azar, Eva L. Dyer, Konrad P. Körding:
Convex Relaxation Regression: Black-Box Optimization of Smooth Functions by Learning Their Convex Envelopes. - Hugo Gilbert, Bruno Zanuttini, Paul Weng, Paolo Viappiani, Esther Nicart:
Model-Free Reinforcement Learning with Skew-Symmetric Bilinear Utilities. - Shi Zong, Hao Ni, Kenny Sung, Nan Rosemary Ke, Zheng Wen, Branislav Kveton:
Cascading Bandits for Large-Scale Recommendation Problems. - Alex Gaunt, Diana Borsa, Yoram Bachrach:
Training Neural Nets to Aggregate Crowdsourced Responses. - Tianfan Fu, Luo Luo, Zhihua Zhang:
Quasi-Newton Hamiltonian Monte Carlo. - Chun-Liang Li, Barnabás Póczos:
Utilize Old Coordinates: Faster Doubly Stochastic Gradients for Kernel Methods. - Xiangli Chen, Mathew Monfort, Brian D. Ziebart, Peter Carr:
Adversarial Inverse Optimal Control for General Imitation Learning Losses and Embodiment Transfer. - Trung Le, Phuong Duong, Mi Dinh, Tu Dinh Nguyen, Vu Nguyen, Dinh Q. Phung:
Budgeted Semi-supervised Support Vector Machine . - Frédéric Koriche:
Online Forest Density Estimation. - Amar Shah, Zoubin Ghahramani:
Markov Beta Processes for Time Evolving Dictionary Learning. - Xi Tan, Syed A. Z. Naqvi, Yuan (Alan) Qi, Katherine A. Heller, Vinayak A. Rao:
Content-based Modeling of Reciprocal Relationships using Hawkes and Gaussian Processes. - Lu Tian, Pan Xu, Quanquan Gu:
Forward Backward Greedy Algorithms for Multi-Task Learning with Faster Rates. - Wen Sun, Roberto Capobianco, Geoffrey J. Gordon, J. Andrew Bagnell, Byron Boots:
Learning to Smooth with Bidirectional Predictive State Inference Machines. - Scott Yang, Mehryar Mohri:
Adaptive Algorithms and Data-Dependent Guarantees for Bandit Convex Optimization. - Seth R. Flaxman, Dino Sejdinovic, John P. Cunningham, Sarah Filippi:
Bayesian Learning of Kernel Embeddings. - Junxiang Chen, Jennifer G. Dy:
A Generative Block-Diagonal Model for Clustering. - Francois Fagan, Jalaj Bhandari, John P. Cunningham:
Elliptical Slice Sampling with Expectation Propagation. - Joseph Futoma, Mark P. Sendak, Blake Cameron, Katherine A. Heller:
Scalable Joint Modeling of Longitudinal and Point Process Data for Disease Trajectory Prediction and Improving Management of Chronic Kidney Disease. - Arto Klami, Aditya Jitta:
Probabilistic Size-constrained Microclustering. - Hans Kersting, Philipp Hennig:
Active Uncertainty Calibration in Bayesian ODE Solvers. - Kareem Amin, Michael P. Wellman, Satinder Singh:
Gradient Methods for Stackelberg Games. - Jianhui Chen, Tianbao Yang, Qihang Lin, Lijun Zhang, Yi Chang:
Optimal Stochastic Strongly Convex Optimization with a Logarithmic Number of Projections. - David P. Wipf, Yue Dong, Bo Xin:
Subspace Clustering with a Twist. - David M. Pennock, Vasilis Syrgkanis, Jennifer Wortman Vaughan:
Bounded Rationality in Wagering Mechanisms. - Raghuraman Gopalan:
Bridging Heterogeneous Domains With Parallel Transport For Vision and Multimedia Applications. - Harikrishna Narasimhan, David C. Parkes:
A General Statistical Framework for Designing Strategy-proof Assignment Mechanisms. - Jinghui Chen, Quanquan Gu:
Accelerated Stochastic Block Coordinate Gradient Descent for Sparsity Constrained Nonconvex Optimization. - Patrice Perny, Paolo Viappiani, Abdellah Boukhatem:
Incremental Preference Elicitation for Decision Making Under Risk with the Rank-Dependent Utility Model. - Tomás Kocák, Gergely Neu, Michal Valko:
Online learning with Erdos-Renyi side-observation graphs. - Leonard J. Schulman, Piyush Srivastava:
Stability of Causal Inference. - David T. Arbour, Katerina Marazopoulou, David D. Jensen:
Inferring Causal Direction from Relational Data. - Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, Masashi Sugiyama:
Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions. - Roy Fox, Ari Pakman, Naftali Tishby:
Taming the Noise in Reinforcement Learning via Soft Updates. - Mustafa Anil Koçak, Dennis E. Shasha, Elza Erkip:
Conjugate Conformal Prediction for Online Binary Classification. - Dmitry Malioutov, Abhishek Kumar, Ian En-Hsu Yen:
Large-scale Submodular Greedy Exemplar Selection with Structured Similarity Matrices. - Md Amran Siddiqui, Alan Fern, Thomas G. Dietterich, Shubhomoy Das:
Finite Sample Complexity of Rare Pattern Anomaly Detection. - Chao Lan, Jianxin Wang, Jun Huan:
Towards a Theoretical Understanding of Negative Transfer in Collective Matrix Factorization. - Qiang Liu:
Importance Weighted Consensus Monte Carlo for Distributed Bayesian Inference. - An T. Nguyen, Byron C. Wallace, Matthew Lease:
A Correlated Worker Model for Grouped, Imbalanced and Multitask Data. - Matej Balog, Balaji Lakshminarayanan, Zoubin Ghahramani, Daniel M. Roy, Yee Whye Teh:
The Mondrian Kernel. - Jalal Etesami, Negar Kiyavash, Kun Zhang, Kushagra Singhal:
Learning Network of Multivariate Hawkes Processes: A Time Series Approach. - Lirong Xia:
Bayesian Estimators As Voting Rules. - Craig Boutilier, Tyler Lu:
Budget Allocation using Weakly Coupled, Constrained Markov Decision Processes. - Paul K. Rubenstein, Kacper Chwialkowski, Arthur Gretton:
A Kernel Test for Three-Variable Interactions with Random Processes. - Antonio Sutera, Gilles Louppe, Vân Anh Huynh-Thu, Louis Wehenkel, Pierre Geurts:
Context-dependent feature analysis with random forests. - Daniele Calandriello, Alessandro Lazaric, Michal Valko:
Analysis of Nyström method with sequential ridge leverage scores. - Tianxiang Gao, Vladimir Jojic:
Degrees of Freedom in Deep Neural Networks. - Viet Huynh, Dinh Q. Phung, Svetha Venkatesh, XuanLong Nguyen, Matthew D. Hoffman, Hung Hai Bui:
Scalable Nonparametric Bayesian Multilevel Clustering. - Chune Li, Yongyi Mao, Richong Zhang, Jinpeng Huai:
On Hyper-Parameter Estimation In Empirical Bayes: A Revisit of The MacKay Algorithm. - Morteza Haghir Chehreghani, Mostafa Haghir Chehreghani:
Modeling Transitivity in Complex Networks. - Mitchell McIntire, Daniel Ratner, Stefano Ermon:
Sparse Gaussian Processes for Bayesian Optimization. - Akshay Balsubramani, Aaditya Ramdas:
Sequential Nonparametric Testing with the Law of the Iterated Logarithm. - Yves-Laurent Kom Samo, Alexander Vervuurt:
Stochastic Portfolio Theory: A Machine Learning Approach. - Muthukumaran Chandrasekaran, Adam Eck, Prashant Doshi, Leenkiat Soh:
Individual Planning in Open and Typed Agent Systems. - Brooks Paige, Dino Sejdinovic, Frank D. Wood:
Super-Sampling with a Reservoir. - Nan Rong, Joseph Y. Halpern, Ashutosh Saxena:
MDPs with Unawareness in Robotics. - Kun Zhang, Jiji Zhang, Biwei Huang, Bernhard Schölkopf, Clark Glymour:
On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection. - Deepak Venugopal, Somdeb Sarkhel, Kyle Cherry:
Non-parametric Domain Approximation for Scalable Gibbs Sampling in MLNs. - DJ Strouse, David J. Schwab:
The Deterministic Information Bottleneck.
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