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ORCID19. AISTATS 2016: Cadiz, Spain
- Arthur Gretton, Christian C. Robert:
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016, Cadiz, Spain, May 9-11, 2016. JMLR Workshop and Conference Proceedings 51, JMLR.org 2016
Accepted Papers
- Mario Lucic, Olivier Bachem, Andreas Krause:
Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures. 1-9 - Alexandra Carpentier, Michal Valko:
Revealing Graph Bandits for Maximizing Local Influence. 10-18 - Anastasios Kyrillidis, Bubacarr Bah, Rouzbeh Hasheminezhad, Quoc Tran-Dinh, Luca Baldassarre, Volkan Cevher:
Convex Block-sparse Linear Regression with Expanders - Provably. 19-27 - Daniel Ritchie
, Andreas Stuhlmüller, Noah D. Goodman:
C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching. 28-37 - Adrian Weller, Justin Domke:
Clamping Improves TRW and Mean Field Approximations. 38-46 - Adrian Weller, Mark Rowland, David A. Sontag:
Tightness of LP Relaxations for Almost Balanced Models. 47-55 - Chris J. Oates, Mark A. Girolami:
Control Functionals for Quasi-Monte Carlo Integration. 56-65 - Markus Schneider:
Probability Inequalities for Kernel Embeddings in Sampling without Replacement. 66-74 - Nicolas Goix, Anne Sabourin, Stéphan Clémençon:
Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking. 75-83 - Yale Chang, Yi Li, A. Adam Ding, Jennifer G. Dy:
A Robust-Equitable Copula Dependence Measure for Feature Selection. 84-92 - Raphaël Féraud, Robin Allesiardo, Tanguy Urvoy, Fabrice Clérot:
Random Forest for the Contextual Bandit Problem. 93-101 - Michael Herman, Tobias Gindele, Jörg Wagner, Felix Schmitt, Wolfram Burgard:
Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics. 102-110 - Hemant Tyagi, Anastasios Kyrillidis, Bernd Gärtner, Andreas Krause:
Learning Sparse Additive Models with Interactions in High Dimensions. 111-120 - Megasthenis Asteris, Anastasios Kyrillidis, Dimitris S. Papailiopoulos, Alexandros G. Dimakis:
Bipartite Correlation Clustering: Maximizing Agreements. 121-129 - Sergey Bartunov, Dmitry Kondrashkin, Anton Osokin, Dmitry P. Vetrov:
Breaking Sticks and Ambiguities with Adaptive Skip-gram. 130-138 - Kwang-Sung Jun, Kevin G. Jamieson, Robert D. Nowak, Xiaojin Zhu:
Top Arm Identification in Multi-Armed Bandits with Batch Arm Pulls. 139-148 - Jonathan Scarlett, Volkan Cevher:
Limits on Sparse Support Recovery via Linear Sketching with Random Expander Matrices. 149-158 - Lee H. Dicker, Murat A. Erdogdu:
Maximum Likelihood for Variance Estimation in High-Dimensional Linear Models. 159-167 - Daniel Hernández-Lobato, José Miguel Hernández-Lobato:
Scalable Gaussian Process Classification via Expectation Propagation. 168-176 - Lingxiao Wang, Xiang Ren, Quanquan Gu:
Precision Matrix Estimation in High Dimensional Gaussian Graphical Models with Faster Rates. 177-185 - Jincheng Mei, Hao Zhang, Bao-Liang Lu:
On the Reducibility of Submodular Functions. 186-194 - Atsushi Nitanda:
Accelerated Stochastic Gradient Descent for Minimizing Finite Sums. 195-203 - Martin Boissier, Siwei Lyu, Yiming Ying, Ding-Xuan Zhou:
Fast Convergence of Online Pairwise Learning Algorithms. 204-212 - Andreas Svensson, Arno Solin, Simo Särkkä, Thomas B. Schön:
Computationally Efficient Bayesian Learning of Gaussian Process State Space Models. 213-221 - Yaniv Tenzer, Gal Elidan:
Generalized Ideal Parent (GIP): Discovering non-Gaussian Hidden Variables. 222-230 - Alexander G. de G. Matthews, James Hensman, Richard E. Turner, Zoubin Ghahramani:
On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes. 231-239 - Kevin G. Jamieson, Ameet Talwalkar:
Non-stochastic Best Arm Identification and Hyperparameter Optimization. 240-248 - Philipp Moritz, Robert Nishihara, Michael I. Jordan:
A Linearly-Convergent Stochastic L-BFGS Algorithm. 249-258 - Robert Nishihara, David Lopez-Paz, Léon Bottou:
No Regret Bound for Extreme Bandits. 259-267 - Anima Anandkumar, Prateek Jain, Yang Shi, U. N. Niranjan:
Tensor vs. Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations. 268-276 - Sougata Chaudhuri, Ambuj Tewari:
Online Learning to Rank with Feedback at the Top. 277-285 - Christopher Srinivasa, Siamak Ravanbakhsh, Brendan J. Frey:
Survey Propagation beyond Constraint Satisfaction Problems. 286-295 - Balázs Csanád Csáji:
Score Permutation Based Finite Sample Inference for Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) Models. 296-304 - Vikas K. Garg, Cynthia Rudin, Tommi S. Jaakkola:
CRAFT: ClusteR-specific Assorted Feature selecTion. 305-313 - Ilija Bogunovic, Jonathan Scarlett, Volkan Cevher:
Time-Varying Gaussian Process Bandit Optimization. 314-323 - Weici Hu, Peter I. Frazier:
Bayes-Optimal Effort Allocation in Crowdsourcing: Bounds and Index Policies. 324-332 - Dinu Kaufmann, Sonali Parbhoo, Aleksander Wieczorek, Sebastian Keller, David Adametz, Volker Roth:
Bayesian Markov Blanket Estimation. 333-341 - Søren Hauberg, Oren Freifeld, Anders Boesen Lindbo Larsen, John W. Fisher III, Lars Kai Hansen:
Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation. 342-350 - Ariel Jaffe, Ethan Fetaya, Boaz Nadler, Tingting Jiang, Yuval Kluger:
Unsupervised Ensemble Learning with Dependent Classifiers. 351-360 - Krzysztof Chalupka, Frederick Eberhardt, Pietro Perona:
Multi-Level Cause-Effect Systems. 361-369 - Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing:
Deep Kernel Learning. 370-378 - Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch:
Nearly Optimal Classification for Semimetrics. 379-388 - Chris M. Lloyd, Tom Gunter, Michael A. Osborne, Stephen J. Roberts, Tom Nickson:
Latent Point Process Allocation. 389-397 - Mijung Park, Wittawat Jitkrittum, Dino Sejdinovic:
K2-ABC: Approximate Bayesian Computation with Kernel Embeddings. 398-407 - Jes Frellsen, Ole Winther, Zoubin Ghahramani, Jesper Ferkinghoff-Borg:
Bayesian Generalised Ensemble Markov Chain Monte Carlo. 408-416 - Yan Li, Han Liu, Warren B. Powell:
A Lasso-based Sparse Knowledge Gradient Policy for Sequential Optimal Learning. 417-425 - Renkun Ni, Quanquan Gu:
Optimal Statistical and Computational Rates for One Bit Matrix Completion. 426-434 - Luc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy:
PAC-Bayesian Bounds based on the Rényi Divergence. 435-444 - Mihai Cucuringu, Ioannis Koutis, Sanjay Chawla, Gary L. Miller, Richard Peng:
Simple and Scalable Constrained Clustering: a Generalized Spectral Method. 445-454 - Avradeep Bhowmik, Nathan Liu, Erheng Zhong, Badri Narayan Bhaskar, Suju Rajan:
Geometry Aware Mappings for High Dimensional Sparse Factors. 455-463 - Chen-Yu Lee, Patrick W. Gallagher, Zhuowen Tu:
Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree. 464-472 - Chun-Liang Li, Hsuan-Tien Lin, Chi-Jen Lu:
Rivalry of Two Families of Algorithms for Memory-Restricted Streaming PCA. 473-481 - Ruiqi Guo, Sanjiv Kumar, Krzysztof Choromanski, David Simcha:
Quantization based Fast Inner Product Search. 482-490 - Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Mingyi Hong:
An Improved Convergence Analysis of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization. 491-499 - Jie Shen, Ping Li:
Learning Structured Low-Rank Representation via Matrix Factorization. 500-509 - Zhaohan Daniel Guo, Shayan Doroudi, Emma Brunskill:
A PAC RL Algorithm for Episodic POMDPs. 510-518 - Sujith Ravi, Qiming Diao:
Large Scale Distributed Semi-Supervised Learning Using Streaming Approximation. 519-528 - Calvin McCarter, Seyoung Kim:
Large-Scale Optimization Algorithms for Sparse Conditional Gaussian Graphical Models. 528-537 - Yining Wang, Yu-Xiang Wang, Aarti Singh:
Graph Connectivity in Noisy Sparse Subspace Clustering. 538-546 - Yu Nishiyama, Amir Afsharinejad, Shunsuke Naruse, Byron Boots, Le Song:
The Nonparametric Kernel Bayes Smoother. 547-555 - Asela Gunawardana, Christopher Meek:
Universal Models of Multivariate Temporal Point Processes. 556-563 - Zhitang Chen, Pascal Poupart, Yanhui Geng:
Online Relative Entropy Policy Search using Reproducing Kernel Hilbert Space Embeddings. 573-581 - Muneki Yasuda:
Relationship between PreTraining and Maximum Likelihood Estimation in Deep Boltzmann Machines. 582-590 - Eunice Yuh-Jie Chen, Arthur Choi, Adnan Darwiche:
Enumerating Equivalence Classes of Bayesian Networks using EC Graphs. 591-599 - Quanquan Gu, Zhaoran Wang, Han Liu:
Low-Rank and Sparse Structure Pursuit via Alternating Minimization. 600-609 - Eli A. Meirom, Pavel Kisilev:
NuC-MKL: A Convex Approach to Non Linear Multiple Kernel Learning. 610-619 - Fanhua Shang, Yuanyuan Liu, James Cheng:
Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization. 620-629 - Antoine Rolet, Marco Cuturi, Gabriel Peyré:
Fast Dictionary Learning with a Smoothed Wasserstein Loss. 630-638 - Canh Hao Nguyen, Hiroshi Mamitsuka:
New Resistance Distances with Global Information on Large Graphs. 639-647 - Javier González, Zhenwen Dai, Philipp Hennig, Neil D. Lawrence:
Batch Bayesian Optimization via Local Penalization. 648-657 - Trung Le, Vu Nguyen, Tu Dinh Nguyen, Dinh Q. Phung:
Nonparametric Budgeted Stochastic Gradient Descent. 654-572 - Alexandra Carpentier, Teresa Schlueter:
Learning Relationships between Data Obtained Independently. 658-666 - Heejin Choi, Ofer Meshi, Nathan Srebro:
Fast and Scalable Structural SVM with Slack Rescaling. 667-675 - Simon Bartels, Philipp Hennig:
Probabilistic Approximate Least-Squares. 676-684 - Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau, Daniel Sheldon:
Approximate Inference Using DC Programming For Collective Graphical Models. 685-693 - Yali Wang, Marcus A. Brubaker, Brahim Chaib-draa, Raquel Urtasun:
Sequential Inference for Deep Gaussian Process. 694-703 - Stephan Mandt, James McInerney, Farhan Abrol, Rajesh Ranganath, David M. Blei:
Variational Tempering. 704-712 - Yi Zhou, Yaoliang Yu, Wei Dai, Yingbin Liang, Eric P. Xing:
On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System. 713-722 - Wenzhe Li, Sungjin Ahn, Max Welling:
Scalable MCMC for Mixed Membership Stochastic Blockmodels. 723-731 - Markus Heinonen, Henrik Mannerström, Juho Rousu, Samuel Kaski, Harri Lähdesmäki:
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo. 732-740 - Yunchen Pu, Xin Yuan, Andrew Stevens, Chunyuan Li, Lawrence Carin:
A Deep Generative Deconvolutional Image Model. 741-750 - Jialei Wang, Mladen Kolar, Nathan Srebro:
Distributed Multi-Task Learning. 751-760 - Rishit Sheth, Roni Khardon:
A Fixed-Point Operator for Inference in Variational Bayesian Latent Gaussian Models. 761-769 - Sebastian Tschiatschek, Josip Djolonga, Andreas Krause:
Learning Probabilistic Submodular Diversity Models Via Noise Contrastive Estimation. 770-779 - Sangkyun Lee, Damian Brzyski, Malgorzata Bogdan:
Fast Saddle-Point Algorithm for Generalized Dantzig Selector and FDR Control with Ordered L1-Norm. 780-789 - Javier González, Michael A. Osborne, Neil D. Lawrence:
GLASSES: Relieving The Myopia Of Bayesian Optimisation. 790-799 - Aonan Zhang, San Gultekin, John W. Paisley:
Stochastic Variational Inference for the HDP-HMM. 800-808 - Siamak Ravanbakhsh, Barnabás Póczos, Jeff G. Schneider, Dale Schuurmans, Russell Greiner:
Stochastic Neural Networks with Monotonic Activation Functions. 809-818 - Xiaowei Hu, Prashanth L. A., András György, Csaba Szepesvári:
(Bandit) Convex Optimization with Biased Noisy Gradient Oracles. 819-828 - Shaobo Han, Xuejun Liao, David B. Dunson, Lawrence Carin:
Variational Gaussian Copula Inference. 829-838 - Guillaume Rabusseau, Borja Balle, Shay B. Cohen:
Low-Rank Approximation of Weighted Tree Automata. 839-847 - Mehryar Mohri, Scott Yang:
Accelerating Online Convex Optimization via Adaptive Prediction. 848-856 - Ye Wang, Antonio Canale, David B. Dunson:
Scalable geometric density estimation. 857-865 - Aghiles Salah, Nicoleta Rogovschi, Mohamed Nadif:
Model-based Co-clustering for High Dimensional Sparse Data. 866-874 - Christina Heinze, Brian McWilliams, Nicolai Meinshausen:
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections. 875-883 - Chun-Liang Li, Kirthevasan Kandasamy, Barnabás Póczos, Jeff G. Schneider:
High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models. 884-892 - Julien Pérolat, Bilal Piot, Bruno Scherrer, Olivier Pietquin:
On the Use of Non-Stationary Strategies for Solving Two-Player Zero-Sum Markov Games. 893-901 - Hanxiao Liu, Yiming Yang:
Semi-Supervised Learning with Adaptive Spectral Transform. 902-910 - Iain Murray, Matthew M. Graham:
Pseudo-Marginal Slice Sampling. 911-919 - Vassilis Kalofolias:
How to Learn a Graph from Smooth Signals. 920-929 - Mário A. T. Figueiredo, Robert D. Nowak:
Ordered Weighted L1 Regularized Regression with Strongly Correlated Covariates: Theoretical Aspects. 930-938 - Peter Auer, Chao-Kai Chiang, Ronald Ortner, Madalina M. Drugan:
Pareto Front Identification from Stochastic Bandit Feedback. 939-947 - Amirali Abdullah, Ravi Kumar, Andrew McGregor, Sergei Vassilvitskii, Suresh Venkatasubramanian:
Sketching, Embedding and Dimensionality Reduction in Information Theoretic Spaces. 948-956 - Suvrit Sra, Adams Wei Yu, Mu Li, Alexander J. Smola:
AdaDelay: Delay Adaptive Distributed Stochastic Optimization. 957-965 - Manzil Zaheer, Michael L. Wick, Jean-Baptiste Tristan, Alexander J. Smola, Guy L. Steele Jr.:
Exponential Stochastic Cellular Automata for Massively Parallel Inference. 966-975 - Pierre-Alexandre Mattei, Charles Bouveyron, Pierre Latouche:
Globally Sparse Probabilistic PCA. 976-984 - Bo Dai, Niao He, Hanjun Dai, Le Song:
Provable Bayesian Inference via Particle Mirror Descent. 985-994 - Xiaokai Wei, Philip S. Yu:
Unsupervised Feature Selection by Preserving Stochastic Neighbors. 995-1003 - Victor Gabillon, Alessandro Lazaric, Mohammad Ghavamzadeh, Ronald Ortner, Peter L. Bartlett:
Improved Learning Complexity in Combinatorial Pure Exploration Bandits. 1004-1012 - William Herlands, Andrew Gordon Wilson, Hannes Nickisch, Seth R. Flaxman, Daniel B. Neill, Wilbert Van Panhuis, Eric P. Xing:
Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces. 1013-1021 - Zi Wang, Bolei Zhou, Stefanie Jegelka:
Optimization as Estimation with Gaussian Processes in Bandit Settings. 1022-1031 - Jiaqian Yu, Matthew B. Blaschko:
A Convex Surrogate Operator for General Non-Modular Loss Functions. 1032-1041 - Jialei Wang, Mladen Kolar:
Inference for High-dimensional Exponential Family Graphical Models. 1042-1050 - Changyou Chen, David E. Carlson, Zhe Gan, Chunyuan Li, Lawrence Carin:
Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization. 1051-1060 - Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos:
Fitting Spectral Decay with the k-Support Norm. 1061-1069 - David Duvenaud, Dougal Maclaurin, Ryan P. Adams:
Early Stopping as Nonparametric Variational Inference. 1070-1077 - Junier B. Oliva, Avinava Dubey, Andrew Gordon Wilson, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing:
Bayesian Nonparametric Kernel-Learning. 1078-1086 - Lun-Kai Hsu, Tudor Achim, Stefano Ermon:
Tight Variational Bounds via Random Projections and I-Projections. 1087-1095 - Kui Tang, Nicholas Ruozzi, David Belanger, Tony Jebara:
Bethe Learning of Graphical Models via MAP Decoding. 1096-1104 - Veronika Rocková, Gemma E. Moran, Edward I. George:
Determinantal Regularization for Ensemble Variable Selection. 1105-1113 - Hao Cheng, Yaoliang Yu, Xinhua Zhang, Eric P. Xing, Dale Schuurmans:
Scalable and Sound Low-Rank Tensor Learning. 1114-1123 - Changwei Hu, Piyush Rai, Lawrence Carin:
Non-negative Matrix Factorization for Discrete Data with Hierarchical Side-Information. 1124-1132 - Changwei Hu, Piyush Rai, Lawrence Carin:
Topic-Based Embeddings for Learning from Large Knowledge Graphs. 1133-1141 - Garrett Bernstein, Daniel Sheldon:
Consistently Estimating Markov Chains with Noisy Aggregate Data. 1142-1150 - Tom Goldstein, Gavin Taylor, Kawika Barabin, Kent Sayre:
Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction. 1151-1158 - Uri Heinemann, Roi Livni, Elad Eban, Gal Elidan, Amir Globerson:
Improper Deep Kernels. 1159-1167 - Bobak Shahriari, Alexandre Bouchard-Côté, Nando de Freitas:
Unbounded Bayesian Optimization via Regularization. 1168-1176 - Hiroaki Sasaki, Gang Niu, Masashi Sugiyama:
Non-Gaussian Component Analysis with Log-Density Gradient Estimation. 1177-1185 - Tomás Kocák, Gergely Neu, Michal Valko:
Online Learning with Noisy Side Observations. 1186-1194 - Jan-Willem van de Meent, Brooks Paige, David Tolpin, Frank D. Wood:
Black-Box Policy Search with Probabilistic Programs. 1195-1204 - Cong Han Lim, Stephen J. Wright:
Efficient Bregman Projections onto the Permutahedron and Related Polytopes. 1205-1213 - Benito van der Zander, Maciej Liskiewicz:
On Searching for Generalized Instrumental Variables. 1214-1222 - Hanie Sedghi, Majid Janzamin, Anima Anandkumar:
Provable Tensor Methods for Learning Mixtures of Generalized Linear Models. 1223-1231 - Daniel Russo, James Zou:
Controlling Bias in Adaptive Data Analysis Using Information Theory. 1232-1240 - Jean-Francis Roy, Mario Marchand, François Laviolette:
A Column Generation Bound Minimization Approach with PAC-Bayesian Generalization Guarantees. 1241-1249 - Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Ryan J. Tibshirani:
Graph Sparsification Approaches for Laplacian Smoothing. 1250-1259 - Ian En-Hsu Yen, Dmitry Malioutov, Abhishek Kumar:
Scalable Exemplar Clustering and Facility Location via Augmented Block Coordinate Descent with Column Generation. 1260-1269 - Xiangli Chen, Mathew Monfort, Anqi Liu, Brian D. Ziebart:
Robust Covariate Shift Regression. 1270-1279 - Cheng Tang, Claire Monteleoni:
On Lloyd's Algorithm: New Theoretical Insights for Clustering in Practice. 1280-1289 - Panos Toulis, Dustin Tran, Edoardo M. Airoldi:
Towards Stability and Optimality in Stochastic Gradient Descent. 1290-1298 - Shang-Tse Chen, Maria-Florina Balcan, Duen Horng Chau:
Communication Efficient Distributed Agnostic Boosting. 1299-1307 - Matt J. Kusner, Yu Sun, Karthik Sridharan, Kilian Q. Weinberger:
Private Causal Inference. 1308-1317 - Guillaume W. Basse, Aaron Smith, Natesh S. Pillai:
Parallel Markov Chain Monte Carlo via Spectral Clustering. 1318-1327 - Chengtao Li, Stefanie Jegelka, Suvrit Sra:
Efficient Sampling for k-Determinantal Point Processes. 1328-1337 - Yasin Abbasi-Yadkori, Peter L. Bartlett, Stephen J. Wright:
A Fast and Reliable Policy Improvement Algorithm. 1338-1346 - Zhao Song, Ricardo Henao, David E. Carlson, Lawrence Carin:
Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization. 1347-1355 - Gautam Dasarathy, Aarti Singh, Maria-Florina Balcan, Jong Hyuk Park:
Active Learning Algorithms for Graphical Model Selection. 1356-1364 - Mina Ghashami, Daniel J. Perry, Jeff M. Phillips:
Streaming Kernel Principal Component Analysis. 1365-1374 - Qichao Que, Mikhail Belkin:
Back to the Future: Radial Basis Function Networks Revisited. 1375-1383 - Loïc Landrieu, Guillaume Obozinski:
Cut Pursuit: Fast Algorithms to Learn Piecewise Constant Functions. 1384-1393 - Daniel Filan, Jan Leike, Marcus Hutter:
Loss Bounds and Time Complexity for Speed Priors. 1394-1402 - Raffaello Camoriano, Tomás Angles, Alessandro Rudi, Lorenzo Rosasco:
NYTRO: When Subsampling Meets Early Stopping. 1403-1411 - Guillaume W. Basse, Hossein Azari Soufiani, Diane Lambert:
Randomization and The Pernicious Effects of Limited Budgets on Auction Experiments. 1412-1420 - Dustin Tran, Minjae Kim, Finale Doshi-Velez:
Spectral M-estimation with Applications to Hidden Markov Models. 1421-1430 - Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence:
Chained Gaussian Processes. 1431-1440 - Nedelina Teneva, Pramod Kaushik Mudrakarta, Risi Kondor:
Multiresolution Matrix Compression. 1441-1449 - Adam Bloniarz, Ameet Talwalkar, Bin Yu, Christopher Wu:
Supervised Neighborhoods for Distributed Nonparametric Regression. 1450-1459 - Dejiao Zhang, Laura Balzano:
Global Convergence of a Grassmannian Gradient Descent Algorithm for Subspace Estimation. 1460-1468 - Abdullah Rashwan, Han Zhao, Pascal Poupart:
Online and Distributed Bayesian Moment Matching for Parameter Learning in Sum-Product Networks. 1469-1477 - Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh:
Mondrian Forests for Large-Scale Regression when Uncertainty Matters. 1478-1487 - Jinchun Zhan, Brian Lois, Han Guo, Namrata Vaswani:
Online (and Offline) Robust PCA: Novel Algorithms and Performance Guarantees. 1488-1496 - Yan Kaganovsky, Ikenna Odinaka, David E. Carlson, Lawrence Carin:
Parallel Majorization Minimization with Dynamically Restricted Domains for Nonconvex Optimization. 1497-1505 - Amirmohammad Rooshenas, Daniel Lowd:
Discriminative Structure Learning of Arithmetic Circuits. 1506-1514 - Ping Li:
One Scan 1-Bit Compressed Sensing. 1515-1523
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