JMLR Volume 17
- On the Complexity of Best-Arm Identification in Multi-Armed Bandit Models
- Emilie Kaufmann, Olivier Cappé, Aurélien Garivier; (1):1−42, 2016.
[abs][pdf][bib]
- Multiscale Dictionary Learning: Non-Asymptotic Bounds and Robustness
- Mauro Maggioni, Stanislav Minsker, Nate Strawn; (2):1−51, 2016.
[abs][pdf][bib]
- Consistent Algorithms for Clustering Time Series
- Azadeh Khaleghi, Daniil Ryabko, Jérémie Mary, Philippe Preux; (3):1−32, 2016.
[abs][pdf][bib]
- Random Rotation Ensembles
- Rico Blaser, Piotr Fryzlewicz; (4):1−26, 2016.
[abs][pdf][bib]
- Should We Really Use Post-Hoc Tests Based on Mean-Ranks?
- Alessio Benavoli, Giorgio Corani, Francesca Mangili; (5):1−10, 2016.
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- Minimax Rates in Permutation Estimation for Feature Matching
- Olivier Collier, Arnak S. Dalalyan; (6):1−31, 2016.
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- Consistency and Fluctuations For Stochastic Gradient Langevin Dynamics
- Yee Whye Teh, Alexandre H. Thiery, Sebastian J. Vollmer; (7):1−33, 2016.
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- Knowledge Matters: Importance of Prior Information for Optimization
- Çağlar Gülçehre, Yoshua Bengio; (8):1−32, 2016.
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- Harry: A Tool for Measuring String Similarity
- Konrad Rieck, Christian Wressnegger; (9):1−5, 2016. (Machine Learning Open Source Software Paper)
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[code]
[webpage]
- Herded Gibbs Sampling
- Yutian Chen, Luke Bornn, Nando de Freitas, Mareija Eskelin, Jing Fang, Max Welling; (10):1−29, 2016.
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- Complexity of Representation and Inference in Compositional Models with Part Sharing
- Alan Yuille, Roozbeh Mottaghi; (11):1−28, 2016.
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- Noisy Sparse Subspace Clustering
- Yu-Xiang Wang, Huan Xu; (12):1−41, 2016.
[abs][pdf][bib]
- Learning the Variance of the Reward-To-Go
- Aviv Tamar, Dotan Di Castro, Shie Mannor; (13):1−36, 2016.
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- Convex Calibration Dimension for Multiclass Loss Matrices
- Harish G. Ramaswamy, Shivani Agarwal; (14):1−45, 2016.
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- LLORMA: Local Low-Rank Matrix Approximation
- Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer, Samy Bengio; (15):1−24, 2016.
[abs][pdf][bib]
- A Consistent Information Criterion for Support Vector Machines in Diverging Model Spaces
- Xiang Zhang, Yichao Wu, Lan Wang, Runze Li; (16):1−26, 2016.
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- Extremal Mechanisms for Local Differential Privacy
- Peter Kairouz, Sewoong Oh, Pramod Viswanath; (17):1−51, 2016.
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- Loss Minimization and Parameter Estimation with Heavy Tails
- Daniel Hsu, Sivan Sabato; (18):1−40, 2016.
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- Analysis of Classification-based Policy Iteration Algorithms
- Alessandro Lazaric, Mohammad Ghavamzadeh, R{\'e}mi Munos; (19):1−30, 2016.
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- Operator-valued Kernels for Learning from Functional Response Data
- Hachem Kadri, Emmanuel Duflos, Philippe Preux, Stéphane Canu, Alain Rakotomamonjy, Julien Audiffren; (20):1−54, 2016.
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- MEKA: A Multi-label/Multi-target Extension to WEKA
- Jesse Read, Peter Reutemann, Bernhard Pfahringer, Geoff Holmes; (21):1−5, 2016. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[code]
[webpage]
- Gradients Weights improve Regression and Classification
- Samory Kpotufe, Abdeslam Boularias, Thomas Schultz, Kyoungok Kim; (22):1−34, 2016.
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- A Closer Look at Adaptive Regret
- Dmitry Adamskiy, Wouter M. Koolen, Alexey Chernov, Vladimir Vovk; (23):1−21, 2016.
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- Learning Using Anti-Training with Sacrificial Data
- Michael L. Valenzuela, Jerzy W. Rozenblit; (24):1−42, 2016.
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- A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning
- Hà Quang Minh, Loris Bazzani, Vittorio Murino; (25):1−72, 2016.
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- Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests
- Lucas Mentch, Giles Hooker; (26):1−41, 2016.
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- Statistical-Computational Tradeoffs in Planted Problems and Submatrix Localization with a Growing Number of Clusters and Submatrices
- Yudong Chen, Jiaming Xu; (27):1−57, 2016.
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- Non-linear Causal Inference using Gaussianity Measures
- Daniel Hern{\'a}ndez-Lobato, Pablo Morales-Mombiela, David Lopez-Paz, Alberto Su{\'a}rez; (28):1−39, 2016.
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- Consistent Distribution-Free $K$-Sample and Independence Tests for Univariate Random Variables
- Ruth Heller, Yair Heller, Shachar Kaufman, Barak Brill, Malka Gorfine; (29):1−54, 2016.
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- A Gibbs Sampler for Learning DAGs
- Robert J. B. Goudie, Sach Mukherjee; (30):1−39, 2016.
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- Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning
- François Denis, Mattias Gybels, Amaury Habrard; (31):1−32, 2016.
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- Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks
- Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf; (32):1−102, 2016.
[abs][pdf][bib]
[appendix 1]
[appendix 2]
- Multi-task Sparse Structure Learning with Gaussian Copula Models
- André R. Gonçalves, Fernando J. Von Zuben, Arindam Banerjee; (33):1−30, 2016.
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- MLlib: Machine Learning in Apache Spark
- Xiangrui Meng, Joseph Bradley, Burak Yavuz, Evan Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, DB Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J. Franklin, Reza Zadeh, Matei Zaharia, Ameet Talwalkar; (34):1−7, 2016. (Machine Learning Open Source Software Paper)
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[code]
[webpage]
- OLPS: A Toolbox for On-Line Portfolio Selection
- Bin Li, Doyen Sahoo, Steven C.H. Hoi; (35):1−5, 2016.
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- A Bounded p-norm Approximation of Max-Convolution for Sub-Quadratic Bayesian Inference on Additive Factors
- Julianus Pfeuffer, Oliver Serang; (36):1−39, 2016.
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- Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Learn Neural Networks
- Shiliang Zhang, Hui Jiang, Lirong Dai; (37):1−33, 2016.
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- The Optimal Sample Complexity of PAC Learning
- Steve Hanneke; (38):1−15, 2016.
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- End-to-End Training of Deep Visuomotor Policies
- Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel; (39):1−40, 2016.
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- On Quantile Regression in Reproducing Kernel Hilbert Spaces with the Data Sparsity Constraint
- Chong Zhang, Yufeng Liu, Yichao Wu; (40):1−45, 2016.
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- BayesPy: Variational Bayesian Inference in Python
- Jaakko Luttinen; (41):1−6, 2016.
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- Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes
- Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence; (42):1−62, 2016.
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- On the Estimation of the Gradient Lines of a Density and the Consistency of the Mean-Shift Algorithm
- Ery Arias-Castro, David Mason, Bruno Pelletier; (43):1−28, 2016.
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- Scalable Learning of Bayesian Network Classifiers
- Ana M. Martínez, Geoffrey I. Webb, Shenglei Chen, Nayyar A. Zaidi; (44):1−35, 2016.
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- A Unified View on Multi-class Support Vector Classification
- {\"U}rün Do\u{g}an, Tobias Glasmachers, Christian Igel; (45):1−32, 2016.
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- Addressing Environment Non-Stationarity by Repeating Q-learning Updates
- Sherief Abdallah, Michael Kaisers; (46):1−31, 2016.
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- Large Scale Online Kernel Learning
- Jing Lu, Steven C.H. Hoi, Jialei Wang, Peilin Zhao, Zhi-Yong Liu; (47):1−43, 2016.
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- Kernel Mean Shrinkage Estimators
- Krikamol Mu, et, Bharath Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf; (48):1−41, 2016.
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- SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions
- Shusen Wang, Luo Luo, Zhihua Zhang; (49):1−49, 2016.
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- Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms
- Wei Chen, Yajun Wang, Yang Yuan, Qinshi Wang; (50):1−33, 2016.
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- Differentially Private Data Releasing for Smooth Queries
- Ziteng Wang, Chi Jin, Kai Fan, Jiaqi Zhang, Junliang Huang, Yiqiao Zhong, Liwei Wang; (51):1−42, 2016.
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- Subspace Learning with Partial Information
- Alon Gonen, Dan Rosenbaum, Yonina C. Eldar, Shai Shalev-Shwartz; (52):1−21, 2016.
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- Iterative Hessian Sketch: Fast and Accurate Solution Approximation for Constrained Least-Squares
- Mert Pilanci, Martin J. Wainwright; (53):1−38, 2016.
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- Estimating Causal Structure Using Conditional DAG Models
- Chris. J. Oates, Jim Q. Smith, Sach Mukherjee; (54):1−23, 2016.
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- Adaptive Lasso and group-Lasso for functional Poisson regression
- Stéphane Ivanoff, Franck Picard, Vincent Rivoirard; (55):1−46, 2016.
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- Causal Inference through a Witness Protection Program
- Ricardo Silva, Robin Evans; (56):1−53, 2016.
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- Structure Discovery in Bayesian Networks by Sampling Partial Orders
- Teppo Niinim\"{a}ki, Pekka Parviainen, Mikko Koivisto; (57):1−47, 2016.
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- Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence
- Nihar B. Shah, Sivaraman Balakrishnan, Joseph Bradley, Abhay Parekh, Kannan Ramch, ran, Martin J. Wainwright; (58):1−47, 2016.
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- Domain-Adversarial Training of Neural Networks
- Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario March, Victor Lempitsky; (59):1−35, 2016.
[abs][pdf][bib]
- Probabilistic Low-Rank Matrix Completion from Quantized Measurements
- Sonia A. Bhaskar; (60):1−34, 2016.
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- DSA: Decentralized Double Stochastic Averaging Gradient Algorithm
- Aryan Mokhtari, Alejandro Ribeiro; (61):1−35, 2016.
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- The Statistical Performance of Collaborative Inference
- Gérard Biau, Kevin Bleakley, Benoît Cadre; (62):1−29, 2016.
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- Convergence of an Alternating Maximization Procedure
- Andreas Andresen, Vladimir Spokoiny; (63):1−53, 2016.
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- StructED: Risk Minimization in Structured Prediction
- Yossi Adi, Joseph Keshet; (64):1−5, 2016.
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- Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches
- Jure Žbontar, Yann LeCun; (65):1−32, 2016. (Machine Learning Open Source Software Paper)
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[code]
- Bayesian Policy Gradient and Actor-Critic Algorithms
- Mohammad Ghavamzadeh, Yaakov Engel, Michal Valko; (66):1−53, 2016.
[abs][pdf][bib]
- Practical Kernel-Based Reinforcement Learning
- André M.S. Barreto, Doina Precup, Joelle Pineau; (67):1−70, 2016.
[abs][pdf][bib]
- An Information-Theoretic Analysis of Thompson Sampling
- Daniel Russo, Benjamin Van Roy; (68):1−30, 2016.
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- Compressed Gaussian Process for Manifold Regression
- Rajarshi Guhaniyogi, David B. Dunson; (69):1−26, 2016.
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- On the Characterization of a Class of Fisher-Consistent Loss Functions and its Application to Boosting
- Matey Neykov, Jun S. Liu, Tianxi Cai; (70):1−32, 2016.
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- Exact Inference on Gaussian Graphical Models of Arbitrary Topology using Path-Sums
- P.-L. Giscard, Z. Choo, S. J. Thwaite, D. Jaksch; (71):1−19, 2016.
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- Challenges in multimodal gesture recognition
- Sergio Escalera, Vassilis Athitsos, Isabelle Guyon; (72):1−54, 2016.
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- An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning
- Richard S. Sutton, A. Rupam Mahmood, Martha White; (73):1−29, 2016.
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- Learning Algorithms for Second-Price Auctions with Reserve
- Mehryar Mohri, Andres Munoz Medina; (74):1−25, 2016.
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- Distributed Coordinate Descent Method for Learning with Big Data
- Peter Richtárik, Martin Takáč; (75):1−25, 2016.
[abs][pdf][bib]
- Scaling-up Empirical Risk Minimization: Optimization of Incomplete $U$-statistics
- Stephan Clémençon, Igor Colin, Aurélien Bellet; (76):1−36, 2016.
[abs][pdf][bib]
- Iterative Regularization for Learning with Convex Loss Functions
- Junhong Lin, Lorenzo Rosasco, Ding-Xuan Zhou; (77):1−38, 2016.
[abs][pdf][bib]
- Latent Space Inference of Internet-Scale Networks
- Qirong Ho, Junming Yin, Eric P. Xing; (78):1−41, 2016.
[abs][pdf][bib]
- Patient Risk Stratification with Time-Varying Parameters: A Multitask Learning Approach
- Jenna Wiens, John Guttag, Eric Horvitz; (79):1−23, 2016.
[abs][pdf][bib]
- Multiplicative Multitask Feature Learning
- Xin Wang, Jinbo Bi, Shipeng Yu, Jiangwen Sun, Minghu Song; (80):1−33, 2016.
[abs][pdf][bib]
- The Benefit of Multitask Representation Learning
- Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes; (81):1−32, 2016.
[abs][pdf][bib]
- Model-free Variable Selection in Reproducing Kernel Hilbert Space
- Lei Yang, Shaogao Lv, Junhui Wang; (82):1−24, 2016.
[abs][pdf][bib]
- CVXPY: A Python-Embedded Modeling Language for Convex Optimization
- Steven Diamond, Stephen Boyd; (83):1−5, 2016. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[code]
[webpage]
- Lenient Learning in Independent-Learner Stochastic Cooperative Games
- Ermo Wei, Sean Luke; (84):1−42, 2016.
[abs][pdf][bib]
- Structure-Leveraged Methods in Breast Cancer Risk Prediction
- Jun Fan, Yirong Wu, Ming Yuan, David Page, Jie Liu, Irene M. Ong, Peggy Peissig, Elizabeth Burnside; (85):1−15, 2016.
[abs][pdf][bib]
- LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems
- Wei-Sheng Chin, Bo-Wen Yuan, Meng-Yuan Yang, Yong Zhuang, Yu-Chin Juan, Chih-Jen Lin; (86):1−5, 2016. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[code]
- L1-Regularized Least Squares for Support Recovery of High Dimensional Single Index Models with Gaussian Designs
- Matey Neykov, Jun S. Liu, Tianxi Cai; (87):1−37, 2016.
[abs][pdf][bib]
- Spectral Ranking using Seriation
- Fajwel Fogel, Alexandre d'Aspremont, Milan Vojnovic; (88):1−45, 2016.
[abs][pdf][bib]
- Sparsity and Error Analysis of Empirical Feature-Based Regularization Schemes
- Xin Guo, Jun Fan, Ding-Xuan Zhou; (89):1−34, 2016.
[abs][pdf][bib]
- Estimating Diffusion Networks: Recovery Conditions, Sample Complexity and Soft-thresholding Algorithm
- Manuel Gomez-Rodriguez, Le Song, Hadi Daneshm, Bernhard Schölkopf; (90):1−29, 2016.
[abs][pdf][bib]
- Rounding-based Moves for Semi-Metric Labeling
- M. Pawan Kumar, Puneet K. Dokania; (91):1−42, 2016.
[abs][pdf][bib]
- Rate Optimal Denoising of Simultaneously Sparse and Low Rank Matrices
- Dan Yang, Zongming Ma, Andreas Buja; (92):1−27, 2016.
[abs][pdf][bib]
- Hierarchical Relative Entropy Policy Search
- Christian Daniel, Gerhard Neumann, Oliver Kroemer, Jan Peters; (93):1−50, 2016.
[abs][pdf][bib]
- Convex Regression with Interpretable Sharp Partitions
- Ashley Petersen, Noah Simon, Daniela Witten; (94):1−31, 2016.
[abs][pdf][bib]
- JCLAL: A Java Framework for Active Learning
- Oscar Reyes, Eduardo Pérez, María del Carmen Rodríguez-Hernández, Habib M. Fardoun, Sebastián Ventura; (95):1−5, 2016. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[code]
- Integrated Common Sense Learning and Planning in POMDPs
- Brendan Juba; (96):1−37, 2016.
[abs][pdf][bib]
- Cells in Multidimensional Recurrent Neural Networks
- Gundram Leifert, Tobias Strau{\ss}, Tobias Gr{ü}ning, Welf Wustlich, Roger Labahn; (97):1−37, 2016.
[abs][pdf][bib]
- Learning Taxonomy Adaptation in Large-scale Classification
- Rohit Babbar, Ioannis Partalas, Eric Gaussier, Massih-Reza Amini, Cécile Amblard; (98):1−37, 2016.
[abs][pdf][bib]
- How to Center Deep Boltzmann Machines
- Jan Melchior, Asja Fischer, Laurenz Wiskott; (99):1−61, 2016.
[abs][pdf][bib]
- Control Function Instrumental Variable Estimation of Nonlinear Causal Effect Models
- Zijian Guo, Dylan S. Small; (100):1−35, 2016.
[abs][pdf][bib]
- Structure Learning in Bayesian Networks of a Moderate Size by Efficient Sampling
- Ru He, Jin Tian, Huaiqing Wu; (101):1−54, 2016.
[abs][pdf][bib]
[appendix]
- Spectral Methods Meet EM: A Provably Optimal Algorithm for Crowdsourcing
- Yuchen Zhang, Xi Chen, Dengyong Zhou, Michael I. Jordan; (102):1−44, 2016.
[abs][pdf][bib]
- Bayesian Leave-One-Out Cross-Validation Approximations for Gaussian Latent Variable Models
- Aki Vehtari, Tommi Mononen, Ville Tolvanen, Tuomas Sivula, Ole Winther; (103):1−38, 2016.
[abs][pdf][bib]
- e-PAL: An Active Learning Approach to the Multi-Objective Optimization Problem
- Marcela Zuluaga, Andreas Krause, Markus P{ü}schel; (104):1−32, 2016.
[abs][pdf][bib]
- Trend Filtering on Graphs
- Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani; (105):1−41, 2016.
[abs][pdf][bib]
- Multi-Task Learning for Straggler Avoiding Predictive Job Scheduling
- Neeraja J. Yadwadkar, Bharath Hariharan, Joseph E. Gonzalez, R, y Katz; (106):1−37, 2016.
[abs][pdf][bib]
- Interleaved Text/Image Deep Mining on a Large-Scale Radiology Database for Automated Image Interpretation
- Hoo-Chang Shin, Le Lu, Lauren Kim, Ari Seff, Jianhua Yao, Ronald M. Summers; (107):1−31, 2016.
[abs][pdf][bib]
- Distribution-Matching Embedding for Visual Domain Adaptation
- Mahsa Baktashmotlagh, Mehrtash Har, i, Mathieu Salzmann; (108):1−30, 2016.
[abs][pdf][bib]
- Monotonic Calibrated Interpolated Look-Up Tables
- Maya Gupta, Andrew Cotter, Jan Pfeifer, Konstantin Voevodski, Kevin Canini, Alexander Mangylov, Wojciech Moczydlowski, Alexander van Esbroeck; (109):1−47, 2016.
[abs][pdf][bib]
- Are Random Forests Truly the Best Classifiers?
- Michael Wainberg, Babak Alipanahi, Brendan J. Frey; (110):1−5, 2016.
[abs][pdf][bib]
- Minimax Adaptive Estimation of Nonparametric Hidden Markov Models
- Yohann De Castro, {\'E}lisabeth Gassiat, Claire Lacour; (111):1−43, 2016.
[abs][pdf][bib]
- Decrypting “Cryptogenic” Epilepsy: Semi-supervised Hierarchical Conditional Random Fields For Detecting Cortical Lesions In MRI-Negative Patients
- Bilal Ahmed, Thomas Thesen, Karen E. Blackmon, Ruben Kuzniekcy, Orrin Devinsky, Carla E. Brodley; (112):1−30, 2016.
[abs][pdf][bib]
- Fused Lasso Approach in Regression Coefficients Clustering -- Learning Parameter Heterogeneity in Data Integration
- Lu Tang, Peter X.K. Song; (113):1−23, 2016.
[abs][pdf][bib]
- The LRP Toolbox for Artificial Neural Networks
- Sebastian Lapuschkin, Alexander Binder, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek; (114):1−5, 2016.
[abs][pdf][bib]
- Equivalence of Graphical Lasso and Thresholding for Sparse Graphs
- Somayeh Sojoudi; (115):1−21, 2016.
[abs][pdf][bib]
- A Network That Learns Strassen Multiplication
- Veit Elser; (116):1−13, 2016.
[abs][pdf][bib]
- Revisiting the Nyström Method for Improved Large-scale Machine Learning
- Alex Gittens, Michael W. Mahoney; (117):1−65, 2016.
[abs][pdf][bib]
- Improving Structure MCMC for Bayesian Networks through Markov Blanket Resampling
- Chengwei Su, Mark E. Borsuk; (118):1−20, 2016.
[abs][pdf][bib]
- Volumetric Spanners: An Efficient Exploration Basis for Learning
- Elad Hazan, Zohar Karnin; (119):1−34, 2016.
[abs][pdf][bib]
- Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels
- Haim Avron, Vikas Sindhwani, Jiyan Yang, Michael W. Mahoney; (120):1−38, 2016.
[abs][pdf][bib]
- Variational Dependent Multi-output Gaussian Process Dynamical Systems
- Jing Zhao, Shiliang Sun; (121):1−36, 2016.
[abs][pdf][bib]
- Multiple Output Regression with Latent Noise
- Jussi Gillberg, Pekka Marttinen, Matti Pirinen, Antti J. Kangas, Pasi Soininen, Mehreen Ali, Aki S. Havulinna, Marjo-Riitta Järvelin, Mika Ala-Korpela, Samuel Kaski; (122):1−35, 2016.
[abs][pdf][bib]
- The Constrained Dantzig Selector with Enhanced Consistency
- Yinfei Kong, Zemin Zheng, Jinchi Lv; (123):1−22, 2016.
[abs][pdf][bib]
- Bootstrap-Based Regularization for Low-Rank Matrix Estimation
- Julie Josse, Stefan Wager; (124):1−29, 2016.
[abs][pdf][bib]
- Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models
- Michael U. Gutmann, Jukka Cor, er; (125):1−47, 2016.
[abs][pdf][bib]
- On Lower and Upper Bounds in Smooth and Strongly Convex Optimization
- Yossi Arjevani, Shai Shalev-Shwartz, Ohad Shamir; (126):1−51, 2016.
[abs][pdf][bib]
- Dual Control for Approximate Bayesian Reinforcement Learning
- Edgar D. Klenske, Philipp Hennig; (127):1−30, 2016.
[abs][pdf][bib]
- Multiple-Instance Learning from Distributions
- Gary Doran, Soumya Ray; (128):1−50, 2016.
[abs][pdf][bib]
- An Online Convex Optimization Approach to Blackwell's Approachability
- Nahum Shimkin; (129):1−23, 2016.
[abs][pdf][bib]
- A Well-Conditioned and Sparse Estimation of Covariance and Inverse Covariance Matrices Using a Joint Penalty
- Ashwini Maurya; (130):1−28, 2016.
[abs][pdf][bib]
- String and Membrane Gaussian Processes
- Yves-Laurent Kom Samo, Stephen J. Roberts; (131):1−87, 2016.
[abs][pdf][bib]
- Extracting PICO Sentences from Clinical Trial Reports using Supervised Distant Supervision
- Byron C. Wallace, Joël Kuiper, Aakash Sharma, Mingxi (Brian) Zhu, Iain J. Marshall; (132):1−25, 2016.
[abs][pdf][bib]
- Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders
- Huseyin Melih Elibol, Vincent Nguyen, Scott Linderman, Matthew Johnson, Amna Hashmi, Finale Doshi-Velez; (133):1−38, 2016.
[abs][pdf][bib]
- Adjusting for Chance Clustering Comparison Measures
- Simone Romano, Nguyen Xuan Vinh, James Bailey, Karin Verspoor; (134):1−32, 2016.
[abs][pdf][bib]
- Refined Error Bounds for Several Learning Algorithms
- Steve Hanneke; (135):1−55, 2016.
[abs][pdf][bib]
- Synergy of Monotonic Rules
- Vladimir Vapnik, Rauf Izmailov; (136):1−33, 2016.
[abs][pdf][bib]
- Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation
- James Townsend, Niklas Koep, Sebastian Weichwald; (137):1−5, 2016.
[abs][pdf][bib]
- CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data
- Vikash Mansinghka, Patrick Shafto, Eric Jonas, Cap Petschulat, Max Gasner, Joshua B. Tenenbaum; (138):1−49, 2016.
[abs][pdf][bib]
- Regularized Policy Iteration with Nonparametric Function Spaces
- Amir-massoud Farahm, , Mohammad Ghavamzadeh, Csaba Szepesvári, Shie Mannor; (139):1−66, 2016.
[abs][pdf][bib]
- Multiscale Adaptive Representation of Signals: I. The Basic Framework
- Cheng Tai, Weinan E; (140):1−38, 2016.
[abs][pdf][bib]
- Sparse PCA via Covariance Thresholding
- Yash Deshp, e, Andrea Montanari; (141):1−41, 2016.
[abs][pdf][bib]
- Large Scale Visual Recognition through Adaptation using Joint Representation and Multiple Instance Learning
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