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ORCIDJournal of Machine Learning Research, Volume 17
Volume 17, 2016
- Emilie Kaufmann, Olivier Cappé, Aurélien Garivier:
On the Complexity of Best-Arm Identification in Multi-Armed Bandit Models. 1:1-1:42 - Mauro Maggioni, Stanislav Minsker, Nate Strawn:
Multiscale Dictionary Learning: Non-Asymptotic Bounds and Robustness. 2:1-2:51 - Azadeh Khaleghi, Daniil Ryabko, Jérémie Mary, Philippe Preux:
Consistent Algorithms for Clustering Time Series. 3:1-3:32 - Rico Blaser, Piotr Fryzlewicz:
Random Rotation Ensembles. 4:1-4:26 - Alessio Benavoli, Giorgio Corani, Francesca Mangili:
Should We Really Use Post-Hoc Tests Based on Mean-Ranks? 5:1-5:10 - Olivier Collier, Arnak S. Dalalyan:
Minimax Rates in Permutation Estimation for Feature Matching. 6:1-6:31 - Yee Whye Teh, Alexandre H. Thiéry, Sebastian J. Vollmer:
Consistency and Fluctuations For Stochastic Gradient Langevin Dynamics. 7:1-7:33 - Çaglar Gülçehre, Yoshua Bengio:
Knowledge Matters: Importance of Prior Information for Optimization. 8:1-8:32 - Konrad Rieck, Christian Wressnegger:
Harry: A Tool for Measuring String Similarity. 9:1-9:5 - Yutian Chen, Luke Bornn, Nando de Freitas, Mareija Eskelin, Jing Fang, Max Welling:
Herded Gibbs Sampling. 10:1-10:29 - Alan L. Yuille, Roozbeh Mottaghi:
Complexity of Representation and Inference in Compositional Models with Part Sharing. 11:1-11:28 - Yu-Xiang Wang, Huan Xu:
Noisy Sparse Subspace Clustering. 12:1-12:41 - Aviv Tamar, Dotan Di Castro, Shie Mannor:
Learning the Variance of the Reward-To-Go. 13:1-13:36 - Harish G. Ramaswamy, Shivani Agarwal:
Convex Calibration Dimension for Multiclass Loss Matrices. 14:1-14:45 - Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer, Samy Bengio:
LLORMA: Local Low-Rank Matrix Approximation. 15:1-15:24 - Xiang Zhang, Yichao Wu, Lan Wang, Runze Li:
A Consistent Information Criterion for Support Vector Machines in Diverging Model Spaces. 16:1-16:26 - Peter Kairouz, Sewoong Oh, Pramod Viswanath:
Extremal Mechanisms for Local Differential Privacy. 17:1-17:51 - Daniel J. Hsu, Sivan Sabato:
Loss Minimization and Parameter Estimation with Heavy Tails. 18:1-18:40 - Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos:
Analysis of Classification-based Policy Iteration Algorithms. 19:1-19:30 - Hachem Kadri, Emmanuel Duflos, Philippe Preux, Stéphane Canu, Alain Rakotomamonjy, Julien Audiffren:
Operator-valued Kernels for Learning from Functional Response Data. 20:1-20:54 - Jesse Read, Peter Reutemann, Bernhard Pfahringer, Geoff Holmes:
MEKA: A Multi-label/Multi-target Extension to WEKA. 21:1-21:5 - Samory Kpotufe, Abdeslam Boularias, Thomas Schultz, Kyoungok Kim:
Gradients Weights improve Regression and Classification. 22:1-22:34 - Dmitry Adamskiy, Wouter M. Koolen, Alexey V. Chernov, Vladimir Vovk:
A Closer Look at Adaptive Regret. 23:1-23:21 - Michael L. Valenzuela, Jerzy W. Rozenblit:
Learning Using Anti-Training with Sacrificial Data. 24:1-24:42 - Ha Quang Minh, Loris Bazzani, Vittorio Murino:
A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning. 25:1-25:72 - Lucas Mentch, Giles Hooker:
Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests. 26:1-26:41 - Yudong Chen, Jiaming Xu:
Statistical-Computational Tradeoffs in Planted Problems and Submatrix Localization with a Growing Number of Clusters and Submatrices. 27:1-27:57 - Daniel Hernández-Lobato, Pablo Morales-Mombiela, David Lopez-Paz, Alberto Suárez:
Non-linear Causal Inference using Gaussianity Measures. 28:1-28:39 - Ruth Heller, Yair Heller, Shachar Kaufman, Barak Brill, Malka Gorfine:
Consistent Distribution-Free $K$-Sample and Independence Tests for Univariate Random Variables. 29:1-29:54 - Robert J. B. Goudie, Sach Mukherjee:
A Gibbs Sampler for Learning DAGs. 30:1-30:39 - François Denis, Mattias Gybels, Amaury Habrard:
Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning. 31:1-31:32 - Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf:
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks. 32:1-32:102 - André R. Gonçalves, Fernando J. Von Zuben, Arindam Banerjee:
Multi-task Sparse Structure Learning with Gaussian Copula Models. 33:1-33:30 - Xiangrui Meng, Joseph K. Bradley, Burak Yavuz, Evan Randall Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, D. B. Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J. Franklin, Reza Zadeh, Matei Zaharia, Ameet Talwalkar:
MLlib: Machine Learning in Apache Spark. 34:1-34:7 - Bin Li, Doyen Sahoo, Steven C. H. Hoi:
OLPS: A Toolbox for On-Line Portfolio Selection. 35:1-35:5 - Julianus Pfeuffer, Oliver Serang:
A Bounded p-norm Approximation of Max-Convolution for Sub-Quadratic Bayesian Inference on Additive Factors. 36:1-36:39 - Shiliang Zhang, Hui Jiang, Li-Rong Dai:
Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Learn Neural Networks. 37:1-37:33 - Steve Hanneke:
The Optimal Sample Complexity of PAC Learning. 38:1-38:15 - Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel:
End-to-End Training of Deep Visuomotor Policies. 39:1-39:40 - Chong Zhang, Yufeng Liu, Yichao Wu:
On Quantile Regression in Reproducing Kernel Hilbert Spaces with the Data Sparsity Constraint. 40:1-40:45 - Jaakko Luttinen:
BayesPy: Variational Bayesian Inference in Python. 41:1-41:6 - Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence:
Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes. 42:1-42:62 - Ery Arias-Castro, David Mason, Bruno Pelletier:
On the Estimation of the Gradient Lines of a Density and the Consistency of the Mean-Shift Algorithm. 43:1-43:28 - Ana M. Martínez, Geoffrey I. Webb, Shenglei Chen, Nayyar Abbas Zaidi:
Scalable Learning of Bayesian Network Classifiers. 44:1-44:35 - Ürün Dogan, Tobias Glasmachers, Christian Igel:
A Unified View on Multi-class Support Vector Classification. 45:1-45:32 - Sherief Abdallah, Michael Kaisers:
Addressing Environment Non-Stationarity by Repeating Q-learning Updates. 46:1-46:31 - Jing Lu, Steven C. H. Hoi, Jialei Wang, Peilin Zhao, Zhiyong Liu:
Large Scale Online Kernel Learning. 47:1-47:43 - Krikamol Muandet, Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf:
Kernel Mean Shrinkage Estimators. 48:1-48:41 - Shusen Wang, Luo Luo, Zhihua Zhang:
SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions. 49:1-49:49 - Wei Chen, Yajun Wang, Yang Yuan, Qinshi Wang:
Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms. 50:1-50:33 - Ziteng Wang, Chi Jin, Kai Fan, Jiaqi Zhang, Junliang Huang, Yiqiao Zhong, Liwei Wang:
Differentially Private Data Releasing for Smooth Queries. 51:1-51:42 - Alon Gonen, Dan Rosenbaum, Yonina C. Eldar, Shai Shalev-Shwartz:
Subspace Learning with Partial Information. 52:1-52:21 - Mert Pilanci, Martin J. Wainwright:
Iterative Hessian Sketch: Fast and Accurate Solution Approximation for Constrained Least-Squares. 53:1-53:38 - Chris J. Oates, Jim Q. Smith, Sach Mukherjee:
Estimating Causal Structure Using Conditional DAG Models. 54:1-54:23 - Stéphane Ivanoff, Franck Picard, Vincent Rivoirard:
Adaptive Lasso and group-Lasso for functional Poisson regression. 55:1-55:46 - Ricardo Silva, Robin J. Evans:
Causal Inference through a Witness Protection Program. 56:1-56:53 - Teppo Niinimaki, Pekka Parviainen, Mikko Koivisto:
Structure Discovery in Bayesian Networks by Sampling Partial Orders. 57:1-57:47 - Nihar B. Shah, Sivaraman Balakrishnan, Joseph K. Bradley, Abhay Parekh, Kannan Ramchandran, Martin J. Wainwright:
Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence. 58:1-58:47 - Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor S. Lempitsky:
Domain-Adversarial Training of Neural Networks. 59:1-59:35 - Sonia A. Bhaskar:
Probabilistic Low-Rank Matrix Completion from Quantized Measurements. 60:1-60:34 - Aryan Mokhtari, Alejandro Ribeiro:
DSA: Decentralized Double Stochastic Averaging Gradient Algorithm. 61:1-61:35 - Gérard Biau, Kevin Bleakley, Benoît Cadre:
The Statistical Performance of Collaborative Inference. 62:1-62:29 - Andreas Andresen, Vladimir G. Spokoiny:
Convergence of an Alternating Maximization Procedure. 63:1-63:53 - Yossi Adi, Joseph Keshet:
StructED: Risk Minimization in Structured Prediction. 64:1-64:5 - Jure Zbontar, Yann LeCun:
Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches. 65:1-65:32 - Mohammad Ghavamzadeh, Yaakov Engel, Michal Valko:
Bayesian Policy Gradient and Actor-Critic Algorithms. 66:1-66:53 - André da Motta Salles Barreto, Doina Precup, Joelle Pineau:
Practical Kernel-Based Reinforcement Learning. 67:1-67:70 - Daniel Russo, Benjamin Van Roy:
An Information-Theoretic Analysis of Thompson Sampling. 68:1-68:30 - Rajarshi Guhaniyogi, David B. Dunson:
Compressed Gaussian Process for Manifold Regression. 69:1-69:26 - Matey Neykov, Jun S. Liu, Tianxi Cai:
On the Characterization of a Class of Fisher-Consistent Loss Functions and its Application to Boosting. 70:1-70:32 - Pierre-Louis Giscard, Z. Choo, S. J. Thwaite, D. Jaksch:
Exact Inference on Gaussian Graphical Models of Arbitrary Topology using Path-Sums. 71:1-71:19 - Sergio Escalera, Vassilis Athitsos
, Isabelle Guyon:
Challenges in multimodal gesture recognition. 72:1-72:54 - Richard S. Sutton, Ashique Rupam Mahmood, Martha White:
An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning. 73:1-73:29 - Mehryar Mohri, Andres Muñoz Medina:
Learning Algorithms for Second-Price Auctions with Reserve. 74:1-74:25 - Peter Richtárik, Martin Takác:
Distributed Coordinate Descent Method for Learning with Big Data. 75:1-75:25 - Stéphan Clémençon, Igor Colin, Aurélien Bellet:
Scaling-up Empirical Risk Minimization: Optimization of Incomplete $U$-statistics. 76:1-76:36 - Junhong Lin, Lorenzo Rosasco, Ding-Xuan Zhou:
Iterative Regularization for Learning with Convex Loss Functions. 77:1-77:38 - Qirong Ho, Junming Yin, Eric P. Xing:
Latent Space Inference of Internet-Scale Networks. 78:1-78:41 - Jenna Wiens, John V. Guttag, Eric Horvitz:
Patient Risk Stratification with Time-Varying Parameters: A Multitask Learning Approach. 79:1-79:23 - Xin Wang, Jinbo Bi, Shipeng Yu, Jiangwen Sun, Minghu Song:
Multiplicative Multitask Feature Learning. 80:1-80:33 - Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes:
The Benefit of Multitask Representation Learning. 81:1-81:32 - Lei Yang, Shaogao Lv, Junhui Wang:
Model-free Variable Selection in Reproducing Kernel Hilbert Space. 82:1-82:24 - Steven Diamond, Stephen P. Boyd:
CVXPY: A Python-Embedded Modeling Language for Convex Optimization. 83:1-83:5 - Ermo Wei, Sean Luke:
Lenient Learning in Independent-Learner Stochastic Cooperative Games. 84:1-84:42 - Jun Fan, Yirong Wu, Ming Yuan, David Page, Jie Liu, Irene M. Ong, Peggy L. Peissig, Elizabeth S. Burnside:
Structure-Leveraged Methods in Breast Cancer Risk Prediction. 85:1-85:15 - Wei-Sheng Chin, Bo-Wen Yuan, Mengyuan Yang, Yong Zhuang, Yu-Chin Juan, Chih-Jen Lin:
LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems. 86:1-86:5 - Matey Neykov, Jun S. Liu, Tianxi Cai:
L1-Regularized Least Squares for Support Recovery of High Dimensional Single Index Models with Gaussian Designs. 87:1-87:37 - Fajwel Fogel, Alexandre d'Aspremont, Milan Vojnovic:
Spectral Ranking using Seriation. 88:1-88:45 - Xin Guo, Jun Fan, Ding-Xuan Zhou:
Sparsity and Error Analysis of Empirical Feature-Based Regularization Schemes. 89:1-89:34 - Manuel Gomez-Rodriguez, Le Song, Hadi Daneshmand, Bernhard Schölkopf:
Estimating Diffusion Networks: Recovery Conditions, Sample Complexity and Soft-thresholding Algorithm. 90:1-90:29 - M. Pawan Kumar, Puneet Kumar Dokania:
Rounding-based Moves for Semi-Metric Labeling. 91:1-91:42 - Dan Yang, Zongming Ma, Andreas Buja:
Rate Optimal Denoising of Simultaneously Sparse and Low Rank Matrices. 92:1-92:27 - Christian Daniel, Gerhard Neumann, Oliver Kroemer, Jan Peters:
Hierarchical Relative Entropy Policy Search. 93:1-93:50 - Ashley Petersen, Noah Simon, Daniela M. Witten:
Convex Regression with Interpretable Sharp Partitions. 94:1-94:31 - Oscar Gabriel Reyes Pupo, Eduardo Pérez, María del Carmen Rodríguez-Hernández, Habib M. Fardoun, Sebastián Ventura:
JCLAL: A Java Framework for Active Learning. 95:1-95:5 - Brendan Juba:
Integrated Common Sense Learning and Planning in POMDPs. 96:1-96:37 - Gundram Leifert, Tobias Strauß, Tobias Grüning, Welf Wustlich, Roger Labahn:
Cells in Multidimensional Recurrent Neural Networks. 97:1-97:37 - Rohit Babbar, Ioannis Partalas, Éric Gaussier, Massih-Reza Amini, Cécile Amblard:
Learning Taxonomy Adaptation in Large-scale Classification. 98:1-98:37 - Jan Melchior, Asja Fischer, Laurenz Wiskott:
How to Center Deep Boltzmann Machines. 99:1-99:61 - Zijian Guo, Dylan S. Small:
Control Function Instrumental Variable Estimation of Nonlinear Causal Effect Models. 100:1-100:35 - Ru He, Jin Tian, Huaiqing Wu:
Structure Learning in Bayesian Networks of a Moderate Size by Efficient Sampling. 101:1-101:54 - Yuchen Zhang, Xi Chen, Dengyong Zhou, Michael I. Jordan:
Spectral Methods Meet EM: A Provably Optimal Algorithm for Crowdsourcing. 102:1-102:44 - Aki Vehtari, Tommi Mononen, Ville Tolvanen, Tuomas Sivula, Ole Winther:
Bayesian Leave-One-Out Cross-Validation Approximations for Gaussian Latent Variable Models. 103:1-103:38 - Marcela Zuluaga, Andreas Krause, Markus Püschel:
e-PAL: An Active Learning Approach to the Multi-Objective Optimization Problem. 104:1-104:32 - Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani:
Trend Filtering on Graphs. 105:1-105:41 - Neeraja Jayant Yadwadkar, Bharath Hariharan, Joseph E. Gonzalez, Randy Howard Katz:
Multi-Task Learning for Straggler Avoiding Predictive Job Scheduling. 106:1-106:37 - Hoo-Chang Shin, Le Lu, Lauren Kim, Ari Seff, Jianhua Yao, Ronald M. Summers:
Interleaved Text/Image Deep Mining on a Large-Scale Radiology Database for Automated Image Interpretation. 107:1-107:31 - Mahsa Baktashmotlagh, Mehrtash Tafazzoli Harandi, Mathieu Salzmann:
Distribution-Matching Embedding for Visual Domain Adaptation. 108:1-108:30 - Maya R. Gupta, Andrew Cotter, Jan Pfeifer, Konstantin Voevodski, Kevin Robert Canini, Alexander Mangylov, Wojtek Moczydlowski, Alexander Van Esbroeck:
Monotonic Calibrated Interpolated Look-Up Tables. 109:1-109:47 - Michael Wainberg, Babak Alipanahi, Brendan J. Frey:
Are Random Forests Truly the Best Classifiers? 110:1-110:5 - Yohann de Castro, Elisabeth Gassiat, Claire Lacour:
Minimax Adaptive Estimation of Nonparametric Hidden Markov Models. 111:1-111:43 - Bilal Ahmed, Thomas Thesen, Karen E. Blackmon, Ruben Kuzniecky, Orrin Devinsky, Carla E. Brodley:
Decrypting "Cryptogenic" Epilepsy: Semi-supervised Hierarchical Conditional Random Fields For Detecting Cortical Lesions In MRI-Negative Patients. 112:1-112:30 - Lu Tang, Peter X. K. Song:
Fused Lasso Approach in Regression Coefficients Clustering - Learning Parameter Heterogeneity in Data Integration. 113:1-113:23 - Sebastian Lapuschkin, Alexander Binder, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek:
The LRP Toolbox for Artificial Neural Networks. 114:1-114:5 - Somayeh Sojoudi:
Equivalence of Graphical Lasso and Thresholding for Sparse Graphs. 115:1-115:21 - Veit Elser:
A Network That Learns Strassen Multiplication. 116:1-116:13 - Alex Gittens, Michael W. Mahoney:
Revisiting the Nystrom Method for Improved Large-scale Machine Learning. 117:1-117:65 - Chengwei Su, Mark E. Borsuk:
Improving Structure MCMC for Bayesian Networks through Markov Blanket Resampling. 118:1-118:20 - Elad Hazan, Zohar S. Karnin:
Volumetric Spanners: An Efficient Exploration Basis for Learning. 119:1-119:34 - Haim Avron, Vikas Sindhwani, Jiyan Yang, Michael W. Mahoney:
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels. 120:1-120:38 - Jing Zhao, Shiliang Sun:
Variational Dependent Multi-output Gaussian Process Dynamical Systems. 121:1-121:36 - Jussi Gillberg, Pekka Marttinen, Matti Pirinen, Antti J. Kangas, Pasi Soininen, Mehreen Ali, Aki S. Havulinna, Marjo-Riitta Järvelin, Mika Ala-Korpela, Samuel Kaski:
Multiple Output Regression with Latent Noise. 122:1-122:35 - Yinfei Kong, Zemin Zheng, Jinchi Lv:
The Constrained Dantzig Selector with Enhanced Consistency. 123:1-123:22 - Julie Josse, Stefan Wager:
Bootstrap-Based Regularization for Low-Rank Matrix Estimation. 124:1-124:29 - Michael U. Gutmann, Jukka Corander:
Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models. 125:1-125:47 - Yossi Arjevani, Shai Shalev-Shwartz, Ohad Shamir:
On Lower and Upper Bounds in Smooth and Strongly Convex Optimization. 126:1-126:51 - Edgar D. Klenske, Philipp Hennig:
Dual Control for Approximate Bayesian Reinforcement Learning. 127:1-127:30 - Gary Doran, Soumya Ray:
Multiple-Instance Learning from Distributions. 128:1-128:50 - Nahum Shimkin:
An Online Convex Optimization Approach to Blackwell's Approachability. 129:1-129:23 - Ashwini Maurya:
A Well-Conditioned and Sparse Estimation of Covariance and Inverse Covariance Matrices Using a Joint Penalty. 130:1-130:28 - Yves-Laurent Kom Samo, Stephen J. Roberts:
String and Membrane Gaussian Processes. 131:1-131:87 - Byron C. Wallace, Joël Kuiper, Aakash Sharma, Mingxi (Brian) Zhu, Iain James Marshall:
Extracting PICO Sentences from Clinical Trial Reports using Supervised Distant Supervision. 132:1-132:25 - Huseyin Melih Elibol, Vincent Nguyen, Scott W. Linderman, Matthew J. Johnson, Amna Hashmi, Finale Doshi-Velez:
Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders. 133:1-133:38 - Simone Romano, Xuan Vinh Nguyen, James Bailey, Karin Verspoor:
Adjusting for Chance Clustering Comparison Measures. 134:1-134:32 - Steve Hanneke:
Refined Error Bounds for Several Learning Algorithms. 135:1-135:55 - Vladimir Vapnik, Rauf Izmailov:
Synergy of Monotonic Rules. 136:1-136:33 - James Townsend, Niklas Koep, Sebastian Weichwald:
Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation. 137:1-137:5 - Vikash Mansinghka, Patrick Shafto, Eric Jonas, Cap Petschulat, Max Gasner, Joshua B. Tenenbaum:
CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data. 138:1-138:49 - Amir-massoud Farahmand, Mohammad Ghavamzadeh, Csaba Szepesvári, Shie Mannor:
Regularized Policy Iteration with Nonparametric Function Spaces. 139:1-139:66 - Cheng Tai, Weinan E:
Multiscale Adaptive Representation of Signals: I. The Basic Framework. 140:1-140:38 - Yash Deshpande, Andrea Montanari:
Sparse PCA via Covariance Thresholding. 141:1-141:41 - Judy Hoffman, Deepak Pathak, Eric Tzeng, Jonathan Long, Sergio Guadarrama, Trevor Darrell, Kate Saenko:
Large Scale Visual Recognition through Adaptation using Joint Representation and Multiple Instance Learning. 142:1-142:31 - Francesca Ieva, Anna Maria Paganoni, Nicholas Tarabelloni:
Covariance-based Clustering in Multivariate and Functional Data Analysis. 143:1-143:21 - Rina Foygel Barber, Emil Y. Sidky:
MOCCA: Mirrored Convex/Concave Optimization for Nonconvex Composite Functions. 144:1-144:51 - Harm van Seijen, Ashique Rupam Mahmood, Patrick M. Pilarski, Marlos C. Machado, Richard S. Sutton:
True Online Temporal-Difference Learning. 145:1-145:40 - Jiahe Lin, Sumanta Basu, Moulinath Banerjee, George Michailidis:
Penalized Maximum Likelihood Estimation of Multi-layered Gaussian Graphical Models. 146:1-146:51 - Twan van Laarhoven, Elena Marchiori:
Local Network Community Detection with Continuous Optimization of Conductance and Weighted Kernel K-Means. 147:1-147:28 - James McQueen, Marina Meila, Jacob VanderPlas, Zhongyue Zhang:
Megaman: Scalable Manifold Learning in Python. 148:1-148:5 - Wei Qian, Yuhong Yang:
Kernel Estimation and Model Combination in A Bandit Problem with Covariates. 149:1-149:37 - Dan Shen, Haipeng Shen, J. S. Marron:
A General Framework for Consistency of Principal Component Analysis. 150:1-150:34 - Jan Lemeire:
Conditional Independencies under the Algorithmic Independence of Conditionals. 151:1-151:20 - Zoltán Szabó, Bharath K. Sriperumbudur, Barnabás Póczos, Arthur Gretton:
Learning Theory for Distribution Regression. 152:1-152:40 - Weijie Su, Stephen P. Boyd, Emmanuel J. Candès:
A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights. 153:1-153:43 - Gergely Neu, Gábor Bartók:
Importance Weighting Without Importance Weights: An Efficient Algorithm for Combinatorial Semi-Bandits. 154:1-154:21 - Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos:
New Perspectives on k-Support and Cluster Norms. 155:1-155:38 - Nicos G. Pavlidis, David P. Hofmeyr, Sotiris K. Tasoulis:
Minimum Density Hyperplanes. 156:1-156:33 - Alberto N. Escalante-B., Laurenz Wiskott:
Theoretical Analysis of the Optimal Free Responses of Graph-Based SFA for the Design of Training Graphs. 157:1-157:36 - Simon Odense, Roderick Edwards:
Universal Approximation Results for the Temporal Restricted Boltzmann Machine and the Recurrent Temporal Restricted Boltzmann Machine. 158:1-158:21 - Sebastian J. Vollmer, Konstantinos C. Zygalakis, Yee Whye Teh:
Exploration of the (Non-)Asymptotic Bias and Variance of Stochastic Gradient Langevin Dynamics. 159:1-159:48 - José Miguel Hernández-Lobato, Michael A. Gelbart, Ryan P. Adams, Matthew W. Hoffman, Zoubin Ghahramani:
A General Framework for Constrained Bayesian Optimization using Information-based Search. 160:1-160:53 - Chao Gao, Yu Lu, Zongming Ma, Harrison H. Zhou:
Optimal Estimation and Completion of Matrices with Biclustering Structures. 161:1-161:29 - Ji Liu, Xiaojin Zhu:
The Teaching Dimension of Linear Learners. 162:1-162:25 - Mingyuan Zhou, Yulai Cong, Bo Chen:
Augmentable Gamma Belief Networks. 163:1-163:44 - Wenlin Dai, Tiejun Tong, Marc G. Genton:
Optimal Estimation of Derivatives in Nonparametric Regression. 164:1-164:25 - Nihar B. Shah, Dengyong Zhou:
Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing. 165:1-165:52 - Jing Ma, George Michailidis:
Joint Structural Estimation of Multiple Graphical Models. 166:1-166:48 - Yuanjia Wang, Tianle Chen, Donglin Zeng:
Support Vector Hazards Machine: A Counting Process Framework for Learning Risk Scores for Censored Outcomes. 167:1-167:37 - Navodit Misra, Ercan E. Kuruoglu:
Stable Graphical Models. 168:1-168:36 - Stavros P. Adam, George D. Magoulas, Dimitrios A. Karras, Michael N. Vrahatis:
Bounding the Search Space for Global Optimization of Neural Networks Learning Error: An Interval Analysis Approach. 169:1-169:40 - Bernd Bischl, Michel Lang, Lars Kotthoff, Julia Schiffner, Jakob Richter, Erich Studerus, Giuseppe Casalicchio, Zachary M. Jones:
mlr: Machine Learning in R. 170:1-170:5 - Wouter M. Kouw, Laurens J. P. van der Maaten, Jesse H. Krijthe, Marco Loog:
Feature-Level Domain Adaptation. 171:1-171:32 - David Rohde, Matt P. Wand:
Semiparametric Mean Field Variational Bayes: General Principles and Numerical Issues. 172:1-172:47 - Jiazhong Nie, Wojciech Kotlowski, Manfred K. Warmuth:
Online PCA with Optimal Regret. 173:1-173:49 - Chiwoo Park, Jianhua Z. Huang:
Efficient Computation of Gaussian Process Regression for Large Spatial Data Sets by Patching Local Gaussian Processes. 174:1-174:29 - Yves-Alexandre de Montjoye, Luc Rocher, Alex 'Sandy' Pentland:
bandicoot: a Python Toolbox for Mobile Phone Metadata. 175:1-175:5 - Céline Brouard, Marie Szafranski, Florence d'Alché-Buc:
Input Output Kernel Regression: Supervised and Semi-Supervised Structured Output Prediction with Operator-Valued Kernels. 176:1-176:48 - Radoslaw Adamczak:
A Note on the Sample Complexity of the Er-SpUD Algorithm by Spielman, Wang and Wright for Exact Recovery of Sparsely Used Dictionaries. 177:1-177:18 - Romain Couillet, Gilles Wainrib, Harry Sevi, Hafiz Tiomoko Ali:
The Asymptotic Performance of Linear Echo State Neural Networks. 178:1-178:35 - Vince Lyzinski, Keith D. Levin, Donniell E. Fishkind, Carey E. Priebe:
On the Consistency of the Likelihood Maximization Vertex Nomination Scheme: Bridging the Gap Between Maximum Likelihood Estimation and Graph Matching. 179:1-179:34 - Yu Nishiyama, Kenji Fukumizu:
Characteristic Kernels and Infinitely Divisible Distributions. 180:1-180:28 - Nicolás García Trillos, Dejan Slepcev, James H. von Brecht, Thomas Laurent, Xavier Bresson:
Consistency of Cheeger and Ratio Graph Cuts. 181:1-181:46 - Leonidas Lefakis, François Fleuret:
Jointly Informative Feature Selection Made Tractable by Gaussian Modeling. 182:1-182:39 - Yu-Xiang Wang, Jing Lei, Stephen E. Fienberg:
Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle. 183:1-183:40 - Immanuel Bayer:
fastFM: A Library for Factorization Machines. 184:1-184:5 - Ramin Moghaddass, Cynthia Rudin, David Madigan:
The Factorized Self-Controlled Case Series Method: An Approach for Estimating the Effects of Many Drugs on Many Outcomes. 185:1-185:24 - Ricardo Henao, James Lu, Joseph E. Lucas, Jeffrey M. Ferranti, Lawrence Carin:
Electronic Health Record Analysis via Deep Poisson Factor Models. 186:1-186:32 - Zhirong Yang, Jukka Corander, Erkki Oja:
Low-Rank Doubly Stochastic Matrix Decomposition for Cluster Analysis. 187:1-187:25 - Chong Wu, Sunghoon Kwon, Xiaotong Shen, Wei Pan:
A New Algorithm and Theory for Penalized Regression-based Clustering. 188:1-188:25 - Artür Manukyan, Elvan Ceyhan:
Classification of Imbalanced Data with a Geometric Digraph Family. 189:1-189:40 - Tobias Sutter, Arnab Ganguly, Heinz Koeppl:
A Variational Approach to Path Estimation and Parameter Inference of Hidden Diffusion Processes. 190:1-190:37 - Stijn Luca, David A. Clifton, Bart Vanrumste:
One-class classification of point patterns of extremes. 191:1-191:21 - Kun Yuan, Bicheng Ying, Ali H. Sayed:
On the Influence of Momentum Acceleration on Online Learning. 192:1-192:66 - Ashish Khetan, Sewoong Oh:
Data-driven Rank Breaking for Efficient Rank Aggregation. 193:1-193:54 - Mona Meister, Ingo Steinwart:
Optimal Learning Rates for Localized SVMs. 194:1-194:44 - Aditya Krishna Menon, Robert C. Williamson:
Bipartite Ranking: a Risk-Theoretic Perspective. 195:1-195:102 - Shiwen Zhao, Chuan Gao, Sayan Mukherjee, Barbara E. Engelhardt:
Bayesian group factor analysis with structured sparsity. 196:1-196:47 - Patrick Hummel, R. Preston McAfee:
Machine Learning in an Auction Environment. 197:1-197:37 - Oren Elisha, Shai Dekel:
Wavelet decompositions of Random Forests - smoothness analysis, sparse approximation and applications. 198:1-198:38 - Lei Sun, Alexander G. Nikolaev:
Mutual Information Based Matching for Causal Inference with Observational Data. 199:1-199:31 - Xiangju Qin, Pádraig Cunningham, Michael Salter-Townshend:
Online Trans-dimensional von Mises-Fisher Mixture Models for User Profiles. 200:1-200:51 - Justin Bedo, Cheng Soon Ong:
Multivariate Spearman's rho for Aggregating Ranks Using Copulas. 201:1-201:30 - Sinead A. Williamson:
Nonparametric Network Models for Link Prediction. 202:1-202:21 - Jianqing Fan, Wen-Xin Zhou:
Guarding against Spurious Discoveries in High Dimensions. 203:1-203:34 - Hongxiao Zhu, Nate Strawn, David B. Dunson:
Bayesian Graphical Models for Multivariate Functional Data. 204:1-204:27 - Benigno Uria, Marc-Alexandre Côté, Karol Gregor, Iain Murray, Hugo Larochelle:
Neural Autoregressive Distribution Estimation. 205:1-205:37 - Ery Arias-Castro, David Mason, Bruno Pelletier:
ERRATA: On the Estimation of the Gradient Lines of a Density and the Consistency of the Mean-Shift Algorithm. 206:1-206:4 - Rajen Dinesh Shah:
Modelling Interactions in High-dimensional Data with Backtracking. 207:1-207:31 - Sylvain Arlot, Matthieu Lerasle:
Choice of V for V-Fold Cross-Validation in Least-Squares Density Estimation. 208:1-208:50 - Shusen Wang, Zhihua Zhang, Tong Zhang:
Towards More Efficient SPSD Matrix Approximation and CUR Matrix Decomposition. 210:1-210:49 - Daniel J. Lizotte, Eric B. Laber:
Multi-Objective Markov Decision Processes for Data-Driven Decision Support. 211:1-211:28 - Yakir A. Reshef, David N. Reshef, Hilary K. Finucane, Pardis C. Sabeti, Michael Mitzenmacher:
Measuring Dependence Powerfully and Equitably. 212:1-212:63 - Anqi Zhao, Yang Feng, Lie Wang, Xin Tong:
Neyman-Pearson Classification under High-Dimensional Settings. 213:1-213:39 - Garvesh Raskutti, Michael W. Mahoney:
A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares. 214:1-214:31 - Jason K. Johnson, Diane Oyen, Michael Chertkov, Praneeth Netrapalli:
Learning Planar Ising Models. 215:1-215:26 - Murat A. Erdogdu:
Newton-Stein Method: An Optimization Method for GLMs via Stein's Lemma. 216:1-216:52 - Xi Chen, Kevin Jiao, Qihang Lin:
Bayesian Decision Process for Cost-Efficient Dynamic Ranking via Crowdsourcing. 217:1-217:40 - Subhajit Dutta, Soham Sarkar, Anil Kumar Ghosh:
Multi-scale Classification using Localized Spatial Depth. 218:1-218:30 - Xi Chen, Adityanand Guntuboyina, Yuchen Zhang:
On Bayes Risk Lower Bounds. 219:1-219:58 - Huizhen Yu:
Weak Convergence Properties of Constrained Emphatic Temporal-difference Learning with Constant and Slowly Diminishing Stepsize. 220:1-220:58 - Tapio Pahikkala, Antti Airola:
RLScore: Regularized Least-Squares Learners. 221:1-221:5 - Ben London, Bert Huang, Lise Getoor:
Stability and Generalization in Structured Prediction. 222:1-222:52 - Robert C. Williamson, Elodie Vernet, Mark D. Reid:
Composite Multiclass Losses. 223:1-223:52 - Nuaman Asbeh, Boaz Lerner:
Learning Latent Variable Models by Pairwise Cluster Comparison: Part I - Theory and Overview. 224:1-224:52 - Gerrit J. J. van den Burg, Patrick J. F. Groenen:
GenSVM: A Generalized Multiclass Support Vector Machine. 225:1-225:42 - Terrance D. Savitsky:
Scalable Approximate Bayesian Inference for Outlier Detection under Informative Sampling. 226:1-226:49 - Thomas Furmston, Guy Lever, David Barber:
Approximate Newton Methods for Policy Search in Markov Decision Processes. 227:1-227:51 - Joon Kwon, Vianney Perchet:
Gains and Losses are Fundamentally Different in Regret Minimization: The Sparse Case. 229:1-229:32 - Chenxin Ma, Rachael Tappenden, Martin Takác:
Linear Convergence of Randomized Feasible Descent Methods Under the Weak Strong Convexity Assumption. 230:1-230:24 - Michael Chichignoud, Johannes Lederer, Martin J. Wainwright:
A Practical Scheme and Fast Algorithm to Tune the Lasso With Optimality Guarantees. 231:1-231:20 - Margareta Ackerman, Shai Ben-David:
A Characterization of Linkage-Based Hierarchical Clustering. 232:1-232:17 - Nuaman Asbeh, Boaz Lerner:
Learning Latent Variable Models by Pairwise Cluster Comparison: Part II - Algorithm and Evaluation. 233:1-233:45 - Peter Schulam, Suchi Saria:
Integrative Analysis using Coupled Latent Variable Models for Individualizing Prognoses. 234:1-234:35 - Bo Peng, Lan Wang, Yichao Wu:
An Error Bound for L1-norm Support Vector Machine Coefficients in Ultra-high Dimension. 236:1-236:26 - Tamir Hazan, Alexander G. Schwing, Raquel Urtasun:
Blending Learning and Inference in Conditional Random Fields. 237:1-237:25 - Baharan Mirzasoleiman, Amin Karbasi, Rik Sarkar, Andreas Krause:
Distributed Submodular Maximization. 238:1-238:44 - Pierre Alquier, James Ridgway, Nicolas Chopin:
On the properties of variational approximations of Gibbs posteriors. 239:1-239:41
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