JMLR Volume 18
- Averaged Collapsed Variational Bayes Inference
- Katsuhiko Ishiguro, Issei Sato, Naonori Ueda; (1):1−29, 2017.
[abs][pdf][bib]
- Scalable Influence Maximization for Multiple Products in Continuous-Time Diffusion Networks
- Nan Du, Yingyu Liang, Maria-Florina Balcan, Manuel Gomez-Rodriguez, Hongyuan Zha, Le Song; (2):1−45, 2017.
[abs][pdf][bib]
- Local algorithms for interactive clustering
- Pranjal Awasthi, Maria Florina Balcan, Konstantin Voevodski; (3):1−35, 2017.
[abs][pdf][bib]
- SnapVX: A Network-Based Convex Optimization Solver
- David Hallac, Christopher Wong, Steven Diamond, Abhijit Sharang, Rok Sosič, Stephen Boyd, Jure Leskovec; (4):1−5, 2017. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[code]
[stanford.edu]
- Communication-efficient Sparse Regression
- Jason D. Lee, Qiang Liu, Yuekai Sun, Jonathan E. Taylor; (5):1−30, 2017.
[abs][pdf][bib]
- Improving Variational Methods via Pairwise Linear Response Identities
- Jack Raymond, Federico Ricci-Tersenghi; (6):1−36, 2017.
[abs][pdf][bib]
- Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks
- Adam S. Charles, Dong Yin, Christopher J. Rozell; (7):1−37, 2017.
[abs][pdf][bib]
- Persistence Images: A Stable Vector Representation of Persistent Homology
- Henry Adams, Tegan Emerson, Michael Kirby, Rachel Neville, Chris Peterson, Patrick Shipman, Sofya Chepushtanova, Eric Hanson, Francis Motta, Lori Ziegelmeier; (8):1−35, 2017.
[abs][pdf][bib]
[erratum]
- Spectral Clustering Based on Local PCA
- Ery Arias-Castro, Gilad Lerman, Teng Zhang; (9):1−57, 2017.
[abs][pdf][bib]
- On Perturbed Proximal Gradient Algorithms
- Yves F. Atchadé, Gersende Fort, Eric Moulines; (10):1−33, 2017.
[abs][pdf][bib]
- Differential Privacy for Bayesian Inference through Posterior Sampling
- Christos Dimitrakakis, Blaine Nelson, Zuhe Zhang, Aikaterini Mitrokotsa, Benjamin I. P. Rubinstein; (11):1−39, 2017.
[abs][pdf][bib]
- Refinery: An Open Source Topic Modeling Web Platform
- Daeil Kim, Benjamin F. Swanson, Michael C. Hughes, Erik B. Sudderth; (12):1−5, 2017. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[code]
[webpage]
- Using Conceptors to Manage Neural Long-Term Memories for Temporal Patterns
- Herbert Jaeger; (13):1−43, 2017.
[abs][pdf][bib]
[supplementary]
- Automatic Differentiation Variational Inference
- Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei; (14):1−45, 2017.
[abs][pdf][bib]
- Empirical Evaluation of Resampling Procedures for Optimising SVM Hyperparameters
- Jacques Wainer, Gavin Cawley; (15):1−35, 2017.
[abs][pdf][bib]
- A Unified Formulation and Fast Accelerated Proximal Gradient Method for Classification
- Naoki Ito, Akiko Takeda, Kim-Chuan Toh; (16):1−49, 2017.
[abs][pdf][bib]
- Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
- Guillaume Lemaître, Fernando Nogueira, Christos K. Aridas; (17):1−5, 2017. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[code]
[webpage]
- Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles
- Yann Ollivier, Ludovic Arnold, Anne Auger, Nikolaus Hansen; (18):1−65, 2017.
[abs][pdf][bib]
- Breaking the Curse of Dimensionality with Convex Neural Networks
- Francis Bach; (19):1−53, 2017.
[abs][pdf][bib]
- Memory Efficient Kernel Approximation
- Si Si, Cho-Jui Hsieh, Inderjit S. Dhillon; (20):1−32, 2017.
[abs][pdf][bib]
- On the Equivalence between Kernel Quadrature Rules and Random Feature Expansions
- Francis Bach; (21):1−38, 2017.
[abs][pdf][bib]
- Analyzing Tensor Power Method Dynamics in Overcomplete Regime
- Animashree An, kumar, Rong Ge, Majid Janzamin; (22):1−40, 2017.
[abs][pdf][bib]
- JSAT: Java Statistical Analysis Tool, a Library for Machine Learning
- Edward Raff; (23):1−5, 2017. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[code]
[webpage]
- Identifying a Minimal Class of Models for High--dimensional Data
- Daniel Nevo, Ya'acov Ritov; (24):1−29, 2017.
[abs][pdf][bib]
- Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA
- Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown; (25):1−5, 2017. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[code]
[webpage]
- POMDPs.jl: A Framework for Sequential Decision Making under Uncertainty
- Maxim Egorov, Zachary N. Sunberg, Edward Balaban, Tim A. Wheeler, Jayesh K. Gupta, Mykel J. Kochenderfer; (26):1−5, 2017. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[code]
[webpage]
- Generalized P{\'o}lya Urn for Time-Varying Pitman-Yor Processes
- François Caron, Willie Neiswanger, Frank Wood, Arnaud Doucet, Manuel Davy; (27):1−32, 2017.
[abs][pdf][bib]
- Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models
- Alexandre Bouchard-Côté, Arnaud Doucet, Andrew Roth; (28):1−39, 2017.
[abs][pdf][bib]
- Certifiably Optimal Low Rank Factor Analysis
- Dimitris Bertsimas, Martin S. Copenhaver, Rahul Mazumder; (29):1−53, 2017.
[abs][pdf][bib]
- Group Sparse Optimization via lp,q Regularization
- Yaohua Hu, Chong Li, Kaiwen Meng, Jing Qin, Xiaoqi Yang; (30):1−52, 2017.
[abs][pdf][bib]
- Preference-based Teaching
- Ziyuan Gao, Christoph Ries, Hans U. Simon, S, ra Zilles; (31):1−32, 2017.
[abs][pdf][bib]
- Nonparametric Risk Bounds for Time-Series Forecasting
- Daniel J. McDonald, Cosma Rohilla Shalizi, Mark Schervish; (32):1−40, 2017.
[abs][pdf][bib]
- Online Bayesian Passive-Aggressive Learning
- Tianlin Shi, Jun Zhu; (33):1−39, 2017.
[abs][pdf][bib]
- Asymptotic Analysis of Objectives Based on Fisher Information in Active Learning
- Jamshid Sourati, Murat Akcakaya, Todd K. Leen, Deniz Erdogmus, Jennifer G. Dy; (34):1−41, 2017.
[abs][pdf][bib]
- A Spectral Algorithm for Inference in Hidden semi-Markov Models
- Igor Melnyk, Arindam Banerjee; (35):1−39, 2017.
[abs][pdf][bib]
- Simplifying Probabilistic Expressions in Causal Inference
- Santtu Tikka, Juha Karvanen; (36):1−30, 2017.
[abs][pdf][bib]
- Nearly optimal classification for semimetrics
- Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch; (37):1−22, 2017.
[abs][pdf][bib]
- Bridging Supervised Learning and Test-Based Co-optimization
- Elena Popovici; (38):1−39, 2017.
[abs][pdf][bib]
[appendix]
- GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis
- Eemeli Leppäaho, Muhammad Ammad-ud-din, Samuel Kaski; (39):1−5, 2017. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[code]
[r-project.org]
- GPflow: A Gaussian Process Library using TensorFlow
- Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo Le{\'o}n-Villagr{\'a}, Zoubin Ghahramani, James Hensman; (40):1−6, 2017. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[code]
[webpage]
- COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Evolution
- Mehrdad Farajtabar, Yichen Wang, Manuel Gomez-Rodriguez, Shuang Li, Hongyuan Zha, Le Song; (41):1−49, 2017.
[abs][pdf][bib]
- Learning Local Dependence In Ordered Data
- Guo Yu, Jacob Bien; (42):1−60, 2017.
[abs][pdf][bib]
- Bayesian Learning of Dynamic Multilayer Networks
- Daniele Durante, Nabanita Mukherjee, Rebecca C. Steorts; (43):1−29, 2017.
[abs][pdf][bib]
- Time-Accuracy Tradeoffs in Kernel Prediction: Controlling Prediction Quality
- Samory Kpotufe, Nakul Verma; (44):1−29, 2017.
[abs][pdf][bib]
- Asymptotic behavior of Support Vector Machine for spiked population model
- Hanwen Huang; (45):1−21, 2017.
[abs][pdf][bib]
- Distributed Semi-supervised Learning with Kernel Ridge Regression
- Xiangyu Chang, Shao-Bo Lin, Ding-Xuan Zhou; (46):1−22, 2017.
[abs][pdf][bib]
- On Markov chain Monte Carlo methods for tall data
- Rémi Bardenet, Arnaud Doucet, Chris Holmes; (47):1−43, 2017.
[abs][pdf][bib]
- Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers
- Abraham J. Wyner, Matthew Olson, Justin Bleich, David Mease; (48):1−33, 2017.
[abs][pdf][bib]
- Clustering from General Pairwise Observations with Applications to Time-varying Graphs
- Shiau Hong Lim, Yudong Chen, Huan Xu; (49):1−47, 2017.
[abs][pdf][bib]
- Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques
- Debarghya Ghoshdastidar, Ambedkar Dukkipati; (50):1−41, 2017.
[abs][pdf][bib]
- Reconstructing Undirected Graphs from Eigenspaces
- Yohann De Castro, Thibault Espinasse, Paul Rochet; (51):1−24, 2017.
[abs][pdf][bib]
- An Optimal Algorithm for Bandit and Zero-Order Convex Optimization with Two-Point Feedback
- Ohad Shamir; (52):1−11, 2017.
[abs][pdf][bib]
- Perishability of Data: Dynamic Pricing under Varying-Coefficient Models
- Adel Javanmard; (53):1−31, 2017.
[abs][pdf][bib]
- Two New Approaches to Compressed Sensing Exhibiting Both Robust Sparse Recovery and the Grouping Effect
- Mehmet Eren Ahsen, Niharika Challapalli, Mathukumalli Vidyasagar; (54):1−24, 2017.
[abs][pdf][bib]
- On the Consistency of Ordinal Regression Methods
- Fabian Pedregosa, Francis Bach, Alexandre Gramfort; (55):1−35, 2017.
[abs][pdf][bib]
- Statistical Inference with Unnormalized Discrete Models and Localized Homogeneous Divergences
- Takashi Takenouchi, Takafumi Kanamori; (56):1−26, 2017.
[abs][pdf][bib]
- Density Estimation in Infinite Dimensional Exponential Families
- Bharath Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Aapo Hyv\"{a}rinen, Revant Kumar; (57):1−59, 2017.
[abs][pdf][bib]
- Lens Depth Function and k-Relative Neighborhood Graph: Versatile Tools for Ordinal Data Analysis
- Matthäus Kleindessner, Ulrike von Luxburg; (58):1−52, 2017.
[abs][pdf][bib]
- Joint Label Inference in Networks
- Deepayan Chakrabarti, Stanislav Funiak, Jonathan Chang, Sofus A. Macskassy; (59):1−39, 2017.
[abs][pdf][bib]
- Achieving Optimal Misclassification Proportion in Stochastic Block Models
- Chao Gao, Zongming Ma, Anderson Y. Zhang, Harrison H. Zhou; (60):1−45, 2017.
[abs][pdf][bib]
- On the Propagation of Low-Rate Measurement Error to Subgraph Counts in Large Networks
- Prakash Balach, ran, Eric D. Kolaczyk, Weston D. Viles; (61):1−33, 2017.
[abs][pdf][bib]
- Dense Distributions from Sparse Samples: Improved Gibbs Sampling Parameter Estimators for LDA
- Yannis Papanikolaou, James R. Foulds, Timothy N. Rubin, Grigorios Tsoumakas; (62):1−58, 2017.
[abs][pdf][bib]
- Fundamental Conditions for Low-CP-Rank Tensor Completion
- Morteza Ashraphijuo, Xiaodong Wang; (63):1−29, 2017.
[abs][pdf][bib]
- Parallel Symmetric Class Expression Learning
- An C. Tran, Jens Dietrich, Hans W. Guesgen, Stephen Marsl, ; (64):1−34, 2017.
[abs][pdf][bib]
- Learning Partial Policies to Speedup MDP Tree Search via Reduction to I.I.D. Learning
- Jervis Pinto, Alan Fern; (65):1−35, 2017.
[abs][pdf][bib]
- Hierarchically Compositional Kernels for Scalable Nonparametric Learning
- Jie Chen, Haim Avron, Vikas Sindhwani; (66):1−42, 2017.
[abs][pdf][bib]
- Sharp Oracle Inequalities for Square Root Regularization
- Benjamin Stucky, Sara van de Geer; (67):1−29, 2017.
[abs][pdf][bib]
- Soft Margin Support Vector Classification as Buffered Probability Minimization
- Matthew Norton, Alexander Mafusalov, Stan Uryasev; (68):1−43, 2017.
[abs][pdf][bib]
- Variational Particle Approximations
- Ardavan Saeedi, Tejas D. Kulkarni, Vikash K. Mansinghka, Samuel J. Gershman; (69):1−29, 2017.
[abs][pdf][bib]
- A Bayesian Framework for Learning Rule Sets for Interpretable Classification
- Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, Perry MacNeille; (70):1−37, 2017.
[abs][pdf][bib]
- A Robust-Equitable Measure for Feature Ranking and Selection
- A. Adam Ding, Jennifer G. Dy, Yi Li, Yale Chang; (71):1−46, 2017.
[abs][pdf][bib]
- Multiscale Strategies for Computing Optimal Transport
- Samuel Gerber, Mauro Maggioni; (72):1−32, 2017.
[abs][pdf][bib]
- Non-parametric Policy Search with Limited Information Loss
- Herke van Hoof, Gerhard Neumann, Jan Peters; (73):1−46, 2017.
[abs][pdf][bib]
- Tests of Mutual or Serial Independence of Random Vectors with Applications
- Martin Bilodeau, Aurélien Guetsop Nangue; (74):1−40, 2017.
[abs][pdf][bib]
[supplementary]
- Recovering PCA and Sparse PCA via Hybrid-(l1,l2) Sparse Sampling of Data Elements
- Abhisek Kundu, Petros Drineas, Malik Magdon-Ismail; (75):1−34, 2017.
[abs][pdf][bib]
- Quantifying the Informativeness of Similarity Measurements
- Austin J. Brockmeier, Tingting Mu, Sophia Ananiadou, John Y. Goulermas; (76):1−61, 2017.
[abs][pdf][bib]
- Time for a Change: a Tutorial for Comparing Multiple Classifiers Through Bayesian Analysis
- Alessio Benavoli, Giorgio Corani, Janez Demšar, Marco Zaffalon; (77):1−36, 2017.
[abs][pdf][bib]
- Relational Reinforcement Learning for Planning with Exogenous Effects
- David Mart\'{i}nez, Guillem Aleny\`{a}, Tony Ribeiro, Katsumi Inoue, Carme Torras; (78):1−44, 2017.
[abs][pdf][bib]
- Bayesian Tensor Regression
- Rajarshi Guhaniyogi, Shaan Qamar, David B. Dunson; (79):1−31, 2017.
[abs][pdf][bib]
- Robust Discriminative Clustering with Sparse Regularizers
- Nicolas Flammarion, Balamurugan Palaniappan, Francis Bach; (80):1−50, 2017.
[abs][pdf][bib]
- Making Decision Trees Feasible in Ultrahigh Feature and Label Dimensions
- Weiwei Liu, Ivor W. Tsang; (81):1−36, 2017.
[abs][pdf][bib]
- Learning Scalable Deep Kernels with Recurrent Structure
- Maruan Al-Shedivat, Andrew Gordon Wilson, Yunus Saatchi, Zhiting Hu, Eric P. Xing; (82):1−37, 2017.
[abs][pdf][bib]
- Convolutional Neural Networks Analyzed via Convolutional Sparse Coding
- Vardan Papyan, Yaniv Romano, Michael Elad; (83):1−52, 2017.
[abs][pdf][bib]
- Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization
- Yuchen Zhang, Lin Xiao; (84):1−42, 2017.
[abs][pdf][bib]
- Angle-based Multicategory Distance-weighted SVM
- Hui Sun, Bruce A. Craig, Lingsong Zhang; (85):1−21, 2017.
[abs][pdf][bib]
- Minimax Estimation of Kernel Mean Embeddings
- Ilya Tolstikhin, Bharath K. Sriperumbudur, Krikamol Mu, et; (86):1−47, 2017.
[abs][pdf][bib]
- The Impact of Random Models on Clustering Similarity
- Alexander J. Gates, Yong-Yeol Ahn; (87):1−28, 2017.
[abs][pdf][bib]
- Hierarchical Clustering via Spreading Metrics
- Aurko Roy, Sebastian Pokutta; (88):1−35, 2017.
[abs][pdf][bib]
- The MADP Toolbox: An Open Source Library for Planning and Learning in (Multi-)Agent Systems
- Frans A. Oliehoek, Matthijs T. J. Spaan, Bas Terwijn, Philipp Robbel, Jo\~{a}o V. Messias; (89):1−5, 2017. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[code]
- A survey of Algorithms and Analysis for Adaptive Online Learning
- H. Brendan McMahan; (90):1−50, 2017.
[abs][pdf][bib]
- A distributed block coordinate descent method for training l1 regularized linear classifiers
- Dhruv Mahajan, S. Sathiya Keerthi, S. Sundararajan; (91):1−35, 2017.
[abs][pdf][bib]
- Distributed Learning with Regularized Least Squares
- Shao-Bo Lin, Xin Guo, Ding-Xuan Zhou; (92):1−31, 2017.
[abs][pdf][bib]
- Identifying Unreliable and Adversarial Workers in Crowdsourced Labeling Tasks
- Srikanth Jagabathula, Lakshminarayanan Subramanian, Ashwin Venkataraman; (93):1−67, 2017.
[abs][pdf][bib]
- An Easy-to-hard Learning Paradigm for Multiple Classes and Multiple Labels
- Weiwei Liu, Ivor W. Tsang, Klaus-Robert M\"{u}ller; (94):1−38, 2017.
[abs][pdf][bib]
- Fisher Consistency for Prior Probability Shift
- Dirk Tasche; (95):1−32, 2017.
[abs][pdf][bib]
- openXBOW -- Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit
- Maximilian Schmitt, Björn Schuller; (96):1−5, 2017. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[code]
- Optimal Rates for Multi-pass Stochastic Gradient Methods
- Junhong Lin, Lorenzo Rosasco; (97):1−47, 2017.
[abs][pdf][bib]
- Rank Determination for Low-Rank Data Completion
- Morteza Ashraphijuo, Xiaodong Wang, Vaneet Aggarwal; (98):1−29, 2017.
[abs][pdf][bib]
- Bayesian Network Learning via Topological Order
- Young Woong Park, Diego Klabjan; (99):1−32, 2017.
[abs][pdf][bib]
- Stability of Controllers for Gaussian Process Dynamics
- Julia Vinogradska, Bastian Bischoff, Duy Nguyen-Tuong, Jan Peters; (100):1−37, 2017.
[abs][pdf][bib]
- Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression
- Aymeric Dieuleveut, Nicolas Flammarion, Francis Bach; (101):1−51, 2017.
[abs][pdf][bib]
- Confidence Sets with Expected Sizes for Multiclass Classification
- Christophe Denis, Mohamed Hebiri; (102):1−28, 2017.
[abs][pdf][bib]
- Online Learning to Rank with Top-k Feedback
- Sougata Chaudhuri, Ambuj Tewari; (103):1−50, 2017.
[abs][pdf][bib]
- A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation
- Thang D. Bui, Josiah Yan, Richard E. Turner; (104):1−72, 2017.
[abs][pdf][bib]
- Accelerating Stochastic Composition Optimization
- Mengdi Wang, Ji Liu, Ethan X. Fang; (105):1−23, 2017.
[abs][pdf][bib]
- Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server
- Leonard Hasenclever, Stefan Webb, Thibaut Lienart, Sebastian Vollmer, Balaji Lakshminarayanan, Charles Blundell, Yee Whye Teh; (106):1−37, 2017.
[abs][pdf][bib]
- Optimal Dictionary for Least Squares Representation
- Mohammed Rayyan Sheriff, Debasish Chatterjee; (107):1−28, 2017.
[abs][pdf][bib]
- Computational Limits of A Distributed Algorithm for Smoothing Spline
- Zuofeng Shang, Guang Cheng; (108):1−37, 2017.
[abs][pdf][bib]
- Hinge-Loss Markov Random Fields and Probabilistic Soft Logic
- Stephen H. Bach, Matthias Broecheler, Bert Huang, Lise Getoor; (109):1−67, 2017.
[abs][pdf][bib]
- Clustering with Hidden Markov Model on Variable Blocks
- Lin Lin, Jia Li; (110):1−49, 2017.
[abs][pdf][bib]
- Approximation Vector Machines for Large-scale Online Learning
- Trung Le, Tu Dinh Nguyen, Vu Nguyen, Dinh Phung; (111):1−55, 2017.
[abs][pdf][bib]
- Efficient Sampling from Time-Varying Log-Concave Distributions
- Hariharan Narayanan, Alexer Rakhlin; (112):1−29, 2017.
[abs][pdf][bib]
- Document Neural Autoregressive Distribution Estimation
- Stanislas Lauly, Yin Zheng, Alex, re Allauzen, Hugo Larochelle; (113):1−24, 2017.
[abs][pdf][bib]
- Target Curricula via Selection of Minimum Feature Sets: a Case Study in Boolean Networks
- Shannon Fenn, Pablo Moscato; (114):1−26, 2017.
[abs][pdf][bib]
- A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization
- Shun Zheng, Jialei Wang, Fen Xia, Wei Xu, Tong Zhang; (115):1−52, 2017.
[abs][pdf][bib]
- Second-Order Stochastic Optimization for Machine Learning in Linear Time
- Naman Agarwal, Brian Bullins, Elad Hazan; (116):1−40, 2017.
[abs][pdf][bib]
[erratum]
- Regularized Estimation and Testing for High-Dimensional Multi-Block Vector-Autoregressive Models
- Jiahe Lin, George Michailidis; (117):1−49, 2017.
[abs][pdf][bib]
- Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network
- Zheng-Chu Guo, Lei Shi, Qiang Wu; (118):1−25, 2017.
[abs][pdf][bib]
- Probabilistic Line Searches for Stochastic Optimization
- Maren Mahsereci, Philipp Hennig; (119):1−59, 2017.
[abs][pdf][bib]
- Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions
- Ricardo Silva, Shohei Shimizu; (120):1−49, 2017.
[abs][pdf][bib]
- Classification of Time Sequences using Graphs of Temporal Constraints
- Mathieu Guillame-Bert, Artur Dubrawski; (121):1−34, 2017.
[abs][pdf][bib]
- Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement
- Jason D. Lee, Qihang Lin, Tengyu Ma, Tianbao Yang; (122):1−43, 2017.
[abs][pdf][bib]
- Kernel Partial Least Squares for Stationary Data
- Marco Singer, Tatyana Krivobokova, Axel Munk; (123):1−41, 2017.
[abs][pdf][bib]
- Robust and Scalable Bayes via a Median of Subset Posterior Measures
- Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin, David B. Dunson; (124):1−40, 2017.
[abs][pdf][bib]
- Statistical and Computational Guarantees for the Baum-Welch Algorithm
- Fanny Yang, Sivaraman Balakrishnan, Martin J. Wainwright; (125):1−53, 2017.
[abs][pdf][bib]
- Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling
- Christophe Dupuy, Francis Bach; (126):1−45, 2017.
[abs][pdf][bib]
- Poisson Random Fields for Dynamic Feature Models
- Valerio Perrone, Paul A. Jenkins, Dario Spanò, Yee Whye Teh; (127):1−45, 2017.
[abs][pdf][bib]
- Gap Safe Screening Rules for Sparsity Enforcing Penalties
- Eugene Ndiaye, Olivier Fercoq, Alex, re Gramfort, Joseph Salmon; (128):1−33, 2017.
[abs][pdf][bib]
- Minimax Filter: Learning to Preserve Privacy from Inference Attacks
- Jihun Hamm; (129):1−31, 2017.
[abs][pdf][bib]
- Knowledge Graph Completion via Complex Tensor Factorization
- Théo Trouillon, Christopher R. Dance, Éric Gaussier, Johannes Welbl, Sebastian Riedel, Guillaume Bouchard; (130):1−38, 2017.
[abs][pdf][bib]
- Stabilized Sparse Online Learning for Sparse Data
- Yuting Ma, Tian Zheng; (131):1−36, 2017.
[abs][pdf][bib]
- Active-set Methods for Submodular Minimization Problems
- K. S. Sesh Kumar, Francis Bach; (132):1−31, 2017.
[abs][pdf][bib]
- A Bayesian Mixed-Effects Model to Learn Trajectories of Changes from Repeated Manifold-Valued Observations
- Jean-Baptiste Schiratti, Stéphanie Allassonnière, Olivier Colliot, Stanley Durrleman; (133):1−33, 2017.
[abs][pdf][bib]
- Stochastic Gradient Descent as Approximate Bayesian Inference
- Stephan M, t, Matthew D. Hoffman, David M. Blei; (134):1−35, 2017.
[abs][pdf][bib]
- STORE: Sparse Tensor Response Regression and Neuroimaging Analysis
- Will Wei Sun, Lexin Li; (135):1−37, 2017.
[abs][pdf][bib]
- A Survey of Preference-Based Reinforcement Learning Methods
- Christian Wirth, Riad Akrour, Gerhard Neumann, Johannes Fürnkranz; (136):1−46, 2017.
[abs][pdf][bib]
- Generalized SURE for optimal shrinkage of singular values in low-rank matrix denoising
- Jérémie Bigot, Charles Deledalle, Delphine Féral; (137):1−50, 2017.
[abs][pdf][bib]
- Dimension Estimation Using Random Connection Models
- Paulo Serra, Michel M, jes; (138):1−35, 2017.
[abs][pdf][bib]
- Bayesian Inference for Spatio-temporal Spike-and-Slab Priors
- Michael Riis Andersen, Aki Vehtari, Ole Winther, Lars Kai Hansen; (139):1−58, 2017.
[abs][pdf][bib]
- Adaptive Randomized Dimension Reduction on Massive Data
- Gregory Darnell, Stoyan Georgiev, Sayan Mukherjee, Barbara E Engelhardt; (140):1−30, 2017.
[abs][pdf][bib]
- A Nonconvex Approach for Phase Retrieval: Reshaped Wirtinger Flow and Incremental Algorithms
- Huishuai Zhang, Yingbin Liang, Yuejie Chi; (141):1−35, 2017.
[abs][pdf][bib]
- Consistency, Breakdown Robustness, and Algorithms for Robust Improper Maximum Likelihood Clustering
- Pietro Coretto, Christian Hennig; (142):1−39, 2017.
[abs][pdf][bib]
- On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models
- Yining Wang, Adams Wei Yu, Aarti Singh; (143):1−41, 2017.
[abs][pdf][bib]
- Generalized Conditional Gradient for Sparse Estimation
- Yaoliang Yu, Xinhua Zhang, Dale Schuurmans; (144):1−46, 2017.
[abs][pdf][bib]
- Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities
- Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvári; (145):1−31, 2017.
[abs][pdf][bib]
- Regularization and the small-ball method II: complexity dependent error rates
- Guillaume Lecué, Shahar Mendelson; (146):1−48, 2017.
[abs][pdf][bib]
- Matrix Completion with Noisy Entries and Outliers
- Raymond K. W. Wong, Thomas C. M. Lee; (147):1−25, 2017.
[abs][pdf][bib]
- Faithfulness of Probability Distributions and Graphs
- Kayvan Sadeghi; (148):1−29, 2017.
[abs][pdf][bib]
- Community Extraction in Multilayer Networks with Heterogeneous Community Structure
- James D. Wilson, John Palowitch, Shankar Bhamidi, Andrew B. Nobel; (149):1−49, 2017.
[abs][pdf][bib]
- On Binary Embedding using Circulant Matrices
- Felix X. Yu, Aditya Bhaskara, Sanjiv Kumar, Yunchao Gong, Shih-Fu Chang; (150):1−30, 2018.
[abs][pdf][bib]
- Variational Fourier Features for Gaussian Processes
- James Hensman, Nicolas Durrande, Arno Solin; (151):1−52, 2018.
[abs][pdf][bib]
- HyperTools: a Python Toolbox for Gaining Geometric Insights into High-Dimensional Data
- Andrew C. Heusser, Kirsten Ziman, Lucy L. W. Owen, Jeremy R. Manning; (152):1−6, 2018. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[code]
[webpage]
- Automatic Differentiation in Machine Learning: a Survey
- Atilim Gunes Baydin, Barak A. Pearlmutter, Alexey Andreyevich Radul, Jeffrey Mark Siskind; (153):1−43, 2018.
[abs][pdf][bib]
- Normal Bandits of Unknown Means and Variances
- Wesley Cowan, Junya Honda, Michael N. Katehakis; (154):1−28, 2018.
[abs][pdf][bib]
- Cost-Sensitive Learning with Noisy Labels
- Nagarajan Natarajan, Inderjit S. Dhillon, Pradeep Ravikumar, Ambuj Tewari; (155):1−33, 2018.
[abs][pdf][bib]
- Provably Correct Algorithms for Matrix Column Subset Selection with Selectively Sampled Data
- Yining Wang, Aarti Singh; (156):1−42, 2018.
[abs][pdf][bib]
- A Study of the Classification of Low-Dimensional Data with Supervised Manifold Learning
- Elif Vural, Christine Guillemot; (157):1−55, 2018.
[abs][pdf][bib]
- Probabilistic preference learning with the Mallows rank model
- Valeria Vitelli, Øystein Sørensen, Marta Crispino, Arnoldo Frigessi, Elja Arjas; (158):1−49, 2018.
[abs][pdf][bib]
- Robust Topological Inference: Distance To a Measure and Kernel Distance
- Fr{\'e}d{\'e}ric Chazal, Brittany Fasy, Fabrizio Lecci, Bertr, Michel, Aless, ro Rinaldo, Larry Wasserman; (159):1−40, 2018.
[abs][pdf][bib]
- Training Gaussian Mixture Models at Scale via Coresets
- Mario Lucic, Matthew Faulkner, Andreas Krause, Dan Feldman; (160):1−25, 2018.
[abs][pdf][bib]
- Gradient Estimation with Simultaneous Perturbation and Compressive Sensing
- Vivek S. Borkar, Vikranth R. Dwaracherla, Neeraja Sahasrabudhe; (161):1−27, 2018.
[abs][pdf][bib]
- Principled Selection of Hyperparameters in the Latent Dirichlet Allocation Model
- Clint P. George, Hani Doss; (162):1−38, 2018.
[abs][pdf][bib]
- Deep Learning the Ising Model Near Criticality
- Alan Morningstar, Roger G. Melko; (163):1−17, 2018.
[abs][pdf][bib]
- pomegranate: Fast and Flexible Probabilistic Modeling in Python
- Jacob Schreiber; (164):1−6, 2018.
[abs][pdf][bib]
- Maximum Principle Based Algorithms for Deep Learning
- Qianxiao Li, Long Chen, Cheng Tai, Weinan E; (165):1−29, 2018.
[abs][pdf][bib]
- Gradient Hard Thresholding Pursuit
- Xiao-Tong Yuan, Ping Li, Tong Zhang; (166):1−43, 2018.
[abs][pdf][bib]
- Risk-Constrained Reinforcement Learning with Percentile Risk Criteria
- Yinlam Chow, Mohammad Ghavamzadeh, Lucas Janson, Marco Pavone; (167):1−51, 2018.
[abs][pdf][bib]
- Local Identifiability of $\ell_1$-minimization Dictionary Learning: a Sufficient and Almost Necessary Condition
- Siqi Wu, Bin Yu; (168):1−56, 2018.
[abs][pdf][bib]
- In Search of Coherence and Consensus: Measuring the Interpretability of Statistical Topics
- Fred Morstatter, Huan Liu; (169):1−32, 2018.
[abs][pdf][bib]
- On the Behavior of Intrinsically High-Dimensional Spaces: Distances, Direct and Reverse Nearest Neighbors, and Hubness
- Fabrizio Angiulli; (170):1−60, 2018.
[abs][pdf][bib]
- Convergence of Unregularized Online Learning Algorithms
- Yunwen Lei, Lei Shi, Zheng-Chu Guo; (171):1−33, 2018.
[abs][pdf][bib]
- Convergence Analysis of Distributed Inference with Vector-Valued Gaussian Belief Propagation
- Jian Du, Shaodan Ma, Yik-Chung Wu, Soummya Kar, José M. F. Moura; (172):1−38, 2018.
[abs][pdf][bib]
- auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks
- Michael Freitag, Shahin Amiriparian, Sergey Pugachevskiy, Nicholas Cummins, Bj\"{o}rn Schuller; (173):1−5, 2018.
[abs][pdf][bib]
- On the Stability of Feature Selection Algorithms
- Sarah Nogueira, Konstantinos Sechidis, Gavin Brown; (174):1−54, 2018.
[abs][pdf][bib]
- Maximum Likelihood Estimation for Mixtures of Spherical Gaussians is NP-hard
- Christopher Tosh, Sanjoy Dasgupta; (175):1−11, 2018.
[abs][pdf][bib]
- The DFS Fused Lasso: Linear-Time Denoising over General Graphs
- Oscar Hernan Madrid Padilla, James Sharpnack, James G. Scott, Ryan J. Tibshirani; (176):1−36, 2018.
[abs][pdf][bib]
- Community Detection and Stochastic Block Models: Recent Developments
- Emmanuel Abbe; (177):1−86, 2018.
[abs][pdf][bib]
- On $b$-bit Min-wise Hashing for Large-scale Regression and Classification with Sparse Data
- Rajen D. Shah, Nicolai Meinshausen; (178):1−42, 2018.
[abs][pdf][bib]
- Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity
- Quanming Yao, James T. Kwok; (179):1−52, 2018.
[abs][pdf][bib]
- Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios
- Hiroaki Sasaki, Takafumi Kanamori, Aapo Hyvärinen, Gang Niu, Masashi Sugiyama; (180):1−47, 2018.
[abs][pdf][bib]
- To Tune or Not to Tune the Number of Trees in Random Forest
- Philipp Probst, Anne-Laure Boulesteix; (181):1−18, 2018.
[abs][pdf][bib]
- Divide-and-Conquer for Debiased $l_1$-norm Support Vector Machine in Ultra-high Dimensions
- Heng Lian, Zengyan Fan; (182):1−26, 2018.
[abs][pdf][bib]
- Beyond the Hazard Rate: More Perturbation Algorithms for Adversarial Multi-armed Bandits
- Zifan Li, Ambuj Tewari; (183):1−24, 2018.
[abs][pdf][bib]
- On Faster Convergence of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization
- Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Mingyi Hong; (184):1−24, 2018.
[abs][pdf][bib]
- Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
- Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar; (185):1−52, 2018.
[abs][pdf][bib]
- Submatrix localization via message passing
- Bruce Hajek, Yihong Wu, Jiaming Xu; (186):1−52, 2018.
[abs][pdf][bib]
- Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations
- Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio; (187):1−30, 2018.
[abs][pdf][bib]
- Significance-based community detection in weighted networks
- John Palowitch, Shankar Bhamidi, Andrew B. Nobel; (188):1−48, 2018.
[abs][pdf][bib]
- Kernel Method for Persistence Diagrams via Kernel Embedding and Weight Factor
- Genki Kusano, Kenji Fukumizu, Yasuaki Hiraoka; (189):1−41, 2018.
[abs][pdf][bib]
- Pycobra: A Python Toolbox for Ensemble Learning and Visualisation
- Benjamin Guedj, Bhargav Srinivasa Desikan; (190):1−5, 2018. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[code]
[webpage]
- KELP: a Kernel-based Learning Platform
- Simone Filice, Giuseppe Castellucci, Giovanni Da San Martino, Aless, ro Moschitti, Danilo Croce, Roberto Basili; (191):1−5, 2018. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[code]
[webpage]
- Uncovering Causality from Multivariate Hawkes Integrated Cumulants
- Massil Achab, Emmanuel Bacry, Stéphane Gaïffas, Iacopo Mastromatteo, Jean-François Muzy; (192):1−28, 2018.
[abs][pdf][bib]
- Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research
- Jennifer Wortman Vaughan; (193):1−46, 2018.
[abs][pdf][bib]
- Enhancing Identification of Causal Effects by Pruning
- Santtu Tikka, Juha Karvanen; (194):1−23, 2018.
[abs][pdf][bib]
- Active Nearest-Neighbor Learning in Metric Spaces
- Aryeh Kontorovich, Sivan Sabato, Ruth Urner; (195):1−38, 2018.
[abs][pdf][bib]
- From Predictive Methods to Missing Data Imputation: An Optimization Approach
- Dimitris Bertsimas, Colin Pawlowski, Ying Daisy Zhuo; (196):1−39, 2018.
[abs][pdf][bib]
- Saturating Splines and Feature Selection
- Nicholas Boyd, Trevor Hastie, Stephen Boyd, Benjamin Recht, Michael I. Jordan; (197):1−32, 2018.
[abs][pdf][bib]
- Nonasymptotic convergence of stochastic proximal point methods for constrained convex optimization
- Andrei Patrascu, Ion Necoara; (198):1−42, 2018.
[abs][pdf][bib]
- Simple, Robust and Optimal Ranking from Pairwise Comparisons
- Nihar B. Shah, Martin J. Wainwright; (199):1−38, 2018.
[abs][pdf][bib]
- Surprising properties of dropout in deep networks
- David P. Helmbold, Philip M. Long; (200):1−28, 2018.
[abs][pdf][bib]
- Exact Learning of Lightweight Description Logic Ontologies
- Boris Konev, Carsten Lutz, Ana Ozaki, Frank Wolter; (201):1−63, 2018.
[abs][pdf][bib]
- Sparse Concordance-assisted Learning for Optimal Treatment Decision
- Shuhan Liang, Wenbin Lu, Rui Song, Lan Wang; (202):1−26, 2018.
[abs][pdf][bib]
- Post-Regularization Inference for Time-Varying Nonparanormal Graphical Models
- Junwei Lu, Mladen Kolar, Han Liu; (203):1−78, 2018.
[abs][pdf][bib]
- Permuted and Augmented Stick-Breaking Bayesian Multinomial Regression
- Quan Zhang, Mingyuan Zhou; (204):1−33, 2018.
[abs][pdf][bib]
- Steering Social Activity: A Stochastic Optimal Control Point Of View
- Ali Zarezade, Abir De, Utkarsh Upadhyay, Hamid R. Rabiee, Manuel Gomez-Rodriguez; (205):1−35, 2018.
[abs][pdf][bib]
- The Search Problem in Mixture Models
- Avik Ray, Joe Neeman, Sujay Sanghavi, Sanjay Shakkottai; (206):1−61, 2018.
[abs][pdf][bib]
- An $\ell_{\infty}$ Eigenvector Perturbation Bound and Its Application
- Jianqing Fan, Weichen Wang, Yiqiao Zhong; (207):1−42, 2018.
[abs][pdf][bib]
- A Tight Bound of Hard Thresholding
- Jie Shen, Ping Li; (208):1−42, 2018.
[abs][pdf][bib]
- Estimation of Graphical Models through Structured Norm Minimization
- Davoud Ataee Tarzanagh, George Michailidis; (209):1−48, 2018.
[abs][pdf][bib]
- Sparse Exchangeable Graphs and Their Limits via Graphon Processes
- Christian Borgs, Jennifer T. Chayes, Henry Cohn, Nina Holden; (210):1−71, 2018.
[abs][pdf][bib]
- Weighted SGD for $\ell_p$ Regression with Randomized Preconditioning
- Jiyan Yang, Yin-Lam Chow, Christopher Ré, Michael W. Mahoney; (211):1−43, 2018.
[abs][pdf][bib]
- Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice
- Hongzhou Lin, Julien Mairal, Zaid Harchaoui; (212):1−54, 2018.
[abs][pdf][bib]
- Gaussian Lower Bound for the Information Bottleneck Limit
- Amichai Painsky, Naftali Tishby; (213):1−29, 2018.
[abs][pdf][bib]
- tick: a Python Library for Statistical Learning, with an emphasis on Hawkes Processes and Time-Dependent Models
- Emmanuel Bacry, Martin Bompaire, Philip Deegan, Stéphane Gaïffas, Søren V. Poulsen; (214):1−5, 2018. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[code]
[webpage]
- SGDLibrary: A MATLAB library for stochastic optimization algorithms
- Hiroyuki Kasai; (215):1−5, 2018. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[code]
- Reward Maximization Under Uncertainty: Leveraging Side-Observations on Networks
- Swapna Buccapatnam, Fang Liu, Atilla Eryilmaz, Ness B. Shroff; (216):1−34, 2018.
[abs][pdf][bib]
- Simultaneous Clustering and Estimation of Heterogeneous Graphical Models
- Botao Hao, Will Wei Sun, Yufeng Liu, Guang Cheng; (217):1−58, 2018.
[abs][pdf][bib]
- Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging
- Shusen Wang, Alex Gittens, Michael W. Mahoney; (218):1−50, 2018.
[abs][pdf][bib]
- Compact Convex Projections
- Steffen Grünewälder; (219):1−43, 2018.
[abs][pdf][bib]
- Complete Graphical Characterization and Construction of Adjustment Sets in Markov Equivalence Classes of Ancestral Graphs
- Emilija Perkovi\'c, Johannes Textor, Markus Kalisch, Marloes H. Maathuis; (220):1−62, 2018.
[abs][pdf][bib]
- Katyusha: The First Direct Acceleration of Stochastic Gradient Methods
- Zeyuan Allen-Zhu; (221):1−51, 2018.
[abs][pdf][bib]
- Average Stability is Invariant to Data Preconditioning. Implications to Exp-concave Empirical Risk Minimization
- Alon Gonen, Shai Shalev-Shwartz; (222):1−13, 2018.
[abs][pdf][bib]
- Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification
- Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; (223):1−42, 2018.
[abs][pdf][bib]
- Learning Quadratic Variance Function (QVF) DAG Models via OverDispersion Scoring (ODS)
- Gunwoong Park, Garvesh Raskutti; (224):1−44, 2018.
[abs][pdf][bib]
- Improved spectral community detection in large heterogeneous networks
- Hafiz TIOMOKO ALI, Romain COUILLET; (225):1−49, 2018.
[abs][pdf][bib]
- Statistical Inference on Random Dot Product Graphs: a Survey
- Avanti Athreya, Donniell E. Fishkind, Minh Tang, Carey E. Priebe, Youngser Park, Joshua T. Vogelstein, Keith Levin, Vince Lyzinski, Yichen Qin, Daniel L Sussman; (226):1−92, 2018.
[abs][pdf][bib]
- Rate of Convergence of $k$-Nearest-Neighbor Classification Rule
- Maik Döring, László Györfi, Harro Walk; (227):1−16, 2018.
[abs][pdf][bib]
- A Theory of Learning with Corrupted Labels
- Brendan van Rooyen, Robert C. Williamson; (228):1−50, 2018.
[abs][pdf][bib]
- Interactive Algorithms: Pool, Stream and Precognitive Stream
- Sivan Sabato, Tom Hess; (229):1−39, 2018.
[abs][pdf][bib]
- CoCoA: A General Framework for Communication-Efficient Distributed Optimization
- Virginia Smith, Simone Forte, Chenxin Ma, Martin Takáč, Michael I. Jordan, Martin Jaggi; (230):1−49, 2018.
[abs][pdf][bib]
- Concentration inequalities for empirical processes of linear time series
- Likai Chen, Wei Biao Wu; (231):1−46, 2018.
[abs][pdf][bib]
- A Cluster Elastic Net for Multivariate Regression
- Bradley S. Price, Ben Sherwood; (232):1−39, 2018.
[abs][pdf][bib]
- Characteristic and Universal Tensor Product Kernels
- Zoltán Szabó, Bharath K. Sriperumbudur; (233):1−29, 2018.
[abs][pdf][bib]
- Learning Certifiably Optimal Rule Lists for Categorical Data
- Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo Seltzer, Cynthia Rudin; (234):1−78, 2018.
[abs][pdf][bib]
