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ORCID27th COLT 2014: Barcelona, Spain
- Maria-Florina Balcan, Vitaly Feldman, Csaba Szepesvári:
Proceedings of The 27th Conference on Learning Theory, COLT 2014, Barcelona, Spain, June 13-15, 2014. JMLR Workshop and Conference Proceedings 35, JMLR.org 2014
Preface
- Preface. 1-2
Regular Papers
- Varun Kanade, Justin Thaler:
Distribution-independent Reliable Learning. 3-24 - Shahar Mendelson:
Learning without concentration. 25-39 - Matthäus Kleindessner, Ulrike von Luxburg:
Uniqueness of Ordinal Embedding. 40-67 - Aditya Krishna Menon, Robert C. Williamson:
Bayes-Optimal Scorers for Bipartite Ranking. 68-106 - Satyen Kale:
Multiarmed Bandits With Limited Expert Advice. 107-122 - Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli, Rashish Tandon:
Learning Sparsely Used Overcomplete Dictionaries. 123-137 - Se-Young Yun, Alexandre Proutière:
Community Detection via Random and Adaptive Sampling. 138-175 - Pierre Gaillard, Gilles Stoltz, Tim van Erven:
A second-order bound with excess losses. 176-196 - Elad Hazan, Tomer Koren, Kfir Y. Levy:
Logistic Regression: Tight Bounds for Stochastic and Online Optimization. 197-209 - Eyal Gofer:
Higher-Order Regret Bounds with Switching Costs. 210-243 - Amit Daniely, Nati Linial, Shai Shalev-Shwartz:
The Complexity of Learning Halfspaces using Generalized Linear Methods. 244-286 - Amit Daniely, Shai Shalev-Shwartz:
Optimal learners for multiclass problems. 287-316 - Sudipto Guha, Kamesh Munagala:
Stochastic Regret Minimization via Thompson Sampling. 317-338 - Shie Mannor, Vianney Perchet, Gilles Stoltz:
Approachability in unknown games: Online learning meets multi-objective optimization. 339-355 - Elchanan Mossel, Joe Neeman, Allan Sly:
Belief propagation, robust reconstruction and optimal recovery of block models. 356-370 - Rahim Samei, Pavel Semukhin, Boting Yang, Sandra Zilles:
Sample Compression for Multi-label Concept Classes. 371-393 - Karthekeyan Chandrasekaran, Richard M. Karp:
Finding a most biased coin with fewest flips. 394-407 - Elad Hazan, Zohar Shay Karnin, Raghu Meka:
Volumetric Spanners: an Efficient Exploration Basis for Learning. 408-422 - Kevin G. Jamieson, Matthew Malloy, Robert D. Nowak, Sébastien Bubeck:
lil' UCB : An Optimal Exploration Algorithm for Multi-Armed Bandits. 423-439 - Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes:
An Inequality with Applications to Structured Sparsity and Multitask Dictionary Learning. 440-460 - Emilie Kaufmann, Olivier Cappé, Aurélien Garivier:
On the Complexity of A/B Testing. 461-481 - Ingo Steinwart, Chloé Pasin, Robert C. Williamson, Siyu Zhang:
Elicitation and Identification of Properties. 482-526 - Shai Ben-David, Ruth Urner:
The sample complexity of agnostic learning under deterministic labels. 527-542 - Morteza Alamgir, Gábor Lugosi, Ulrike von Luxburg:
Density-preserving quantization with application to graph downsampling. 543-559 - Yudong Chen, Xinyang Yi, Constantine Caramanis:
A Convex Formulation for Mixed Regression with Two Components: Minimax Optimal Rates. 560-604 - Evgeny Burnaev, Vladimir Vovk:
Efficiency of conformalized ridge regression. 605-622 - Che-Yu Liu, Sébastien Bubeck:
Most Correlated Arms Identification. 623-637 - Moritz Hardt, Mary Wootters:
Fast matrix completion without the condition number. 638-678 - Vitaly Feldman, Pravesh Kothari:
Learning Coverage Functions and Private Release of Marginals. 679-702 - Moritz Hardt, Raghu Meka, Prasad Raghavendra, Benjamin Weitz:
Computational Limits for Matrix Completion. 703-725 - Alekh Agarwal, Ashwinkumar Badanidiyuru, Miroslav Dudík, Robert E. Schapire, Aleksandrs Slivkins:
Robust Multi-objective Learning with Mentor Feedback. 726-741 - Aditya Bhaskara, Moses Charikar, Aravindan Vijayaraghavan:
Uniqueness of Tensor Decompositions with Applications to Polynomial Identifiability. 742-778 - Sanjeev Arora, Rong Ge, Ankur Moitra:
New Algorithms for Learning Incoherent and Overcomplete Dictionaries. 779-806 - Jacob D. Abernethy, Chansoo Lee, Abhinav Sinha, Ambuj Tewari:
Online Linear Optimization via Smoothing. 807-823 - Prateek Jain, Sewoong Oh:
Learning Mixtures of Discrete Product Distributions using Spectral Decompositions. 824-856 - Ilya O. Tolstikhin, Gilles Blanchard, Marius Kloft:
Localized Complexities for Transductive Learning. 857-884 - Harish G. Ramaswamy, Balaji Srinivasan Babu, Shivani Agarwal, Robert C. Williamson:
On the Consistency of Output Code Based Learning Algorithms for Multiclass Learning Problems. 885-902 - Jiaming Xu, Laurent Massoulié, Marc Lelarge:
Edge Label Inference in Generalized Stochastic Block Models: from Spectral Theory to Impossibility Results. 903-920 - Yuchen Zhang, Martin J. Wainwright, Michael I. Jordan:
Lower bounds on the performance of polynomial-time algorithms for sparse linear regression. 921-948 - Tim van Erven, Wojciech Kotlowski:
Follow the Leader with Dropout Perturbations. 949-974 - Stefan Magureanu, Richard Combes, Alexandre Proutière:
Lipschitz Bandits: Regret Lower Bound and Optimal Algorithms. 975-999 - Vitaly Feldman, David Xiao:
Sample Complexity Bounds on Differentially Private Learning via Communication Complexity. 1000-1019 - H. Brendan McMahan, Francesco Orabona:
Unconstrained Online Linear Learning in Hilbert Spaces: Minimax Algorithms and Normal Approximations. 1020-1039 - Ravi Kannan, Santosh S. Vempala, David P. Woodruff:
Principal Component Analysis and Higher Correlations for Distributed Data. 1040-1057 - Ping Li, Cun-Hui Zhang, Tong Zhang:
Compressed Counting Meets Compressed Sensing. 1058-1077 - Robert C. Williamson:
The Geometry of Losses. 1078-1108 - Ashwinkumar Badanidiyuru, John Langford, Aleksandrs Slivkins:
Resourceful Contextual Bandits. 1109-1134 - Joseph Anderson, Mikhail Belkin, Navin Goyal, Luis Rademacher
, James R. Voss:
The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures. 1135-1164 - Nick Harvey, Samira Samadi:
Near-Optimal Herding. 1165-1182 - Constantinos Daskalakis, Gautam Kamath:
Faster and Sample Near-Optimal Algorithms for Proper Learning Mixtures of Gaussians. 1183-1213 - Ofer Dekel, Jian Ding, Tomer Koren, Yuval Peres:
Online Learning with Composite Loss Functions. 1214-1231 - Alexander Rakhlin, Karthik Sridharan:
Online Non-Parametric Regression. 1232-1264
Open Problems
- Afonso S. Bandeira, Yuehaw Khoo, Amit Singer:
Open Problem: Tightness of maximum likelihood semidefinite relaxations. 1265-1267 - Balázs Kégl:
Open Problem: A (missing) boosting-type convergence result for AdaBoost.MH with factorized multi-class classifiers. 1268-1275 - Manuel Gomez-Rodriguez, Le Song, Bernhard Schölkopf:
Open Problem: Finding Good Cascade Sampling Processes for the Network Inference Problem. 1276-1279 - Aditya Bhaskara, Moses Charikar, Ankur Moitra, Aravindan Vijayaraghavan:
Open Problem: Tensor Decompositions: Algorithms up to the Uniqueness Threshold? 1280-1282 - Vitaly Feldman:
Open Problem: The Statistical Query Complexity of Learning Sparse Halfspaces. 1283-1289 - Paul F. Christiano:
Open Problem: Online Local Learning. 1290-1294 - Manfred K. Warmuth, Wouter M. Koolen:
Open Problem: Shifting Experts on Easy Data. 1295-1298 - Satyen Kale:
Open Problem: Efficient Online Sparse Regression. 1299-1301
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