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Proceedings of Machine Learning Research

Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research
Proceedings of Machine Learning Research
PMLR · 2026-06-02 · via Proceedings of Machine Learning Research

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Volume 35: Conference on Learning Theory, 13-15 June 2014, Barcelona, Spain

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Editors: Maria Florina Balcan, Vitaly Feldman, Csaba Szepesvári

[bib][citeproc]

Contents:

  • Preface
  • Regular Papers
  • Open Problems

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Preface

Preface

; Proceedings of The 27th Conference on Learning Theory, PMLR 35:1-2

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Regular Papers

Open Problems

Open Problem: Tightness of maximum likelihood semidefinite relaxations

Afonso S. Bandeira, Yuehaw Khoo, Amit Singer; Proceedings of The 27th Conference on Learning Theory, PMLR 35:1265-1267

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Open Problem: A (Missing) Boosting-type Convergence Result for AdaBoost.MH with Factorized Multi-class Classifiers

Balázs Kégl; Proceedings of The 27th Conference on Learning Theory, PMLR 35:1268-1275

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Open Problem: Finding Good Cascade Sampling Processes for the Network Inference Problem

Manuel Gomez-Rodriguez, Le Song, Bernhard Schoelkopf; Proceedings of The 27th Conference on Learning Theory, PMLR 35:1276-1279

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Open Problem: Tensor Decompositions: Algorithms up to the Uniqueness Threshold?

Aditya Bhaskara, Moses Charikar, Ankur Moitra, Aravindan Vijayaraghavan; Proceedings of The 27th Conference on Learning Theory, PMLR 35:1280-1282

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Open Problem: The Statistical Query Complexity of Learning Sparse Halfspaces

Vitaly Feldman; Proceedings of The 27th Conference on Learning Theory, PMLR 35:1283-1289

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Open Problem: Online Local Learning

Paul Christiano; Proceedings of The 27th Conference on Learning Theory, PMLR 35:1290-1294

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Open Problem: Shifting Experts on Easy Data

Manfred K. Warmuth, Wouter M. Koolen; Proceedings of The 27th Conference on Learning Theory, PMLR 35:1295-1298

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Open Problem: Efficient Online Sparse Regression

Satyen Kale; Proceedings of The 27th Conference on Learning Theory, PMLR 35:1299-1301

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Distribution-independent Reliable Learning

Varun Kanade, Justin Thaler; Proceedings of The 27th Conference on Learning Theory, PMLR 35:3-24

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Learning without concentration

Shahar Mendelson; Proceedings of The 27th Conference on Learning Theory, PMLR 35:25-39

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Uniqueness of Ordinal Embedding

Matthäus Kleindessner, Ulrike Luxburg; Proceedings of The 27th Conference on Learning Theory, PMLR 35:40-67

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Bayes-Optimal Scorers for Bipartite Ranking

Aditya Krishna Menon, Robert C. Williamson; Proceedings of The 27th Conference on Learning Theory, PMLR 35:68-106

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Multiarmed Bandits With Limited Expert Advice

Satyen Kale; Proceedings of The 27th Conference on Learning Theory, PMLR 35:107-122

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Learning Sparsely Used Overcomplete Dictionaries

Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli, Rashish Tandon; Proceedings of The 27th Conference on Learning Theory, PMLR 35:123-137

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Community Detection via Random and Adaptive Sampling

Se-Young Yun, Alexandre Proutiere; Proceedings of The 27th Conference on Learning Theory, PMLR 35:138-175

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A second-order bound with excess losses

Pierre Gaillard, Gilles Stoltz, Tim van Erven; Proceedings of The 27th Conference on Learning Theory, PMLR 35:176-196

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Logistic Regression: Tight Bounds for Stochastic and Online Optimization

Elad Hazan, Tomer Koren, Kfir Y. Levy; Proceedings of The 27th Conference on Learning Theory, PMLR 35:197-209

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Higher-Order Regret Bounds with Switching Costs

Eyal Gofer; Proceedings of The 27th Conference on Learning Theory, PMLR 35:210-243

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The Complexity of Learning Halfspaces using Generalized Linear Methods

Amit Daniely, Nati Linial, Shai Shalev-Shwartz; Proceedings of The 27th Conference on Learning Theory, PMLR 35:244-286

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Optimal learners for multiclass problems

Amit Daniely, Shai Shalev-Shwartz; Proceedings of The 27th Conference on Learning Theory, PMLR 35:287-316

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Stochastic Regret Minimization via Thompson Sampling

Sudipto Guha, Kamesh Munagala; Proceedings of The 27th Conference on Learning Theory, PMLR 35:317-338

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Approachability in unknown games: Online learning meets multi-objective optimization

Shie Mannor, Vianney Perchet, Gilles Stoltz; Proceedings of The 27th Conference on Learning Theory, PMLR 35:339-355

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Belief propagation, robust reconstruction and optimal recovery of block models

Elchanan Mossel, Joe Neeman, Allan Sly; Proceedings of The 27th Conference on Learning Theory, PMLR 35:356-370

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Sample Compression for Multi-label Concept Classes

Rahim Samei, Pavel Semukhin, Boting Yang, Sandra Zilles; Proceedings of The 27th Conference on Learning Theory, PMLR 35:371-393

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Finding a most biased coin with fewest flips

Karthekeyan Chandrasekaran, Richard Karp; Proceedings of The 27th Conference on Learning Theory, PMLR 35:394-407

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Volumetric Spanners: an Efficient Exploration Basis for Learning

Elad Hazan, Zohar Karnin, Raghu Meka; Proceedings of The 27th Conference on Learning Theory, PMLR 35:408-422

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lil’ UCB : An Optimal Exploration Algorithm for Multi-Armed Bandits

Kevin Jamieson, Matthew Malloy, Robert Nowak, Sébastien Bubeck; Proceedings of The 27th Conference on Learning Theory, PMLR 35:423-439

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An Inequality with Applications to Structured Sparsity and Multitask Dictionary Learning

Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes; Proceedings of The 27th Conference on Learning Theory, PMLR 35:440-460

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On the Complexity of A/B Testing

Emilie Kaufmann, Olivier Cappé, Aurélien Garivier; Proceedings of The 27th Conference on Learning Theory, PMLR 35:461-481

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Elicitation and Identification of Properties

Ingo Steinwart, Chloé Pasin, Robert Williamson, Siyu Zhang; Proceedings of The 27th Conference on Learning Theory, PMLR 35:482-526

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The sample complexity of agnostic learning under deterministic labels

Shai Ben-David, Ruth Urner; Proceedings of The 27th Conference on Learning Theory, PMLR 35:527-542

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Density-preserving quantization with application to graph downsampling

Morteza Alamgir, Gábor Lugosi, Ulrike Luxburg; Proceedings of The 27th Conference on Learning Theory, PMLR 35:543-559

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A Convex Formulation for Mixed Regression with Two Components: Minimax Optimal Rates

Yudong Chen, Xinyang Yi, Constantine Caramanis; Proceedings of The 27th Conference on Learning Theory, PMLR 35:560-604

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Efficiency of conformalized ridge regression

Evgeny Burnaev, Vladimir Vovk; Proceedings of The 27th Conference on Learning Theory, PMLR 35:605-622

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Most Correlated Arms Identification

Che-Yu Liu, Sébastien Bubeck; Proceedings of The 27th Conference on Learning Theory, PMLR 35:623-637

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Fast matrix completion without the condition number

Moritz Hardt, Mary Wootters; Proceedings of The 27th Conference on Learning Theory, PMLR 35:638-678

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Learning Coverage Functions and Private Release of Marginals

Vitaly Feldman, Pravesh Kothari; Proceedings of The 27th Conference on Learning Theory, PMLR 35:679-702

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Computational Limits for Matrix Completion

Moritz Hardt, Raghu Meka, Prasad Raghavendra, Benjamin Weitz; Proceedings of The 27th Conference on Learning Theory, PMLR 35:703-725

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Robust Multi-objective Learning with Mentor Feedback

Alekh Agarwal, Ashwinkumar Badanidiyuru, Miroslav Dudík, Robert E. Schapire, Aleksandrs Slivkins; Proceedings of The 27th Conference on Learning Theory, PMLR 35:726-741

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Uniqueness of Tensor Decompositions with Applications to Polynomial Identifiability

Aditya Bhaskara, Moses Charikar, Aravindan Vijayaraghavan; Proceedings of The 27th Conference on Learning Theory, PMLR 35:742-778

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New Algorithms for Learning Incoherent and Overcomplete Dictionaries

Sanjeev Arora, Rong Ge, Ankur Moitra; Proceedings of The 27th Conference on Learning Theory, PMLR 35:779-806

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Online Linear Optimization via Smoothing

Jacob Abernethy, Chansoo Lee, Abhinav Sinha, Ambuj Tewari; Proceedings of The 27th Conference on Learning Theory, PMLR 35:807-823

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Learning Mixtures of Discrete Product Distributions using Spectral Decompositions

Prateek Jain, Sewoong Oh; Proceedings of The 27th Conference on Learning Theory, PMLR 35:824-856

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Localized Complexities for Transductive Learning

Ilya Tolstikhin, Gilles Blanchard, Marius Kloft; Proceedings of The 27th Conference on Learning Theory, PMLR 35:857-884

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On the Consistency of Output Code Based Learning Algorithms for Multiclass Learning Problems

Harish G. Ramaswamy, Balaji Srinivasan Babu, Shivani Agarwal, Robert C. Williamson; Proceedings of The 27th Conference on Learning Theory, PMLR 35:885-902

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Edge Label Inference in Generalized Stochastic Block Models: from Spectral Theory to Impossibility Results

Jiaming Xu, Laurent Massoulié, Marc Lelarge; Proceedings of The 27th Conference on Learning Theory, PMLR 35:903-920

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Lower Bounds on the Performance of Polynomial-time Algorithms for Sparse Linear Regression

Yuchen Zhang, Martin J. Wainwright, Michael I. Jordan; Proceedings of The 27th Conference on Learning Theory, PMLR 35:921-948

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Follow the Leader with Dropout Perturbations

Tim Van Erven, Wojciech Kotłowski, Manfred K. Warmuth; Proceedings of The 27th Conference on Learning Theory, PMLR 35:949-974

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Lipschitz Bandits: Regret Lower Bound and Optimal Algorithms

Stefan Magureanu, Richard Combes, Alexandre Proutiere; Proceedings of The 27th Conference on Learning Theory, PMLR 35:975-999

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Sample Complexity Bounds on Differentially Private Learning via Communication Complexity

Vitaly Feldman, David Xiao; Proceedings of The 27th Conference on Learning Theory, PMLR 35:1000-1019

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Unconstrained Online Linear Learning in Hilbert Spaces: Minimax Algorithms and Normal Approximations

H. Brendan McMahan, Francesco Orabona; Proceedings of The 27th Conference on Learning Theory, PMLR 35:1020-1039

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Principal Component Analysis and Higher Correlations for Distributed Data

Ravi Kannan, Santosh Vempala, David Woodruff; Proceedings of The 27th Conference on Learning Theory, PMLR 35:1040-1057

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Compressed Counting Meets Compressed Sensing

Ping Li, Cun-Hui Zhang, Tong Zhang; Proceedings of The 27th Conference on Learning Theory, PMLR 35:1058-1077

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The Geometry of Losses

Robert C. Williamson; Proceedings of The 27th Conference on Learning Theory, PMLR 35:1078-1108

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Resourceful Contextual Bandits

Ashwinkumar Badanidiyuru, John Langford, Aleksandrs Slivkins; Proceedings of The 27th Conference on Learning Theory, PMLR 35:1109-1134

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The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures

Joseph Anderson, Mikhail Belkin, Navin Goyal, Luis Rademacher, James Voss; Proceedings of The 27th Conference on Learning Theory, PMLR 35:1135-1164

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Near-Optimal Herding

Nick Harvey, Samira Samadi; Proceedings of The 27th Conference on Learning Theory, PMLR 35:1165-1182

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Faster and Sample Near-Optimal Algorithms for Proper Learning Mixtures of Gaussians

Constantinos Daskalakis, Gautam Kamath; Proceedings of The 27th Conference on Learning Theory, PMLR 35:1183-1213

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Online Learning with Composite Loss Functions

Ofer Dekel, Jian Ding, Tomer Koren, Yuval Peres; Proceedings of The 27th Conference on Learning Theory, PMLR 35:1214-1231

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Online Non-Parametric Regression

Alexander Rakhlin, Karthik Sridharan; Proceedings of The 27th Conference on Learning Theory, PMLR 35:1232-1264

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