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Accepted papers

All papers can be found here.

- Jacob Abernethy, Chansoo Lee, Abhinav Sinha and Ambuj Tewari.
Learning with Perturbations via Gaussian Smoothing

- Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli and Rashish Tandon.
Learning Sparsely Used Overcomplete Dictionaries

- Alekh Agarwal, Ashwin Badanidiyuru, Miroslav Dudik, Robert Schapire and Aleksandrs Slivkins.
Robust Multi-objective Learning with Mentor Feedback

- Morteza Alamgir, Ulrike von Luxburg and Gabor Lugosi.
Density-preserving quantization with application to graph downsampling

- Joseph Anderson, Mikhail Belkin, Navin Goyal, Luis Rademacher and James Voss.
The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures

- Sanjeev Arora, Rong Ge and Ankur Moitra.
New Algorithms for Learning Incoherent and Overcomplete Dictionaries

- Ashwinkumar Badanidiyuru, John Langford and Aleksandrs Slivkins.
Resourceful Contextual Bandits

- Shai Ben-David and Ruth Urner.
The sample complexity of agnostic learning under deterministic labels

- Aditya Bhaskara, Moses Charikar and Aravindan Vijayaraghavan.
Uniqueness of Tensor Decompositions with Applications to Polynomial Identifiability

- Evgeny Burnaev and Vladimir Vovk.
Efficiency of conformalized ridge regression

- Karthekeyan Chandrasekaran and Richard M. Karp.
Finding a most biased coin with fewest flips

- Yudong Chen, Xinyang Yi and Constantine Caramanis.
A Convex Formulation for Mixed Regression: Minimax Optimal Rates

- Amit Daniely, Nati Linial and Shai Shalev-Shwartz.
The complexity of learning halfspaces using generalized linear methods

- Amit Daniely and Shai Shalev-Shwartz.
Optimal Learners for Multiclass Problems

- Constantinos Daskalakis and Gautam Kamath.
Faster and Sample Near-Optimal Algorithms for Proper Learning Mixtures of Gaussians

- Ofer Dekel, Jian Ding, Tomer Koren and Yuval Peres.
Online Learning with Composite Loss Functions Can Be Hard

- Tim van Erven, Wojciech Kotlowski and Manfred K. Warmuth.
Follow the Leader with Dropout Perturbations

- Vitaly Feldman and Pravesh Kothari.
Learning Coverage Functions and Private Release of Marginals

- Vitaly Feldman and David Xiao.
Sample Complexity Bounds on Differentially Private Learning via Communication Complexity

- Pierre Gaillard, Gilles Stoltz and Tim van Erven.
A Second-order Bound with Excess Losses

- Eyal Gofer.
Higher-Order Regret Bounds with Switching Costs

- Sudipto Guha and Kamesh Munagala.
Stochastic Regret Minimization via Thompson Sampling

- Moritz Hardt, Raghu Meka, Prasad Raghavendra and Benjamin Weitz.
Computational Limits for Matrix Completion

- Moritz Hardt and Mary Wootters.
Fast Matrix Completion Without the Condition Number

- Elad Hazan, Zohar Karnin and Raghu Meka.
Volumetric Spanners: an Efficient Exploration Basis for Learning

- Prateek Jain and Sewoong Oh.
Learning Mixtures of Discrete Product Distributions using Spectral Decompositions

- Kevin Jamieson, Matthew Malloy, Robert Nowak and Sebastien Bubeck.
lil' UCB: An Optimal Exploration Algorithm for Multi-Armed Bandits

- Satyen Kale.
Multiarmed Bandits With Limited Expert Advice

- Varun Kanade and Justin Thaler.
Distribution-Independent Reliable Learning

- Ravindran Kannan, Santosh S. Vempala and David Woodruff.
Principal Component Analysis and Higher Correlations for Distributed Data

- Emilie Kaufmann, Olivier Cappé and Aurélien Garivier.
On the Complexity of A/B Testing

- Matthäus Kleindessner and Ulrike von Luxburg.
Uniqueness of ordinal embedding

- Kfir Levy, Elad Hazan and Tomer Koren.
Logistic Regression: Tight Bounds for Stochastic and Online Optimization

- Ping Li, Cun-Hui Zhang and Tong Zhang.
Compressed Counting Meets Compressed Sensing

- Che-Yu Liu and Sébastien Bubeck.
Most Correlated Arms Identification

- Stefan Magureanu, Richard Combes and Alexandre Proutière.
Lipschitz Bandits:Regret Lower Bounds and Optimal Algorithms

- Shie Mannor, Vianney Perchet and Gilles Stoltz.
Approachability in unknown games: Online learning meets multi-objective optimization

- Brendan McMahan and Francesco Orabona.
Unconstrained Online Linear Learning in Hilbert Spaces: Minimax Algorithms and Normal Approximations

- Shahar Mendelson.
Learning without Concentration

- Aditya Menon and Robert Williamson.
Bayes-Optimal Scorers for Bipartite Ranking

- Elchanan Mossel, Joe Neeman and Allan Sly.
Belief Propagation, Robust Reconstruction and Optimal Recovery of Block Models

- Andreas Maurer, Massimiliano Pontil and Bernardino Romera-Paredes.
An Inequality with Applications to Structured Sparsity and Multitask Dictionary Learning

- Alexander Rakhlin and Karthik Sridharan.
Online Nonparametric Regression

- Harish Ramaswamy, Balaji S.B., Shivani Agarwal and Robert Williamson.
On the Consistency of Output Code Based Learning Algorithms for Multiclass Learning Problems

- Samira Samadi and Nick Harvey.
Near-Optimal Herding

- Rahim Samei, Pavel Semukhin, Boting Yang and Sandra Zilles.
Sample Compression for Multi-label Concept Classes

- Ingo Steinwart, Chloe Pasin and Robert Williamson.
Elicitation and Identification of Properties

- Ilya Tolstikhin, Gilles Blanchard and Marius Kloft.
Localized Complexities for Transductive Learning

- Robert Williamson.
The Geometry of Losses

- Jiaming Xu, Marc Lelarge and Laurent Massoulie.
Edge Label Inference in Generalized Stochastic Block Models: from Spectral Theory to Impossibility Results

- Se-Young Yun and Alexandre Proutiere.
Community Detection via Random and Adaptive Sampling

- Yuchen Zhang, Martin Wainwright and Michael Jordan.
Lower bounds on the performance of polynomial-time algorithms for sparse linear regression