- 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.
- 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