Papers
1,396 papers found
Stochastic Regret Minimization via Thompson Sampling
Sudipto Guha, Kamesh Munagala
The Complexity of Learning Halfspaces using Generalized Linear Methods
Amit Daniely, Nati Linial, Shai Shalev-Shwartz
The Geometry of Losses
Robert C. Williamson
The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures
Joseph Anderson, Mikhail Belkin, Navin Goyal et al.
The sample complexity of agnostic learning under deterministic labels
Shai Ben-David, Ruth Urner
Unconstrained Online Linear Learning in Hilbert Spaces: Minimax Algorithms and Normal Approximations
H. Brendan McMahan, Francesco Orabona
Uniqueness of Ordinal Embedding
Matthäus Kleindessner, Ulrike Luxburg
Uniqueness of Tensor Decompositions with Applications to Polynomial Identifiability
Aditya Bhaskara, Moses Charikar, Aravindan Vijayaraghavan
Volumetric Spanners: an Efficient Exploration Basis for Learning
Elad Hazan, Zohar Karnin, Raghu Meka
Active and passive learning of linear separators under log-concave distributions
Maria-Florina Balcan, Phil Long
Adaptive Crowdsourcing Algorithms for the Bandit Survey Problem
Ittai Abraham, Omar Alonso, Vasilis Kandylas et al.
Algorithms and Hardness for Robust Subspace Recovery
Moritz Hardt, Ankur Moitra
Approachability, fast and slow
Vianney Perchet, Shie Mannor
A Tale of Two Metrics: Simultaneous Bounds on Competitiveness and Regret
Lachlan Andrew, Siddharth Barman, Katrina Ligett et al.
A Tensor Spectral Approach to Learning Mixed Membership Community Models
Animashree Anandkumar, Rong Ge, Daniel Hsu et al.
A Theoretical Analysis of NDCG Type Ranking Measures
Yining Wang, Liwei Wang, Yuanzhi Li et al.
Beating Bandits in Gradually Evolving Worlds
Chao-Kai Chiang, Chia-Jung Lee, Chi-Jen Lu
Blind Signal Separation in the Presence of Gaussian Noise
Mikhail Belkin, Luis Rademacher, James Voss
Boosting with the Logistic Loss is Consistent
Matus Telgarsky
Bounded regret in stochastic multi-armed bandits
Sébastien Bubeck, Vianney Perchet, Philippe Rigollet
Classification with Asymmetric Label Noise: Consistency and Maximal Denoising
Clayton Scott, Gilles Blanchard, Gregory Handy
Competing With Strategies
Wei Han, Alexander Rakhlin, Karthik Sridharan
Complexity Theoretic Lower Bounds for Sparse Principal Component Detection
Quentin Berthet, Philippe Rigollet
Consistency of Robust Kernel Density Estimators
Robert Vandermeulen, Clayton Scott