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05/06/2023
An improved regret analysis for UCB-N and TS-N
In the setting of stochastic online learning with undirected feedback gr...
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01/11/2023
Adversarial Online Multi-Task Reinforcement Learning
We consider the adversarial online multi-task reinforcement learning set...
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06/23/2021
Best-Case Lower Bounds in Online Learning
Much of the work in online learning focuses on the study of sublinear up...
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02/16/2021
Optimal Algorithms for Private Online Learning in a Stochastic Environment
We consider two variants of private stochastic online learning. The firs...
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03/06/2020
A Farewell to Arms: Sequential Reward Maximization on a Budget with a Giving Up Option
We consider a sequential decision-making problem where an agent can take...
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10/29/2019
Dying Experts: Efficient Algorithms with Optimal Regret Bounds
We study a variant of decision-theoretic online learning in which the se...
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02/27/2018
Multi-Observation Regression
Recent work introduced loss functions which measure the error of a predi...
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10/21/2017
A Tight Excess Risk Bound via a Unified PAC-Bayesian-Rademacher-Shtarkov-MDL Complexity
We present a novel notion of complexity that interpolates between and ge...
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09/12/2016
CompAdaGrad: A Compressed, Complementary, Computationally-Efficient Adaptive Gradient Method
The adaptive gradient online learning method known as AdaGrad has seen w...
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05/01/2016
Fast Rates for General Unbounded Loss Functions: from ERM to Generalized Bayes
We present new excess risk bounds for general unbounded loss functions i...
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07/09/2015
Fast rates in statistical and online learning
The speed with which a learning algorithm converges as it is presented w...
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10/23/2012
MLPACK: A Scalable C++ Machine Learning Library
MLPACK is a state-of-the-art, scalable, multi-platform C++ machine learn...
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09/13/2012
Minimax Multi-Task Learning and a Generalized Loss-Compositional Paradigm for MTL
Since its inception, the modus operandi of multi-task learning (MTL) has...
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02/18/2012
