International audienceIn this paper, we formalise order-robust optimisation as an instance of online learning minimising simple regret, and propose VROOM, a zeroth order optimisation algorithm capable of achieving vanishing regret in non-stationary environments, while recovering favorable rates under stochastic reward-generating processes. Our results are the first to target simple regret definitions in adversarial scenarios unveiling a challenge that has been rarely considered in prior work
We consider the problem of minimizing the long term average expected regret of an agent in an online...
We propose a novel online learning method for minimizing regret in large extensive-form games. The a...
Most methods for decision-theoretic online learning are based on the Hedge algo-rithm, which takes a...
Abstract We consider the problem of online optimization, where a learner chooses a decision from a g...
We study the problem of online learning with a notion of regret defined with respect to a set of str...
This paper develops a methodology for regret minimization with stochastic first-order oracle feedbac...
We consider online convex optimizations in the bandit setting. The decision maker does not know the ...
The framework of online learning with memory naturally captures learning problems with temporal effe...
34 pages, 15 figuresSpurred by the enthusiasm surrounding the "Big Data" paradigm, the mathematical ...
First, we study online learning with an extended notion of regret, which is defined with respect to ...
First, we study online learning with an extended notion of regret, which is defined with respect to ...
The regret bound of dynamic online learning algorithms is often expressed in terms of the variation ...
Markov decision processes (MDPs) have proven to be a useful model for sequential decision- theoretic...
The greedy algorithm is extensively studied in the field of combinatorial optimiza-tion for decades....
International audienceWe examine the problem of regret minimization when the learner is involved in ...
We consider the problem of minimizing the long term average expected regret of an agent in an online...
We propose a novel online learning method for minimizing regret in large extensive-form games. The a...
Most methods for decision-theoretic online learning are based on the Hedge algo-rithm, which takes a...
Abstract We consider the problem of online optimization, where a learner chooses a decision from a g...
We study the problem of online learning with a notion of regret defined with respect to a set of str...
This paper develops a methodology for regret minimization with stochastic first-order oracle feedbac...
We consider online convex optimizations in the bandit setting. The decision maker does not know the ...
The framework of online learning with memory naturally captures learning problems with temporal effe...
34 pages, 15 figuresSpurred by the enthusiasm surrounding the "Big Data" paradigm, the mathematical ...
First, we study online learning with an extended notion of regret, which is defined with respect to ...
First, we study online learning with an extended notion of regret, which is defined with respect to ...
The regret bound of dynamic online learning algorithms is often expressed in terms of the variation ...
Markov decision processes (MDPs) have proven to be a useful model for sequential decision- theoretic...
The greedy algorithm is extensively studied in the field of combinatorial optimiza-tion for decades....
International audienceWe examine the problem of regret minimization when the learner is involved in ...
We consider the problem of minimizing the long term average expected regret of an agent in an online...
We propose a novel online learning method for minimizing regret in large extensive-form games. The a...
Most methods for decision-theoretic online learning are based on the Hedge algo-rithm, which takes a...