We propose a computationally efficient random walk on a convex body which rapidly mixes to a time-varying Gibbs distribution. In the setting of online convex optimization and repeated games, the algorithm yields low regret and presents a novel efficient method for implementing mixture forecasting strategies
We provide several applications of Optimistic Mirror Descent, an online learning algorithm based on ...
We address online linear optimization problems when the possible actions of the decision maker are r...
The minmax regret problem for combinatorial optimization under uncertainty can be viewed as a zero-s...
We propose a computationally efficient random walk on a convex body which rapidly mixes to a time-va...
We consider online convex optimizations in the bandit setting. The decision maker does not know the ...
In an online convex optimization problem a decision-maker makes a sequence of decisions, i.e., choos...
Regret minimization is a powerful tool for solving large-scale extensive-form games. State-of-the-ar...
First, we study online learning with an extended notion of regret, which is defined with respect to ...
A natural algorithmic scheme in online game playing is called ‘follow-the-leader’, first proposed by...
First, we study online learning with an extended notion of regret, which is defined with respect to ...
No-regret algorithms for online convex optimization are potent online learning tools and have been d...
This paper develops a methodology for regret minimization with stochastic first-order oracle feedbac...
We study the regret of optimal strategies for online convex optimization games. Using von Neumann's ...
The framework of online learning with memory naturally captures learning problems with temporal effe...
We consider the online convex optimization problem. In the setting of arbitrary sequences and finite...
We provide several applications of Optimistic Mirror Descent, an online learning algorithm based on ...
We address online linear optimization problems when the possible actions of the decision maker are r...
The minmax regret problem for combinatorial optimization under uncertainty can be viewed as a zero-s...
We propose a computationally efficient random walk on a convex body which rapidly mixes to a time-va...
We consider online convex optimizations in the bandit setting. The decision maker does not know the ...
In an online convex optimization problem a decision-maker makes a sequence of decisions, i.e., choos...
Regret minimization is a powerful tool for solving large-scale extensive-form games. State-of-the-ar...
First, we study online learning with an extended notion of regret, which is defined with respect to ...
A natural algorithmic scheme in online game playing is called ‘follow-the-leader’, first proposed by...
First, we study online learning with an extended notion of regret, which is defined with respect to ...
No-regret algorithms for online convex optimization are potent online learning tools and have been d...
This paper develops a methodology for regret minimization with stochastic first-order oracle feedbac...
We study the regret of optimal strategies for online convex optimization games. Using von Neumann's ...
The framework of online learning with memory naturally captures learning problems with temporal effe...
We consider the online convex optimization problem. In the setting of arbitrary sequences and finite...
We provide several applications of Optimistic Mirror Descent, an online learning algorithm based on ...
We address online linear optimization problems when the possible actions of the decision maker are r...
The minmax regret problem for combinatorial optimization under uncertainty can be viewed as a zero-s...
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