Add to ORKGWe 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
First, we study online learning with an extended notion of regret, which is defined with respect to ...
A number of learning problems can be cast as an Online Convex Game: on each round, a learner makes a...
The regret bound of dynamic online learning algorithms is often expressed in terms of the variation ...
We propose a computationally efficient random walk on a convex body which rapidly mixes to a time-va...
In an online convex optimization problem a decision-maker makes a sequence of decisions, i.e., choos...
We consider online convex optimizations in the bandit setting. The decision maker does not know the ...
Thesis (Ph.D.)--University of Washington, 2018We consider a few aspects of the interplay between con...
We provide several applications of Optimistic Mirror Descent, an online learning algorithm based on ...
We study the rates of growth of the regret in online convex optimization. First, we show that a simp...
We develop an algorithmic framework for solving convex optimization problems using no-regret game dy...
Several Markov chain sampling algorithms, including the Hit-and-Run algorithm, are unified within th...
Motivated by applications in machine learning and operations research, we study regret minimization ...
This paper develops a methodology for regret minimization with stochastic first-order oracle feedbac...
A natural algorithmic scheme in online game playing is called ‘follow-the-leader’, first proposed by...
Tracking time-varying sparse signals is a recent problem with widespread applications. Techniques de...
First, we study online learning with an extended notion of regret, which is defined with respect to ...
A number of learning problems can be cast as an Online Convex Game: on each round, a learner makes a...
The regret bound of dynamic online learning algorithms is often expressed in terms of the variation ...
We propose a computationally efficient random walk on a convex body which rapidly mixes to a time-va...
In an online convex optimization problem a decision-maker makes a sequence of decisions, i.e., choos...
We consider online convex optimizations in the bandit setting. The decision maker does not know the ...
Thesis (Ph.D.)--University of Washington, 2018We consider a few aspects of the interplay between con...
We provide several applications of Optimistic Mirror Descent, an online learning algorithm based on ...
We study the rates of growth of the regret in online convex optimization. First, we show that a simp...
We develop an algorithmic framework for solving convex optimization problems using no-regret game dy...
Several Markov chain sampling algorithms, including the Hit-and-Run algorithm, are unified within th...
Motivated by applications in machine learning and operations research, we study regret minimization ...
This paper develops a methodology for regret minimization with stochastic first-order oracle feedbac...
A natural algorithmic scheme in online game playing is called ‘follow-the-leader’, first proposed by...
Tracking time-varying sparse signals is a recent problem with widespread applications. Techniques de...
First, we study online learning with an extended notion of regret, which is defined with respect to ...
A number of learning problems can be cast as an Online Convex Game: on each round, a learner makes a...
The regret bound of dynamic online learning algorithms is often expressed in terms of the variation ...