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Found 12 papers, 0 papers with code
Equilibrium Bandits: Learning Optimal Equilibria of Unknown Dynamics
We prove that UECB achieves a regret of $\mathcal{O}(\log(T)+\tau_c\log(\tau_c)+\tau_c\log\log(T))$ for this equilibrium bandit problem where $\tau_c$ is the worst case approximate convergence time to equilibrium.
Learning in quantum games
In this paper, we introduce a class of learning dynamics for general quantum games, that we call "follow the quantum regularized leader" (FTQL), in reference to the classical "follow the regularized leader" (FTRL) template for learning in finite games.
Distributed stochastic optimization with large delays
One of the most widely used methods for solving large-scale stochastic optimization problems is distributed asynchronous stochastic gradient descent (DASGD), a family of algorithms that result from parallelizing stochastic gradient descent on distributed computing architectures (possibly) asychronously.
No Weighted-Regret Learning in Adversarial Bandits with Delays
Using these bounds, we show that FKM and EXP3 have no weighted-regret even for $d_{t}=O\left(t\log t\right)$.
Cooperative Multi-player Bandit Optimization
At each turn, each player chooses an action and receives a reward that is an unknown function of all the players' actions.
Distributed Distillation for On-Device Learning
We prove that our algorithm converges with probability 1 to a stationary point where all devices in the communication network distill the entire network's knowledge on the reference data, regardless of their local connections.
Online EXP3 Learning in Adversarial Bandits with Delayed Feedback
An adversary chooses the cost of each arm in a bounded interval, and a sequence of feedback delays \left\{ d_{t}\right\} that are unknown to the player.
Learning in Games with Lossy Feedback
We consider a game-theoretical multi-agent learning problem where the feedback information can be lost during the learning process and rewards are given by a broad class of games known as variationally stable games.
Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go?
One of the most widely used optimization methods for large-scale machine learning problems is distributed asynchronous stochastic gradient descent (DASGD).
Countering Feedback Delays in Multi-Agent Learning
We consider a model of game-theoretic learning based on online mirror descent (OMD) with asynchronous and delayed feedback information.
Stochastic Mirror Descent in Variationally Coherent Optimization Problems
In this paper, we examine a class of non-convex stochastic optimization problems which we call variationally coherent, and which properly includes pseudo-/quasiconvex and star-convex optimization problems.
On the convergence of mirror descent beyond stochastic convex programming
In this paper, we examine the convergence of mirror descent in a class of stochastic optimization problems that are not necessarily convex (or even quasi-convex), and which we call variationally coherent.
