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A simple approach for non-stationary linear bandits
This paper investigates the problem of non-stationary linear bandits, where the unknown
regression parameter is evolving over time. Previous studies have adopted sophisticated …
regression parameter is evolving over time. Previous studies have adopted sophisticated …
Adaptivity and non-stationarity: Problem-dependent dynamic regret for online convex optimization
We investigate online convex optimization in non-stationary environments and choose
dynamic regret as the performance measure, defined as the difference between cumulative …
dynamic regret as the performance measure, defined as the difference between cumulative …
No-regret learning in time-varying zero-sum games
Learning from repeated play in a fixed two-player zero-sum game is a classic problem in
game theory and online learning. We consider a variant of this problem where the game …
game theory and online learning. We consider a variant of this problem where the game …
Adapting to online label shift with provable guarantees
The standard supervised learning paradigm works effectively when training data shares the
same distribution as the upcoming testing samples. However, this stationary assumption is …
same distribution as the upcoming testing samples. However, this stationary assumption is …
Optimistic online mirror descent for bridging stochastic and adversarial online convex optimization
The stochastically extended adversarial (SEA) model, introduced by Sachs et al.(2022),
serves as an interpolation between stochastic and adversarial online convex optimization …
serves as an interpolation between stochastic and adversarial online convex optimization …
Regret and cumulative constraint violation analysis for online convex optimization with long term constraints
This paper considers online convex optimization with long term constraints, where
constraints can be violated in intermediate rounds, but need to be satisfied in the long run …
constraints can be violated in intermediate rounds, but need to be satisfied in the long run …
Non-stationary online learning with memory and non-stochastic control
We study the problem of Online Convex Optimization (OCO) with memory, which allows loss
functions to depend on past decisions and thus captures temporal effects of learning …
functions to depend on past decisions and thus captures temporal effects of learning …
Improved analysis for dynamic regret of strongly convex and smooth functions
In this paper, we present an improved analysis for dynamic regret of strongly convex and
smooth functions. Specifically, we investigate the Online Multiple Gradient Descent (OMGD) …
smooth functions. Specifically, we investigate the Online Multiple Gradient Descent (OMGD) …
Dynamic regret of online markov decision processes
Abstract We investigate online Markov Decision Processes (MDPs) with adversarially
changing loss functions and known transitions. We choose dynamic regret as the …
changing loss functions and known transitions. We choose dynamic regret as the …
Adapting to continuous covariate shift via online density ratio estimation
Dealing with distribution shifts is one of the central challenges for modern machine learning.
One fundamental situation is the covariate shift, where the input distributions of data change …
One fundamental situation is the covariate shift, where the input distributions of data change …