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Found 15 papers, 0 papers with code
High-Probability Risk Bounds via Sequential Predictors
Online learning methods yield sequential regret bounds under minimal assumptions and provide in-expectation risk bounds for statistical learning.
Trading-Off Payments and Accuracy in Online Classification with Paid Stochastic Experts
Here, before making their prediction, each expert must be paid.
Delayed Bandits: When Do Intermediate Observations Help?
However, if the mapping of states to losses is stochastic, we show that the regret grows at a rate of $\sqrt{\big(K+\min\{|\mathcal{S}|, d\}\big)T}$ (within log factors), implying that if the number $|\mathcal{S}|$ of states is smaller than the delay, then intermediate observations help.
A Unified Analysis of Nonstochastic Delayed Feedback for Combinatorial Semi-Bandits, Linear Bandits, and MDPs
We derive a new analysis of Follow The Regularized Leader (FTRL) for online learning with delayed bandit feedback.
Learning on the Edge: Online Learning with Stochastic Feedback Graphs
The framework of feedback graphs is a generalization of sequential decision-making with bandit or full information feedback.
A Regret-Variance Trade-Off in Online Learning
We prove that a variant of EWA either achieves a negative regret (i. e., the algorithm outperforms the best expert), or guarantees a $O(\log K)$ bound on both variance and regret.
A Near-Optimal Best-of-Both-Worlds Algorithm for Online Learning with Feedback Graphs
The algorithm combines ideas from the EXP3++ algorithm for stochastic and adversarial bandits and the EXP3. G algorithm for feedback graphs with a novel exploration scheme.
Nonstochastic Bandits and Experts with Arm-Dependent Delays
We study nonstochastic bandits and experts in a delayed setting where delays depend on both time and arms.
Beyond Bandit Feedback in Online Multiclass Classification
We study the problem of online multiclass classification in a setting where the learner's feedback is determined by an arbitrary directed graph.
Distributed Online Learning for Joint Regret with Communication Constraints
Each round, an adversary first activates one of the agents to issue a prediction and provides a corresponding gradient, and then the agents are allowed to send a $b$-bit message to their neighbors in the graph.
MetaGrad: Adaptation using Multiple Learning Rates in Online Learning
We provide a new adaptive method for online convex optimization, MetaGrad, that is robust to general convex losses but achieves faster rates for a broad class of special functions, including exp-concave and strongly convex functions, but also various types of stochastic and non-stochastic functions without any curvature.
Exploiting the Surrogate Gap in Online Multiclass Classification
In the bandit classification setting we show that Gaptron is the first linear time algorithm with $O(K\sqrt{T})$ expected regret, where $K$ is the number of classes.
Comparator-adaptive Convex Bandits
We study bandit convex optimization methods that adapt to the norm of the comparator, a topic that has only been studied before for its full-information counterpart.
User-Specified Local Differential Privacy in Unconstrained Adaptive Online Learning
In this paper we generalize this approach by allowing the provider of the data to choose the distribution of the noise without disclosing any parameters of the distribution to the learner, under the constraint that the distribution is symmetrical.
The Many Faces of Exponential Weights in Online Learning
A standard introduction to online learning might place Online Gradient Descent at its center and then proceed to develop generalizations and extensions like Online Mirror Descent and second-order methods.
