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Found 7 papers, 1 papers with code
Thompson Sampling for Stochastic Bandits with Noisy Contexts: An Information-Theoretic Regret Analysis
We explore a stochastic contextual linear bandit problem where the agent observes a noisy, corrupted version of the true context through a noise channel with an unknown noise parameter.
Distributed Multi-Task Learning for Stochastic Bandits with Context Distribution and Stage-wise Constraints
We prove the regret and communication bounds on the algorithm.
Minimal Input Structural Modifications for Strongly Structural Controllability
This paper studies the problem of modifying the input matrix of a structured system to make the system strongly structurally controllable.
Federated Learning for Heterogeneous Bandits with Unobserved Contexts
We study the problem of federated stochastic multi-arm contextual bandits with unknown contexts, in which M agents are faced with different bandits and collaborate to learn.
Distributed Stochastic Bandit Learning with Delayed Context Observation
We consider the problem where M agents collaboratively interact with an instance of a stochastic K-armed contextual bandit, where K>>M.
Stochastic Conservative Contextual Linear Bandits
In this paper, we formulate a conservative stochastic contextual bandit formulation for real-time decision making when an adversary chooses a distribution on the set of possible contexts and the learner is subject to certain safety/performance constraints.
Stochastic Dynamic Information Flow Tracking Game using Supervised Learning for Detecting Advanced Persistent Threats
Our game model has imperfect information as the players do not have information about the actions of the opponent.
Computer Science and Game Theory Cryptography and Security
