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Found 9 papers, 1 papers with code
Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits
The study of collaborative multi-agent bandits has attracted significant attention recently.
Minimax Regret for Cascading Bandits
Cascading bandits is a natural and popular model that frames the task of learning to rank from Bernoulli click feedback in a bandit setting.
Robust Multi-Agent Bandits Over Undirected Graphs
Thus, we generalize existing regret bounds beyond the complete graph (where $d_{\text{mal}}(i) = m$), and show the effect of malicious agents is entirely local (in the sense that only the $d_{\text{mal}}(i)$ malicious agents directly connected to $i$ affect its long-term regret).
Improved Algorithms for Misspecified Linear Markov Decision Processes
(P1) Its regret after $K$ episodes scales as $K \max \{ \varepsilon_{\text{mis}}, \varepsilon_{\text{tol}} \}$, where $\varepsilon_{\text{mis}}$ is the degree of misspecification and $\varepsilon_{\text{tol}}$ is a user-specified error tolerance.
Regret Bounds for Stochastic Shortest Path Problems with Linear Function Approximation
We propose an algorithm that uses linear function approximation (LFA) for stochastic shortest path (SSP).
One-bit feedback is sufficient for upper confidence bound policies
We consider a variant of the traditional multi-armed bandit problem in which each arm is only able to provide one-bit feedback during each pull based on its past history of rewards.
Robust Multi-Agent Multi-Armed Bandits
Recent works have shown that agents facing independent instances of a stochastic $K$-armed bandit can collaborate to decrease regret.
Empirical Policy Evaluation with Supergraphs
We devise and analyze algorithms for the empirical policy evaluation problem in reinforcement learning.
On the role of clustering in Personalized PageRank estimation
We then show that the common underlying graph can be leveraged to efficiently and jointly estimate PPR for many pairs, rather than treating each pair separately using the primitive algorithm.
Social and Information Networks
