Search Results for author:
Found 9 papers, 2 papers with code
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
Empirical Policy Evaluation with Supergraphs
We devise and analyze algorithms for the empirical policy evaluation problem in reinforcement learning.
Common Information based Approximate State Representations in Multi-Agent Reinforcement Learning
Due to information asymmetry, finding optimal policies for Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) is hard with the complexity growing doubly exponentially in the horizon length.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure
For the bandit setting, we propose a hierarchical bandit algorithm that achieves a near-optimal gap-independent regret of $\widetilde{\mathcal{O}}(\sqrt{ABT})$ and a near-optimal gap-dependent regret of $\mathcal{O}(\log(T))$, where $A$ and $B$ are the numbers of actions of the leader and the follower, respectively, and $T$ is the number of steps.
Learning a Discrete Set of Optimal Allocation Rules in a Queueing System with Unknown Service Rate
Our goal is to design a dispatching policy that maximizes the long-term average reward for the dispatcher based on observing only the arrival times and the state of the system at each arrival that reflects a realistic sampling of such systems.
OpenGridGym: An Open-Source AI-Friendly Toolkit for Distribution Market Simulation
This paper presents OpenGridGym, an open-source Python-based package that allows for seamless integration of distribution market simulation with state-of-the-art artificial intelligence (AI) decision-making algorithms.
Rarest-First with Probabilistic-Mode-Suppression
In this work, we propose a tunable piece-selection policy that minimizes this (undesirable) requisite by combining the (work-conserving but not stabilizing) rarest-first protocol with only an appropriate share of the (non-work conserving and stabilizing) mode-suppression protocol.
Bayesian Learning of Optimal Policies in Markov Decision Processes with Countably Infinite State-Space
Models of many real-life applications, such as queuing models of communication networks or computing systems, have a countably infinite state-space.
Backward and Forward Inference in Interacting Independent-Cascade Processes: A Scalable and Convergent Message-Passing Approach
We study the problems of estimating the past and future evolutions of two diffusion processes that spread concurrently on a network.
