Search Results for author:
Found 13 papers, 0 papers with code
Online Heavy-tailed Change-point detection
We derive guarantees on worst-case, finite-sample false-positive rate (FPR) over the family of all distributions with bounded second moment.
Predict-and-Critic: Accelerated End-to-End Predictive Control for Cloud Computing through Reinforcement Learning
PnC jointly trains a prediction model and a terminal Q function that approximates cost-to-go over a long horizon, by back-propagating the cost of decisions through the optimization problem \emph{and from the future}.
Decentralized Competing Bandits in Non-Stationary Matching Markets
We propose and analyze a decentralized and asynchronous learning algorithm, namely Decentralized Non-stationary Competing Bandits (\texttt{DNCB}), where the agents play (restrictive) successive elimination type learning algorithms to learn their preference over the arms.
Breaking the $\sqrt{T}$ Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits
The (poly) logarithmic regret of \texttt{LR-SCB} stems from two crucial facts: (a) the application of a norm adaptive algorithm to exploit the parameter estimation and (b) an analysis of the shifted linear contextual bandit algorithm, showing that shifting results in increasing regret.
Model Selection for Generic Contextual Bandits
We consider the problem of model selection for the general stochastic contextual bandits under the realizability assumption.
Adaptive Clustering and Personalization in Multi-Agent Stochastic Linear Bandits
We show that, for any agent, the regret scales as $\mathcal{O}(\sqrt{T/N})$, if the agent is in a `well separated' cluster, or scales as $\mathcal{O}(T^{\frac{1}{2} + \varepsilon}/(N)^{\frac{1}{2} -\varepsilon})$ if its cluster is not well separated, where $\varepsilon$ is positive and arbitrarily close to $0$.
Beyond $\log^2(T)$ Regret for Decentralized Bandits in Matching Markets
We design decentralized algorithms for regret minimization in the two-sided matching market with one-sided bandit feedback that significantly improves upon the prior works (Liu et al. 2020a, 2020b, Sankararaman et al. 2020).
Multi-Agent Low-Dimensional Linear Bandits
We study a multi-agent stochastic linear bandit with side information, parameterized by an unknown vector $\theta^* \in \mathbb{R}^d$.
Dominate or Delete: Decentralized Competing Bandits in Serial Dictatorship
Online learning in a two-sided matching market, with demand side agents continuously competing to be matched with supply side (arms), abstracts the complex interactions under partial information on matching platforms (e. g. UpWork, TaskRabbit).
Problem-Complexity Adaptive Model Selection for Stochastic Linear Bandits
This is the first algorithm that achieves such model selection guarantees.
The Gossiping Insert-Eliminate Algorithm for Multi-Agent Bandits
Agents use the communication medium to recommend only arm-IDs (not samples), and thus update the set of arms from which they play.
ComHapDet: A Spatial Community Detection Algorithm for Haplotype Assembly
Results: We propose a novel graphical representation of sequencing reads and pose the haplotype assembly problem as an instance of community detection on a spatial random graph.
Social Learning in Multi Agent Multi Armed Bandits
Our setting consists of a large number of agents $n$ that collaboratively and simultaneously solve the same instance of $K$ armed MAB to minimize the average cumulative regret over all agents.
