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Found 10 papers, 0 papers with code
Query-Efficient Correlation Clustering with Noisy Oracle
We study a general clustering setting in which we have $n$ elements to be clustered, and we aim to perform as few queries as possible to an oracle that returns a noisy sample of the similarity between two elements.
Best-of-Both-Worlds Algorithms for Linear Contextual Bandits
We study best-of-both-worlds algorithms for $K$-armed linear contextual bandits.
Dynamic Structure Estimation from Bandit Feedback
This work present novel method for structure estimation of an underlying dynamical system.
Collaborative Pure Exploration in Kernel Bandit
In this paper, we formulate a Collaborative Pure Exploration in Kernel Bandit problem (CoPE-KB), which provides a novel model for multi-agent multi-task decision making under limited communication and general reward functions, and is applicable to many online learning tasks, e. g., recommendation systems and network scheduling.
Combinatorial Pure Exploration with Bottleneck Reward Function
For the FC setting, we propose novel algorithms with optimal sample complexity for a broad family of instances and establish a matching lower bound to demonstrate the optimality (within a logarithmic factor).
Combinatorial Pure Exploration with Full-bandit Feedback and Beyond: Solving Combinatorial Optimization under Uncertainty with Limited Observation
Combinatorial optimization is one of the fundamental research fields that has been extensively studied in theoretical computer science and operations research.
Online Dense Subgraph Discovery via Blurred-Graph Feedback
Dense subgraph discovery aims to find a dense component in edge-weighted graphs.
Combinatorial Pure Exploration with Full-Bandit or Partial Linear Feedback
In this paper, we first study the problem of combinatorial pure exploration with full-bandit feedback (CPE-BL), where a learner is given a combinatorial action space $\mathcal{X} \subseteq \{0, 1\}^d$, and in each round the learner pulls an action $x \in \mathcal{X}$ and receives a random reward with expectation $x^{\top} \theta$, with $\theta \in \mathbb{R}^d$ a latent and unknown environment vector.
Graph Mining Meets Crowdsourcing: Extracting Experts for Answer Aggregation
Aggregating responses from crowd workers is a fundamental task in the process of crowdsourcing.
Polynomial-time Algorithms for Multiple-arm Identification with Full-bandit Feedback
Based on our approximation algorithm, we propose novel bandit algorithms for the top-k selection problem, and prove that our algorithms run in polynomial time.
