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Found 5 papers, 1 papers with code
A Hierarchical Destroy and Repair Approach for Solving Very Large-Scale Travelling Salesman Problem
Fair comparisons based on nineteen famous large-scale instances (with 10, 000 to 10, 000, 000 cities) show that HDR is highly competitive against existing state-of-the-art TSP algorithms, in terms of both efficiency and solution quality.
$m$-mix: Generating hard negatives via multiple samples mixing for contrastive learning
Negative pairs are essential in contrastive learning, which plays the role of avoiding degenerate solutions.
Variance-Dependent Best Arm Identification
Given a set of $n$ arms indexed from $1$ to $n$, each arm $i$ is associated with an unknown reward distribution supported on $[0, 1]$ with mean $\theta_i$ and variance $\sigma_i^2$.
Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching
As such, the agent can finish inlier matching timely when the affinity score stops growing, for which otherwise an additional parameter i. e. the number of inliers is needed to avoid matching outliers.
Combinatorial Multi-Armed Bandit with General Reward Functions
Our framework enables a much larger class of reward functions such as the $\max()$ function and nonlinear utility functions.
