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Found 6 papers, 0 papers with code
Bridging the Gap Between General and Down-Closed Convex Sets in Submodular Maximization
In this work, we suggest novel offline and online algorithms that provably provide such an interpolation based on a natural decomposition of the convex body constraint into two distinct convex bodies: a down-closed convex body and a general convex body.
ORBSLAM3-Enhanced Autonomous Toy Drones: Pioneering Indoor Exploration
In response to this formidable challenge, we introduce a real-time autonomous indoor exploration system tailored for drones equipped with a monocular \emph{RGB} camera.
Submodular Minimax Optimization: Finding Effective Sets
Despite the rich existing literature about minimax optimization in continuous settings, only very partial results of this kind have been obtained for combinatorial settings.
Resolving the Approximability of Offline and Online Non-monotone DR-Submodular Maximization over General Convex Sets
We also present an inapproximability result showing that our online algorithm and Du's (2022) offline algorithm are both optimal in a strong sense.
Pruning Neural Networks via Coresets and Convex Geometry: Towards No Assumptions
Lately, coresets (provable data summarizations) were leveraged for pruning DNNs, adding the advantage of theoretical guarantees on the trade-off between the compression rate and the approximation error.
Using Partial Monotonicity in Submodular Maximization
Over the last two decades, submodular function maximization has been the workhorse of many discrete optimization problems in machine learning applications.
