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Found 12 papers, 3 papers with code
Online Combinatorial Linear Optimization via a Frank-Wolfe-based Metarounding Algorithm
Metarounding is an approach to convert an approximation algorithm for linear optimization over some combinatorial classes to an online linear optimization algorithm for the same class.
Pure exploration in multi-armed bandits with low rank structure using oblivious sampler
Then, we show the lower bound to the pure exploration in multi-armed bandits with low rank sequence.
Boosting-based Construction of BDDs for Linear Threshold Functions and Its Application to Verification of Neural Networks
Some previous work proposes to transform neural networks into equivalent Boolean expressions and apply verification techniques for characteristics of interest.
Boosting as Frank-Wolfe
LPBoost rapidly converges to an $\epsilon$-approximate solution in practice, but it is known to take $\Omega(m)$ iterations in the worst case, where $m$ is the sample size.
A generalised log-determinant regularizer for online semi-definite programming and its applications
We consider a variant of online semi-definite programming problem (OSDP): The decision space consists of semi-definite matrices with bounded $\Gamma$-trace norm, which is a generalization of trace norm defined by a positive definite matrix $\Gamma.$ To solve this problem, we utilise the follow-the-regularized-leader algorithm with a $\Gamma$-dependent log-determinant regularizer.
Improved algorithms for online load balancing
The cost is the makespan if the norm is $L_\infty$-norm.
Theory and Algorithms for Shapelet-based Multiple-Instance Learning
We propose a new formulation of Multiple-Instance Learning (MIL), in which a unit of data consists of a set of instances called a bag.
Simplified and Unified Analysis of Various Learning Problems by Reduction to Multiple-Instance Learning
In this work, we focus on a particular problem formulation called Multiple-Instance Learning (MIL), and show that various learning problems including all the problems mentioned above with some of new problems can be reduced to MIL with theoretically guaranteed generalization bounds, where the reductions are established under a new reduction scheme we provide as a by-product.
Multiple-Instance Learning by Boosting Infinitely Many Shapelet-based Classifiers
Classifiers based on a single shapelet are not sufficiently strong for certain applications.
Online linear optimization with the log-determinant regularizer
Moreover, we apply our method to online linear optimization over vectors and show that the FTRL with the Burg entropy regularizer, which is the analogue of the log-determinant regularizer in the vector case, works well.
Boosting the kernelized shapelets: Theory and algorithms for local features
We consider binary classification problems using local features of objects.
Bandit Online Optimization Over the Permutahedron
Unfortunately, CombBand requires at each step an $n$-by-$n$ matrix permanent approximation to within improved accuracy as $T$ grows, resulting in a total running time that is super linear in $T$, making it impractical for large time horizons.
