A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New algorithms reduce regret for convex bandits with small comparator norms.
New approach reduces unconstrained linear bandits to simpler optimization problems.
New algorithm reduces dynamic regret in time-varying movement costs.
New method for distributed online learning with communication constraints reduces joint regret.
The paper improves on existing algorithms for minimizing different types of regret in online learning.
Large-scale graph data in real-world applications is often not static but dynamic, i. e., new nodes and edges appear over time. Current graph convolution approaches are promising, especially, when all the graph's nodes and edges are available during training. When unseen nodes and edges are inserted after training, it …
Study compares adaptive vs fixed query learning methods.
Fisher et al. extend multi-VAR for better modeling of heterogeneous time series.
The computational effort for the evaluation of numerical simulations based on e.g. the finite-element method is high. Metamodels can be utilized to create a low-cost alternative. However the number of required samples for the creation of a sufficient metamodel should be kept low, which can be achieved by using adaptive…
New algorithm reduces dynamic regret by adapting to comparator complexity.