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.
Method estimates dynamic treatment effects using machine learning and g-estimation.
A method combines deep learning and G-estimation for causal mediation analysis.
DeepBlip estimates treatment effects over time using neural networks.
This work constructs a hypothesis test for detecting whether an data-generating function belongs to a specific reproducing kernel Hilbert space , where the structure of is only partially known. Utilizing the theory of reproducing kernels, we reduce this hypothesis …
Discovering cause-effect relationships between variables from observational data is a fundamental challenge in many scientific disciplines. However, in many situations it is desirable to directly estimate the change in causal relationships across two different conditions, e.g., estimating the change in genetic expressi…
Online DEM improves tracking of latent states in dynamic systems.
Learning exists in the context of data, yet notions of confidence typically focus on model predictions, not label quality. Confident learning (CL) is an alternative approach which focuses instead on label quality by characterizing and identifying label errors in datasets, based on the principles of pruning noisy data, …
The likelihood function of a finite mixture model is a non-convex function with multiple local maxima and commonly used iterative algorithms such as EM will converge to different solutions depending on initial conditions. In this paper we ask: is it possible to assess how far we are from the global maximum of the likel…
Review of methods enabling causal predictions under hypothetical interventions.
Randomness is crucial for stability in learning and statistics, especially for differential privacy.