Conformal Regression in Calorie Prediction for Team Jumbo-Visma

no code implementations • 6 Apr 2023 • Kristian van Kuijk, Mark Dirksen, Christof Seiler

In this paper, we propose a new, more effective approach to predicting energy needs for cycling races.

Conformal Prediction Decision Making +2

Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Game

2 code implementations • NeurIPS 2021 • Alexander G. Reisach, Christof Seiler, Sebastian Weichwald

Here, we show that marginal variance tends to increase along the causal order for generically sampled additive noise models.

Causal Discovery

Uncertainty Quantification in Multivariate Mixed Models for Mass Cytometry Data

1 code implementation • 19 Mar 2019 • Christof Seiler, Lisa M. Kronstad, Laura J. Simpson, Mathieu Le Gars, Elena Vendrame, Catherine A. Blish, Susan Holmes

In this article, our aim is to exhibit the use of statistical analyses on the raw, uncompressed data thus improving replicability, and exposing multivariate patterns and their associated uncertainty profiles.

Applications

Positive Curvature and Hamiltonian Monte Carlo

no code implementations • NeurIPS 2014 • Christof Seiler, Simon Rubinstein-Salzedo, Susan Holmes

The Jacobi metric introduced in mathematical physics can be used to analyze Hamiltonian Monte Carlo (HMC).

Curvature and Concentration of Hamiltonian Monte Carlo in High Dimensions

1 code implementation • 4 Jul 2014 • Susan Holmes, Simon Rubinstein-Salzedo, Christof Seiler

In this article, we analyze Hamiltonian Monte Carlo (HMC) by placing it in the setting of Riemannian geometry using the Jacobi metric, so that each step corresponds to a geodesic on a suitable Riemannian manifold.

Probability Differential Geometry Statistics Theory Statistics Theory