Leander Kurscheidt
@leanderk.bsky.social
ML PhD student @ Uni Edinburgh
Interesting in (tractable) probabilistic machine learning, computing niche integrals and whatever you want to tell me about your research!
Interesting in (tractable) probabilistic machine learning, computing niche integrals and whatever you want to tell me about your research!
Reposted by Leander Kurscheidt
We're glad to announce the NeSy 2025 Test of Time award for "Probabilistic Inference Modulo Theories"!
🏆Rodrigo de Salvo Braz was here to accept the award.
This is groundwork for recent NeSy approaches like DeepSeaProbLog and the probabilistic algebraic layer.
🏆Rodrigo de Salvo Braz was here to accept the award.
This is groundwork for recent NeSy approaches like DeepSeaProbLog and the probabilistic algebraic layer.
September 9, 2025 at 9:11 PM
We're glad to announce the NeSy 2025 Test of Time award for "Probabilistic Inference Modulo Theories"!
🏆Rodrigo de Salvo Braz was here to accept the award.
This is groundwork for recent NeSy approaches like DeepSeaProbLog and the probabilistic algebraic layer.
🏆Rodrigo de Salvo Braz was here to accept the award.
This is groundwork for recent NeSy approaches like DeepSeaProbLog and the probabilistic algebraic layer.
Reposted by Leander Kurscheidt
🚨 New paper: “Towards Adaptive Self-Normalized IS”, @ IEEE Statistical Signal Processing Workshop.
TLDR;
To estimate µ = E_p[f(θ)] with SNIS, instead of doing MCMC on p(θ) or learning a parametric q(θ), we try MCMC directly on p(θ)| f(θ)-µ | (variance-minimizing proposal).
arxiv.org/abs/2505.00372
TLDR;
To estimate µ = E_p[f(θ)] with SNIS, instead of doing MCMC on p(θ) or learning a parametric q(θ), we try MCMC directly on p(θ)| f(θ)-µ | (variance-minimizing proposal).
arxiv.org/abs/2505.00372
Towards Adaptive Self-Normalized Importance Samplers
The self-normalized importance sampling (SNIS) estimator is a Monte Carlo estimator widely used to approximate expectations in statistical signal processing and machine learning.
The efficiency of S...
arxiv.org
May 2, 2025 at 1:29 PM
🚨 New paper: “Towards Adaptive Self-Normalized IS”, @ IEEE Statistical Signal Processing Workshop.
TLDR;
To estimate µ = E_p[f(θ)] with SNIS, instead of doing MCMC on p(θ) or learning a parametric q(θ), we try MCMC directly on p(θ)| f(θ)-µ | (variance-minimizing proposal).
arxiv.org/abs/2505.00372
TLDR;
To estimate µ = E_p[f(θ)] with SNIS, instead of doing MCMC on p(θ) or learning a parametric q(θ), we try MCMC directly on p(θ)| f(θ)-µ | (variance-minimizing proposal).
arxiv.org/abs/2505.00372
Reposted by Leander Kurscheidt
Interested in estimating posterior predictives in Bayesian inference? Really want to know if your approximate inference "is working"?
Come to our poster at the NeurIPS BDU workshop on Saturday - see TL;DR below.
Come to our poster at the NeurIPS BDU workshop on Saturday - see TL;DR below.
December 11, 2024 at 5:25 PM
Interested in estimating posterior predictives in Bayesian inference? Really want to know if your approximate inference "is working"?
Come to our poster at the NeurIPS BDU workshop on Saturday - see TL;DR below.
Come to our poster at the NeurIPS BDU workshop on Saturday - see TL;DR below.