Code repository for the paper "On Continuous Monitoring of Risk Violations under Unknown Shift" @ UAI 2025

Statistical Rethinking Course 2026

Built by a PhD whose memory was failing, whose diet was a mess, and whose anxiety had its own agenda. Most second brain tools ignore the fact that your brain doesn't work in isolation: your body an…

Resources on how to use the GPU clusters

A library for uncertainty quantification based on PyTorch

Official implementation of On-Demand Sampling: Learning Optimally from Multiple Distributions (Neurips 2022)

Confidence sequences and uniform boundaries

Repository for the NeurIPS 2023 paper "Beyond Confidence: Reliable Models Should Also Consider Atypicality"

Material for the course Large-Scale Convex Optimisation at LTH, autumn 2020

Conditional calibration of conformal p-values for outlier detection.

Code for reproducing the results from the paper "Nonparametric Two-Sample Testing by Betting"

A package for Safe Anytime Valid Inference

LSUN Dataset Documentation and Demo Code

Let's train vision transformers (ViT) for cifar 10 / cifar 100!

Distributionally Robust Learning in PyTorch. Install with `pip install sqwash`.

Stochastic Automatic Differentiation library for PyTorch.

relplot: Utilities for measuring calibration and plotting reliability diagrams

FinOps and cloud cost optimization tool. Supports AWS, Azure, GCP, Alibaba Cloud and Kubernetes.

Code repository accompanying the research paper "Uncertainty Quantification for Image-based Traffic Prediction across Cities"

Code and experiments for L-empirical risk minimization.

Python package for evaluating model calibration in classification

Code for "Particle algorithms for maximum likelihood training of latent variable models" (Kuntz, Lim, Johansen, AISTATS, 2023).

This repo contains a PyTorch implementation of the paper: "Evidential Deep Learning to Quantify Classification Uncertainty"

Folklore facts on probability distribution learning, testing, and whatever-ing

Schedule and Syllabus for Human-Centered Machine learning.