A research library for pytorch-based neural network pruning, compression, and more.

Coresets via Bilevel Optimization

A library for coreset algorithms, written in JAX for fast execution and GPU support.

Bayesian Coresets Construction with Accelerated Iterative Hard Thresholding (A-IHT).

DataCull is a modular, light-weight data pruning library containing many dataset pruning (coreset selection) algorithm including the official Implementation of the paper, titled, RCAP: Robust, Class-Aware, Probab ilistic Dynamic Dataset Pruning

Geometrical Homogeneous Clustering for Image Data Reduction. An algorithm to reduce large image datasets maintaining similar accuracy.

BICO is a fast streaming algorithm to compute coresets for the k-means problem on very large sets of points.

This is the accompanying code repository for the AISTATS 2022 publication p-Generalized Probit Regression and Scalable Maximum Likelihood Estimation via Sketching and Coresets by Alexander Munteanu, Simon Omlor and Christian Peters.

[ECCV 2022] The official repository of ''$\ell_\infty$-Robustness and Beyond: Unleashing Efficient Adversarial Training''.

Official implementation of "Data-Efficient Training of CNNs and Transformers with Coresets: A Stability Perspective"

An interactive tool for generating compact, representative coresets from images, enabling faster, memory-efficient processing for tasks like segmentation and clustering with minimal accuracy loss.

Efficient image segmentation using KMeans clustering with coreset sampling to speed up processing while preserving segmentation quality.

Speeding up clustering algorithms using Sampling techniques (Lightweight Coresets)

MECO: Multi-objective Evolutionary Compression

Surgical Machine Unlearning for LLMs, VLMs, and Diffusion models. Erasus uses coreset selection to enable efficient data removal with 27+ strategies and 19 selectors. Supports certified removal, multimodal decoupling, and comprehensive evaluation with 90% less compute than retraining.

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