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002_machinelearning_ebook

2015

Public material for CS109

a-variational-neural-architecture-for-skill-based-team-formation

A Variational Neural Architecture for Skill-based Team Formation

acl2022-reparametrization-trick-broke-differential-privacy

ACL 2022: How reparametrization trick broke differentially-private text representation leaning

adamel-supplementary

This repo is for the supplementary materials of AdaMEL, "Deep transfer learning for multi-source entity linkage via domain adaptation". Proc. VLDB Endow. 15, 3 (November 2021), 465–477.

aima-python

Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"

aix360

Interpretability and explainability of data and machine learning models

alacarte

alex

A library for building an in-memory, Adaptive Learned indEX

alg

paper for POD

alinet

Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation, AAAI 2020

amortizedcausaldiscovery

Code for the paper: Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data

analytic-gaussian-mechanism

Analytic calibration for differential privacy with Gaussian perturbations

applied-deep-learning

Applied Deep Learning Course

associationabacminer

Uses association/frequent itemset mining to generate and score ABAC policies from AWS CloudTrail logs.

auditing-text-generation

Code for Auditing Data Provenance in Text-Generation Models (in KDD 2019)

authorship-anonymization

Authorship Anonymization through Style Transfer

awesome-causal-inference

A curated list of awesome work on causal inference, particularly in machine learning.

awesome-papers

A collection of papers and resources about Data-centric Graph Machine Learning (DC-GML).

awesome-vaes

A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.

benchmarking-gnns

Repository for benchmarking graph neural networks

bnn_chaos_model

Model and training code for Bayesian neural network for compact planetary instability

books-ml-and-dl

.pdf Format Books for Machine and Deep Learning

brewer

Reference paper: "Entity Resolution On-Demand" (Giovanni Simonini, Luca Zecchini, Sonia Bergamaschi, Felix Naumann). Proceedings of the VLDB Endowment (PVLDB), vol. 15, n. 7, pp. 1506-1518 (2022)

brewer_demo

Reference paper: Luca Zecchini, Giovanni Simonini, Sonia Bergamaschi, Felix Naumann: "BrewER: Entity Resolution On-Demand". Demo paper submitted to Proceedings of the VLDB Endowment (PVLDB), vol. 16 (2023)

butterfly-core

multi-labeled dataset

cardinalityestimationtestbed

CardinalityEstimationTestbed

cat-cgl

causal-inference-and-discovery-in-python

Causal Inference and Discovery in Python by Packt Publishing

causal_learning_unknown_interventions

Code for "Neural causal learning from unknown interventions"