nicomanzonelli

nicomanzonelli's Stars

• eriklindernoren/PyTorch-GAN

  PyTorch implementations of Generative Adversarial Networks.

  Language:Python16.5k2191574.1k

• cxli233/FriendsDontLetFriends

  Friends don't let friends make certain types of data visualization - What are they and why are they bad.

  Language:R6.5k1147248

• davidteather/TikTok-Api

  The Unofficial TikTok API Wrapper In Python

  Language:Python4.9k82780984

• taspinar/twitterscraper

  Scrape Twitter for Tweets

  Language:Python2.4k87285577

• dselivanov/text2vec

  Fast vectorization, topic modeling, distances and GloVe word embeddings in R.

  Language:R85254307136

• ropensci/rtweet

  🐦 R client for interacting with Twitter's [stream and REST] APIs

  Language:R78638604201

• suchow/Dissertate

  Beautiful LaTeX dissertation templates.

  Language:TeX7213064163

• rwalk/gsdmm

  GSDMM: Short text clustering

  Language:Python35351395

• HongshengHu/membership-inference-machine-learning-literature

  29412034

• zeeraktalat/hatespeech

  227101684

• ethz-spylab/rlhf_trojan_competition

  Finding trojans in aligned LLMs. Official repository for the competition hosted at SaTML 2024.

  Language:Python109479

• Xarrow/weibo-scraper

  Simple Weibo Scraper

  Language:Python963918

• ENCASEH2020/hatespeech-twitter

  53459

• ahoho/topics

  Language:Jupyter Notebook52547

• jackyin12/GSDMM

  Language:Java21105

• jrmazarura/GPM

  Language:Python13115

• heyyjudes/differentially-private-set-union

  Language:Python8301

• jacquessham/tokenize_chinese_nlp

  This is a project to testing whether the package jieba is a good package to tokenize Chinese phrases.

  Language:Python8101

• jwilber/4PLY

  www.4PLYMAG.com

  Language:Jupyter Notebook8300

• jwilber/4ply_data

  data driven stuff for 4ply

  Language:HTML6201

• chriswi93/LDA

  Collapsed Gibbs Sampling for LDA described in Griffiths and Steyvers (2004): Finding scientific topics.

  Language:Python5

• umd-huang-lab/private-topic-model-tensor-methods

  We provide an end-to-end differentially pri- vate spectral algorithm for learning LDA, based on matrix/tensor decompositions, and establish theoretical guarantees on util- ity/consistency of the estimated model pa- rameters. The spectral algorithm consists of multiple algorithmic steps, named as “edges”, to which noise could be injected to obtain differential privacy. We identify subsets of edges, named as “configurations”, such that adding noise to all edges in such a subset guarantees differential privacy of the end-to-end spectral algorithm. We character- ize the sensitivity of the edges with respect to the input and thus estimate the amount of noise to be added to each edge for any required privacy level. We then character- ize the utility loss for each configuration as a function of injected noise. Overall, by com- bining the sensitivity and utility characteri- zation, we obtain an end-to-end differentially private spectral algorithm for LDA and iden- tify the corresponding configuration that out- performs others in any specific regime. We are the first to achieve utility guarantees un- der the required level of differential privacy for learning in LDA. Overall our method sys- tematically outperforms differentially private variational inference.

  Language:Python5200

• McGill-DMaS/Authorship-Anonymization

  Authorship Anonymization through Style Transfer

  Language:Python4252

• ricardocarvalhods/diff-private-set-union

  Repository for AAAI 2022 paper "Incorporating Item Frequency for Differentially Private Set Union"

  Language:Jupyter Notebook1100