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EvalML: a library for automated machine learning and model understanding

EvalML is an AutoML library that builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions, it is a library for automated machine learning (AutoML) and model understanding, written in Python

16 de May de 20212 de April de 2021

Machine Learning From Scratch

An extensive list of fundamental machine learning models and algorithms from scratch in vanilla Python.

30 de May de 2020

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• Practical Differentially Private Clustering
  21 de October de 2021
  Posted by Alisa Chang, Software Engineer, Google Cloud and Pritish […]
• Predicting Spreadsheet Formulas from Semi-structured Contexts
  20 de October de 2021
  Posted by Rishabh Singh, Research Scientist and Max Lin, Software […]
• How Underspecification Presents Challenges for Machine Learning
  18 de October de 2021
  Posted by Alex D’Amour and Katherine Heller, Research Scientists, Google […]

• DIGITAL SME’s Brussels office welcomes two new staff members
  18 de October de 2021
  Two new professionals will reinforce our team. Welcome Alessandra and Sophie! While big tech companies spend record amounts on lobbying […] The post DIGITAL SME’s Brussels office welcomes two new staff members appeared first on […]

• Decentralised Person Re-Identification with Selective Knowledge Aggregation. (arXiv:2110.11384v1 [cs.CV])
  Existing person re-identification (Re-ID) methods mostly follow a centralised learning paradigm which shares all training data to a collection for […]
• The MuSe 2021 Multimodal Sentiment Analysis Challenge: Sentiment, Emotion, Physiological-Emotion, and Stress. (arXiv:2104.07123v2 [cs.CL] UPDATED)
  Multimodal Sentiment Analysis (MuSe) 2021 is a challenge focusing on the tasks of sentiment and emotion, as well as physiological-emotion […]
• Auctions Between Regret-Minimizing Agents. (arXiv:2110.11855v1 [cs.GT])
  We analyze a scenario in which software agents implemented as regret minimizing algorithms engage in a repeated auction on behalf […]