Katja Hofmann

Researcher at Microsoft Research Cambridge, UK

About

Future interactive systems will learn directly from their users, and continuously adapt to their preferences. My research goal is to develop exploration, reward, and learning approaches that will make this possible. [...]

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Katja Hofmann

Intelligent Systems Lab Amsterdam
Informatics Institute
University of Amsterdam

Room C3.258
Science Park 904
1098 XH Amsterdam

+31 (20) 525 6767

LinkedIn
Twitter

Recent Posts

  • Query Formulation with Query Auto-Completion
  • RUSSIR 2014: Lectures on Online Experimentation for Information Retrieval
  • Fast and Reliably Online Learning to Rank for Information Retrieval
  • Practical Online Retrieval Evaluation
  • Balancing Exploration and Exploitation in Online Learning to Rank for IR

Recent Tweets

  • RT @MSFTResearch: We’re now accepting nominations and proposals for the Microsoft Research PhD fellowship across Asia, Canada, United State… 5 days ago
  • RT @iclr_conf: Thank you all for making #ICLR2022 a huge success! We look forward to seeing you next year in Kigali, Rwanda! https://t.co/f… 3 weeks ago
  • I just caught up with @Jenny_L_Davis' powerful #ICLR22 talk on the social side of technology and especially AI syst… twitter.com/i/web/status/1… 4 weeks ago
Follow @katjahofmann

RSS Microsoft Research News

  • (De)ToxiGen: Leveraging large language models to build more robust hate speech detection tools
    It’s a well-known challenge that large language models (LLMs)—growing in popularity thanks to their adaptability across a variety of applications—carry risks. Because they’re trained on large amounts of data from across the internet, they’re capable of generating inappropriate and harmful language based on similar language encountered during training.   Cont […]
  • Partnering people with large language models to find and fix bugs in NLP systems
    Advances in platform models—large-scale models that can serve as foundations across applications—have significantly improved the ability of computers to process natural language. But natural language processing (NLP) models are still far from perfect, sometimes failing in embarrassing ways, like translating “Eu não recomendo este prato” (I don’t recommend th […]
  • FLUTE: A scalable federated learning simulation platform
    Federated learning has become a major area of machine learning (ML) research in recent years due to its versatility in training complex models over massive amounts of data without the need to share that data with a centralized entity. However, despite this flexibility and the amount of research already conducted, it’s difficult to implement due […] The post […]

RSS Recent MSR Publications

  • (De)ToxiGen: Leveraging large language models to build more robust hate speech detection tools
    It’s a well-known challenge that large language models (LLMs)—growing in popularity thanks to their adaptability across a variety of applications—carry risks. Because they’re trained on large amounts of data from across the internet, they’re capable of generating inappropriate and harmful language based on similar language encountered during training.   Cont […]
  • Partnering people with large language models to find and fix bugs in NLP systems
    Advances in platform models—large-scale models that can serve as foundations across applications—have significantly improved the ability of computers to process natural language. But natural language processing (NLP) models are still far from perfect, sometimes failing in embarrassing ways, like translating “Eu não recomendo este prato” (I don’t recommend th […]
  • FLUTE: A scalable federated learning simulation platform
    Federated learning has become a major area of machine learning (ML) research in recent years due to its versatility in training complex models over massive amounts of data without the need to share that data with a centralized entity. However, despite this flexibility and the amount of research already conducted, it’s difficult to implement due […] The post […]

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