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 @iclr_conf: #ICLR 2023 welcomes your paper submissions: iclr.cc/Conferences/20…. The Abstract deadline is Sep 21st, and the full paper d… 1 week ago
  • @luisa_zintgraf @shimon8282 Huge congratulations 🎉🥳🙌 - it's been such a delight to work with you! 1 week ago
  • RT @davlindner: Our #ICML2022 paper argues that representing learned human preferences as constraints might be a more robust alternative to… 2 months ago
Follow @katjahofmann

RSS Microsoft Research News

  • Assessing AI system performance: thinking beyond models to deployment contexts
    AI systems are becoming increasingly complex as we move from visionary research to deployable technologies such as self-driving cars, clinical predictive models, and novel accessibility devices. Unlike singular AI models, it is more difficult to assess whether these more complex AI systems are performing consistently and as intended to realize human benefit. […]
  • AI Models vs. AI Systems: Understanding Units of Performance Assessment
    As AI becomes more deeply integrated into every aspect of our lives, it is essential that AI systems perform appropriately for their intended use. We know AI models can never be perfect, so how do we decide when AI performance is ‘good enough’ for use in a real life application? Is level of accuracy a […] The post AI Models vs. AI Systems: Understanding Unit […]
  • CCF: Bringing efficiency and usability to a decentralized trust model
    Online trust has come a long way since the time of centralized databases, where information was concentrated in one location and the security and validation of that information relied on a core set of people and systems. While convenient, this model of centralized management and oversight had a number of drawbacks. Trust depended on how […] The post CCF: Bri […]

RSS Recent MSR Publications

  • Assessing AI system performance: thinking beyond models to deployment contexts
    AI systems are becoming increasingly complex as we move from visionary research to deployable technologies such as self-driving cars, clinical predictive models, and novel accessibility devices. Unlike singular AI models, it is more difficult to assess whether these more complex AI systems are performing consistently and as intended to realize human benefit. […]
  • AI Models vs. AI Systems: Understanding Units of Performance Assessment
    As AI becomes more deeply integrated into every aspect of our lives, it is essential that AI systems perform appropriately for their intended use. We know AI models can never be perfect, so how do we decide when AI performance is ‘good enough’ for use in a real life application? Is level of accuracy a […] The post AI Models vs. AI Systems: Understanding Unit […]
  • CCF: Bringing efficiency and usability to a decentralized trust model
    Online trust has come a long way since the time of centralized databases, where information was concentrated in one location and the security and validation of that information relied on a core set of people and systems. While convenient, this model of centralized management and oversight had a number of drawbacks. Trust depended on how […] The post CCF: Bri […]

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