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: #ICLR21 will be a virtual-only conference! We have taken this decision now to give authors certainty as they plan and prep… 21 hours ago
  • RT @MSFTResearch: For over a decade, Microsoft researchers have worked on Project InnerEye—technology to help analyze 3D medical imaging wi… 4 days ago
  • RT @iclr_conf: Just under 1 week left for abstract submission - remember to submit your #ICLR2021 abstract by September 28! https://t.co/fI… 5 days ago
Follow @katjahofmann

RSS Microsoft Research News

  • Measuring dataset similarity using optimal transport
    Is FashionMNIST, a dataset of images of clothing items labeled by category, more similar to MNIST or to USPS, both of which are classification datasets of handwritten digits? This is a pretty hard question to answer, but the solution could have an impact on various aspects of machine learning. For example, it could change how […] The post Measuring dataset s […]
  • Project InnerEye open-source deep learning toolkit: Democratizing medical imaging AI
    For over a decade, the Project InnerEye team at Microsoft Research Cambridge has been developing state-of-the-art machine learning methods for the automatic, quantitative analysis of three-dimensional medical images. An important application is to assist clinicians for image preparation and planning tasks for radiotherapy cancer treatment. This task involves […]
  • In search for future of cloud storage, researchers look to holographic storage solutions
    Data storage has always been a key tenet of compute, and with the massive growth in cloud compute, the demand for cloud data storage has opened an avenue for both revisiting prior technologies and developing new ones. It is projected that around 125 zettabytes of data will be generated annually by 2024, and storing this […] The post In search for future of c […]

RSS Recent MSR Publications

  • Measuring dataset similarity using optimal transport
    Is FashionMNIST, a dataset of images of clothing items labeled by category, more similar to MNIST or to USPS, both of which are classification datasets of handwritten digits? This is a pretty hard question to answer, but the solution could have an impact on various aspects of machine learning. For example, it could change how […] The post Measuring dataset s […]
  • Project InnerEye open-source deep learning toolkit: Democratizing medical imaging AI
    For over a decade, the Project InnerEye team at Microsoft Research Cambridge has been developing state-of-the-art machine learning methods for the automatic, quantitative analysis of three-dimensional medical images. An important application is to assist clinicians for image preparation and planning tasks for radiotherapy cancer treatment. This task involves […]
  • In search for future of cloud storage, researchers look to holographic storage solutions
    Data storage has always been a key tenet of compute, and with the massive growth in cloud compute, the demand for cloud data storage has opened an avenue for both revisiting prior technologies and developing new ones. It is projected that around 125 zettabytes of data will be generated annually by 2024, and storing this […] The post In search for future of c […]

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