Katja Hofmann
Impact in
- Information Systems top 1%
- Recommender Systems and Techniques
- Information Retrieval and Search Behavior
- Expert finding and Q&A systems
- Web Data Mining and Analysis
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- Mobile Crowdsensing and Crowdsourcing
Papers in
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- Mobile Crowdsensing and Crowdsourcing 10
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- Reinforcement Learning in Robotics 16
- Topic Modeling 11
- Natural Language Processing Techniques 9
- Domain Adaptation and Few-Shot Learning 8
- Co-authors
- Filip RadlinskiMaarten de RijkeKonstantina ChristakopoulouShimon WhitesonStefan SpangeStefan BöttgerMatthias F. MelzigAnne Schuth
- Journals
- New Journal of Chemistry (5 papers)The Journal of Organic Chemistry (3 papers)ACM Transactions on Information Systems (1 paper)Chemistry of Materials (1 paper)Journal of Sol-Gel Science and Technology (1 paper)
- Partner nations
- United KingdomNetherlandsUnited States
In The Last Decade
Katja Hofmann
95 papers receiving 1.6k citations
Hit Papers
Peers
Comparison fields: 5 of 149
- Information Systems 622
- Computer Science Applications 141
- Artificial Intelligence 835
- Management Science and Operations Research 224
- Catalysis 97
Countries citing papers authored by Katja Hofmann
This map shows the geographic impact of Katja Hofmann's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Katja Hofmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Katja Hofmann more than expected).
Fields of papers citing papers by Katja Hofmann
This network shows the impact of papers produced by Katja Hofmann. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Katja Hofmann. The network helps show where Katja Hofmann may publish in the future.
Co-authors
The 25 scholars most cited alongside Katja Hofmann, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning | 2021 | 9 |
| 2 | AMRL: Aggregated Memory For Reinforcement Learning | 2020 | 4 |
| 3 | VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning | 2020 | 9 |
| 4 | 2020 | 2 | |
| 5 | Recognizing Spatial Configurations of Objects with Graph Neural Networks. | 2020 | 1 |
| 6 | Variational Integrator Networks for Physically Structured Embeddings. | 2019 | 1 |
| 7 | Variational Integrator Networks for Physically Meaningful Embeddings. | 2019 | 3 |
| 8 | Learning good policies from suboptimal demonstrations | 2018 | 1 |
| 9 | Meta Reinforcement Learning with Latent Variable Gaussian Processes | 2018 | 6 |
| 10 | Player expectations of a learning AI companion in Minecraft | 2017 | 1 |
| 11 | 2017 | 3 | |
| 12 | Decoding multitask DQN in the world of Minecraft | 2016 | 3 |
| 13 | The Malmo platform for artificial intelligence experimentation | 2016 | 96 |
| 14 | The University of Amsterdam at the TREC 2011 Session Track | 2012 | 2 |
| 15 | Heuristic ranking and diversification of web documents | 2010 | 8 |
| 16 | The University of Amsterdam at TREC 2010: Session, Entity, and Relevance Feedback | 2010 | 7 |
| 17 | An exploratory study of user goals and strategies in podcast search | 2008 | 7 |
| 18 | Query and Document Models for Enterprise Search | 2008 | 5 |
| 19 | Web Corpus Cleaning using Content and Structure | 2007 | 4 |
| 20 | The University of Amsterdam at the TREC 2007 QA Track | 2007 | 1 |
About Katja Hofmann
Katja Hofmann is a scholar working on Computer Science Applications, Artificial Intelligence, Management Science and Operations Research, Information Systems and Physical and Theoretical Chemistry, having authored 96 papers that have together received 1.7k indexed citations. Recurring topics across this work include Information Retrieval and Search Behavior (23 papers), Reinforcement Learning in Robotics (16 papers), Expert finding and Q&A systems (12 papers), Topic Modeling (11 papers), Mobile Crowdsensing and Crowdsourcing (10 papers), Natural Language Processing Techniques (9 papers), Advanced Bandit Algorithms Research (9 papers) and Domain Adaptation and Few-Shot Learning (8 papers). The work is most often cited by research in Information Systems (622 citations), Computer Science Applications (141 citations), Artificial Intelligence (835 citations), Management Science and Operations Research (224 citations) and Catalysis (97 citations). Katja Hofmann has collaborated with scholars based in United Kingdom, Netherlands and United States. Frequent co-authors include Filip Radlinski, Maarten de Rijke, Konstantina Christakopoulou, Shimon Whiteson, Stefan Spange, Stefan Böttger, Matthias F. Melzig, Anne Schuth, Matthew Johnson and David E. Bignell. Their work appears in journals such as New Journal of Chemistry, The Journal of Organic Chemistry, ACM Transactions on Information Systems, Chemistry of Materials and Journal of Sol-Gel Science and Technology.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.