Welcome to the Learning & Adaptive Systems Group at the Department of Computer Science at ETH Zurich. The group is led by Andreas Krause.
Our research is in learning and adaptive systems that actively acquire information, reason and make decisions in large, distributed and uncertain domains, such as sensor networks and the Web. The theoretical aspects include statistical machine learning (online, active, large-scale, ...), probabilistic reasoning and optimization (in particular submodular and non-convex optimization). We devise new algorithms, build models, analyze large and complex data sets and develop systems that can automatically acquire and reason about highly uncertain information. Our application domains include community seismic and traffic sensing, computational sustainability, optimal experimental design and information gathering on the web. See more details here.
- AISTATS 2015 Best Student Paper Award for the paper Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning by Mario Lucic, Mesrob I. Ohannessian, Amin Karbasi and Andreas Krause.
- Olivier Bachem received an ETH Medal for his M.Sc. thesis.
- Consider contributing to the ICML 2015 workshop on Crowdsourcing and Machine Learning.
- Adish Singla awarded a Facebook Ph.D. Fellowship in the area of Machine Learning.
- Josip Djolonga and Alkis Gotovos both received an ETH Medal for outstanding M.Sc. theses.
- Consider contributing to the NIPS 2014 workshops Discrete Optimization in Machine Learning (DISCML) and Crowdsourcing and Machine Learning.
- The Community Seismic Network project features on the cover of CACM in July 2014.
- Amin Karbasi accepts a faculty position at Yale University, starting Fall 2014.
- Yuxin Chen awarded a Google European Ph.D. Fellowship on Interactive Machine Learning.
- We organized an ETH/MPI Summer School on Learning Systems. Lecture videos available here.
- Baharan Mirzasoleiman awarded a Google Anita Borg Scholarship.
- Our paper on Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization receives the IJCAI-JAIR Best Paper Award 2013.
- New tutorial on Submodularity in Machine Learning -- New Directions at ICML 2013.
- Download our Matlab toolbox for optimizing submodular functions