Emma Brunskill mainly focuses on Artificial intelligence, Reinforcement learning, Machine learning, Mathematical optimization and Markov decision process. She has included themes like Domain and Computer vision in her Artificial intelligence study. Her Reinforcement learning research is multidisciplinary, relying on both Sequence and Importance sampling.
In the field of Machine learning, her study on Leverage overlaps with subjects such as Ranging. Her Mathematical optimization research is multidisciplinary, incorporating perspectives in Partially observable Markov decision process, Regret and Macro. Her research in Markov decision process intersects with topics in Algorithm, Data-driven, Finite set and Learning analytics.
Emma Brunskill mostly deals with Reinforcement learning, Artificial intelligence, Machine learning, Markov decision process and Mathematical optimization. Emma Brunskill focuses mostly in the field of Reinforcement learning, narrowing it down to matters related to Regret and, in some cases, Function and Structure. Her Artificial intelligence research incorporates themes from Sequence and Set.
Her Machine learning study combines topics from a wide range of disciplines, such as Teaching method and Markov process. Her Markov decision process study combines topics in areas such as Discrete mathematics, Upper and lower bounds and Econometrics. Her work on Bellman equation as part of her general Mathematical optimization study is frequently connected to Probably approximately correct learning, thereby bridging the divide between different branches of science.
Her primary areas of investigation include Reinforcement learning, Markov decision process, Artificial intelligence, Machine learning and Mathematical optimization. Emma Brunskill has researched Reinforcement learning in several fields, including Regret, Interface, Function approximation and Importance sampling. Her Markov decision process research includes elements of Function, Upper and lower bounds, Discrete mathematics and Correctness.
She interconnects Observational study and Linear least squares in the investigation of issues within Artificial intelligence. Her Machine learning study integrates concerns from other disciplines, such as Energy and Reduction. Her research in Mathematical optimization focuses on subjects like Value, which are connected to Bellman equation and Sublinear function.
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Building peer-to-peer systems with chord, a distributed lookup service
F. Dabek;E. Brunskill;M.F. Kaashoek;D. Karger.
Proceedings Eighth Workshop on Hot Topics in Operating Systems (2001)
Designing mobile interfaces for novice and low-literacy users
Indrani Medhi;Somani Patnaik;Emma Brunskill;S.N. Nagasena Gautama.
ACM Transactions on Computer-Human Interaction (2011)
Data-efficient off-policy policy evaluation for reinforcement learning
Philip S. Thomas;Emma Brunskill.
international conference on machine learning (2016)
New Potentials for Data-Driven Intelligent Tutoring System Development and Optimization
Kenneth R. Koedinger;Emma Brunskill;Ryan Shaun Joazeiro de Baker;Elizabeth A. McLaughlin.
Ai Magazine (2013)
Offline policy evaluation across representations with applications to educational games
Travis Mandel;Yun-En Liu;Sergey Levine;Emma Brunskill.
adaptive agents and multi-agents systems (2014)
Sample complexity of episodic fixed-horizon reinforcement learning
Christoph Dann;Emma Brunskill.
neural information processing systems (2015)
The Impact on Individualizing Student Models on Necessary Practice Opportunities.
Jung In Lee;Emma Brunskill.
educational data mining (2012)
Evaluating the accuracy of data collection on mobile phones: a study of forms, sms, and voice
Somani Patnaik;Emma Brunskill;William Thies.
information and communication technologies and development (2009)
Topological mapping using spectral clustering and classification
E. Brunskill;T. Kollar;N. Roy.
intelligent robots and systems (2007)
Efficient Exploration Through Bayesian Deep Q-Networks
Kamyar Azizzadenesheli;Emma Brunskill;Animashree Anandkumar.
information theory and applications (2018)
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