Research @Rutgers
2009-2015
I was interested in how we evaluate Reinforcement Learning algorithms and challenges using pre-collected/batch data. You can follow some of my work below. I also keep track of a lot of work that is concerned with "Data Science". So you will notice that some of my blog posts are data analysis hacks that I sometimes do during my free time.
Publications
- Vukosi N. Marivate, Jessica Chemali, Michael Littman, and Emma Brunskill, "Discovering Multi-modal Characteristics in Observational Clinical Data", Machine Learning for Clinical Data Analysis and Healthcare NIPS Workshop 2013. [PDF]
- Vukosi Marivate and Michael Littman, "An Offline Evaluation Metric for Comparing Value Functions". Poster and Extended Abstract: The 1st Multidisciplinary Conference on Reinforcement Learning and Decision Making.
- Vukosi Marivate and Michael Littman, "An Ensemble of Linearly Combined Reinforcement-Learning Agents", Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence, 2013. [PDF Link]
- Monica Babes, Vukosi Marivate, Kaushik Subramanian, Michael Littman,"Apprenticeship Learning About Multiple Intentions", ICML 2011 [PDF link]
- Jordan Ash, Monica Babes, Gal Cohen, Sameen Jalal, Michael Littman, Vukosi Marivate, Philip Quiza, Blase U, "Scratchable Devices: User-Friendly Programming for Household Appliances",HCI International. 2011 [PDF link]
Courses Taken at Graduate Level
CS529: Computational Geometry
CS672: Graph Mining and Network Analysis
CS510: Numerical Analysis
CS536: Pattern Recognition
CS530: Principles of Artificial Intelligence
CS503: Computational Thinking (Very Interesting course)
CS513: Analysis of Data Structures and Algorithms (I am in Computer Science now, have to act the part)
CS534: Computer Vision
CS536: Machine Learning
Light Seminar: Algorithms, Game theory and Data Mining for Internet Ad Systems
Light Seminar on Bioinformatics
Light Seminar: Machine Learning
Light Seminar on Personal Information Management