• Bayesian Nonparametric Methods for Reinforcement Learning in Partially Observable Domains. Finale Doshi-Velez, David Pfau, Frank Wood, Nicholas Roy. Transactions of Pattern Analysis and Machine Intelligence. To appear.
• Improving Safety and Operational Efficiency in Residential Care Settings with WiFi-based Localization. Finale Doshi-Velez, William Li, Yoni Battat, Jun-geun Park, Ben Charrow, Dorothy Curtis, Sachi Hemachandra, Bryan Reimer, Javier Velez, Cynthia Walsh, Don Fredette, Nicholas Roy, Seth Teller. Journal of the American Medical Directors Association 13(6), July 2012, 558-563. preprint
• Reinforcement Learning with Limited Reinforcement: Bayes Risk for Active Learning in POMDPs. Finale Doshi-Velez, Joelle Pineau, Nicholas Roy. AI Journal, Volumes 187–188, August 2012, 115–132. preprint
• A Bayesian nonparametric approach to modeling motion patterns. Joshua Joseph, Finale Doshi-Velez, Albert Huang, Nicholas Roy. Autonomous Robots 31(4): 383-400 (2011). preprint
• Spoken Language Interaction with Model Uncertainty: An Adaptive Human-Robot Interaction System. Finale Doshi, Nicholas Roy. Connection Science November 2008. preprint

• A Bayesian Nonparametric Approach to Modeling Battery Health. Joshua Joseph, Finale Doshi-Velez, Nicholas Roy. ICRA 2012. paper
• Infinite Dynamic Bayesian Networks. Finale Doshi-Velez, David Wingate, Joshua Tenenbaum, Nicholas Roy. ICML 2011. paper presentation
• Online Discovery of Feature Dependencies. Alborz Geramifard, Finale Doshi-Velez, Joshua Redding, Nicholas Roy, Jonathan P. How. ICML 2011. paper
• A Comparison of Human and Agent Reinforcement Learning in Partially Observable Domains. Finale Doshi-Velez and Zoubin Ghahramani. CogSci 2011. paper
• Nonparametric Bayesian Policy Priors for Reinforcement Learning. Finale Doshi-Velez, David Wingate, Joshua Tenenbaum, Nicholas Roy. NIPS 2010 paper
• A Bayesian Nonparametric Approach to Modeling Mobility Patterns. Josh Joseph, Finale Doshi-Velez, Nicholas Roy. AAAI 2010 paper
• The Infinite Partially Observable Markov Decision Process. Finale Doshi-Velez. NIPS 2009 paper
• Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process. Finale Doshi-Velez, David Knowles, Shakir Mohammed, Zoubin Ghahramani. NIPS 2009 paper
• Correlated Non-Parametric Latent Feature Models. Finale Doshi-Velez, Zoubin Ghahramani. UAI 2009 paper spotlight poster
• Accelerated Gibbs Sampling for the Indian Buffet Process. Finale Doshi-Velez, Zoubin Ghahramani. ICML 2009 paper slides poster
• Variational Inference for the Indian Buffet Process. Finale Doshi-Velez, Kurt Miller, Jurgen Van Gael, Yee Whye Teh. AISTATS 2009 (Best Paper Nominee) paper, slides (Presented by Kurt Miller). See below for extended tech report version and code.
• Reinforcement Learning with Limited Reinforcement: Using Bayes-Risk for Active Learning in POMDPS. Finale Doshi, Joelle Pineau, Nicholas Roy. ICML 2008 paper, slides , video
• The Permutable POMDP: Fast Solutions to POMDPs for Preference Elicitation. Finale Doshi, Nicholas Roy. AAMAS 2008 (Best Paper Nominee) paper slides poster
• Collision Detection in Legged Locomotion using Supervised Learning. Finale Doshi, Emma Brunskill, Alex Shkolnik, Tom Kollar, Khash Rohanimanesh, Russ Tedrake, Nicholas Roy. IROS 2007 paper
• Efficient Model Learning for Dialog Management. Finale Doshi, Nicholas Roy. HRI 2007 paper slides video

• Phenotype discovery using combined curated ontologies and electronic health data. Finale Doshi-Velez (joint work with Ryan Adams, Isaac Kohane). MUCMD 2013.
• Transfer Learning by Discovering Latent Task Parameterizations. Finale Doshi-Velez* and George Konidaris*. NIPS 2012 Workshop: Bayesian Nonparametric Models for Reliable Planning and Decision-Making Under Uncertainty paper slides
• An Analysis of Activity Changes in MS Patients: A Case Study in the Use of Bayesian Nonparametrics. Finale Doshi-Velez, Nicholas Roy. NIPS 2011 Workshop: Bayesian Nonparametrics, Hope or Hype? paper poster
• Reports of the AAAI 2011 Spring Symposia. Mark Buller, Paul Cuddihy, Ernest Davis, Patrick Doherty, Finale Doshi-Velez, Esra Erdem, Douglas Fisher, Nancy Green, Knut Hinkelmann, Mary Lou Maher, James McLurkin, Rajiv Maheswaran, Sara Rubinelli, Nathan Schurr, Donia Scott, Dylan Shell, Pedro Szekely, Barbara Thönssen, Arnold B. Urken. AI Magazine 32(3): 119-127 (2011).
• Bayesian Nonparametric Approaches to Reinforcement Learning in Partially Observable Domains. Finale Doshi-Velez. AAAI Doctoral Consortium 2010 paper slides
• Nonparametric Bayesian Methods for Finding Software Bugs. Finale Doshi and Jurgen Van Gael. CRISM Workshop on High Dimensional Data, 2008. poster
• Thinking Machines. Marc Deisenroth, Finale Doshi, and Mate Lengyel. Cambridge Horizons Seminar, 2008. (for a broader audience) poster
• Reinforcement Learning with Limited Reinforcement: Using Bayes Risk for Active Learning in POMDPs. Finale Doshi and Nicholas Roy. ISAIM 2008 paper slides
• Learning User Models with Limited Reinforcement: An Adaptive Human-Robot Interaction System. Finale Doshi and Nicholas Roy. LANGRO 2007 paper slides
• Efficient Model Learning for Dialog Management. Finale Doshi and Nicholas Roy. AAAI 2007 Spring Symposium paper slides
• Model Learning for Dialog Management. Finale Doshi and Nicholas Roy. NIPS 2006 Workshop on Reinforcement Learning. poster

• Bayesian Nonparametric Approaches for Reinforcement Learning in Partially Observable Domains. PhD Thesis, MIT, 2012 thesis and slides; if those documents are too long, check out the 2.5 minute musical summary
• The Indian Buffet Process: Scalable Inference and Extensions. Masters Thesis, Cambridge, 2009 thesis
• Variational Inference for the Indian Buffet Process. Extended version of AISTATS 2009 paper with all equations fully described and derived. tech report
• Efficient Model Learning for Dialog Management. Masters Thesis, MIT, 2007 thesis

Here are slides from a few of my presentations. For a list of my invited talks, please see my CV.

• Presentation at the CSAIL Student Workshop on Bayesian Nonparametric Approaches to Reinforcement Learning in Partially Observable Domains. (Includes more introductory material than conference presentations.) September 2010. (Best Paper Award Winner) slides
• Presentation to the MIT Spoken Language Systems group on POMDPs for dialog management. (Includes more introductory material than conference presentations.) October 2006. slides

• Assessing Compressed-Database Raw Size, #20130212075. Lyric Pankaj Doshi and Finale Doshi-Velez. Issued August 2013.

• iPOMDP model and test-bench (Matlab). This is a rather large set of files that contain both the iPOMDP code (with flags for FFBS/EM as well as several action-selection strategies) and several benchmark domains. Not as tidy as my other code, but you can use it to run many of the iPOMDP experiments from my thesis. code
• Samplers for nonparametric correlated latent feature models (Matlab). Contains Gibbs samplers for the models described in Nonparametric Correlated Latent Feature Models for linear-Gaussian, binary, and exponential likelihood models. samplers and utils (needed for the samplers).
• Gibbs sampling for the IBP (Matlab). Contains two different collapsed samplers (including the fast sampler described in Accelerated Gibbs Sampling for the Indian Buffet Process), one semi-collapsed sampler, and a fully uncollpased sampler for the linear-Gaussian model. samplers and utils (needed for the samplers). November 25, 2009: Changed test matrix format to handle large, sparse matrices AND fixed MH bug when sampling adding new features
• Variational inference for the IBP (Matlab). Contains simplified code described in Variational Inference for the Indian Buffet Process; current version contains only the linear-Gaussian likelihood model. vibp
• Point Based Value Iteration (PBVI) POMDP solver (Matlab). pbvi