| Online inference of topics with latent Dirichlet allocation K Canini, L Shi, T Griffiths Artificial Intelligence and Statistics, 65-72, 2009 | 298 | 2009 |
| Finding credible information sources in social networks based on content and social structure KR Canini, B Suh, PL Pirolli 2011 IEEE Third International Conference on Privacy, Security, Risk and …, 2011 | 195 | 2011 |
| Monotonic calibrated interpolated look-up tables M Gupta, A Cotter, J Pfeifer, K Voevodski, K Canini, A Mangylov, ... The Journal of Machine Learning Research 17 (1), 3790-3836, 2016 | 111 | 2016 |
| Unifying rational models of categorization via the hierarchical Dirichlet process T Griffiths, K Canini, A Sanborn, D Navarro Psychology Press, 2007 | 107 | 2007 |
| Deep lattice networks and partial monotonic functions S You, D Ding, K Canini, J Pfeifer, M Gupta Advances in neural information processing systems 30, 2017 | 106 | 2017 |
| Categorization as nonparametric Bayesian density estimation TL Griffiths, AN Sanborn, KR Canini, DJ Navarro The probabilistic mind: Prospects for Bayesian cognitive science, 303-328, 2008 | 65 | 2008 |
| Fast and flexible monotonic functions with ensembles of lattices M Milani Fard, K Canini, A Cotter, J Pfeifer, M Gupta Advances in neural information processing systems 29, 2016 | 48 | 2016 |
| A case for adaptive datacenters to conserve energy and improve reliability P Bodik, MP Armbrust, K Canini, A Fox, M Jordan, DA Patterson University of California at Berkeley, Tech. Rep. UCB/EECS-2008-127, 2008 | 48 | 2008 |
| Online collaborative filtering J Abernethy, K Canini, J Langford, A Simma University of California at Berkeley, Tech. Rep, 2007 | 46 | 2007 |
| Modeling transfer learning in human categorization with the hierarchical Dirichlet process KR Canini, MM Shashkov, TL Griffiths ICML, 2010 | 43 | 2010 |
| Parallel boosting with momentum I Mukherjee, K Canini, R Frongillo, Y Singer Joint European Conference on Machine Learning and Knowledge Discovery in …, 2013 | 35 | 2013 |
| Revealing human inductive biases for category learning by simulating cultural transmission KR Canini, TL Griffiths, W Vanpaemel, ML Kalish Psychonomic Bulletin & Review 21 (3), 785-793, 2014 | 25 | 2014 |
| A nonparametric Bayesian model of multi-level category learning KR Canini, TL Griffiths Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011 | 24 | 2011 |
| Diminishing returns shape constraints for interpretability and regularization M Gupta, D Bahri, A Cotter, K Canini Advances in neural information processing systems 31, 2018 | 20 | 2018 |
| Launch and iterate: Reducing prediction churn M Milani Fard, Q Cormier, K Canini, M Gupta Advances in Neural Information Processing Systems 29, 2016 | 19 | 2016 |
| Nonparametric Bayesian models of categorization TL Griffiths, AN Sanborn, KR Canini, DJ Navarro, JB Tenenbaum Formal approaches in categorization, 173-198, 2011 | 18 | 2011 |
| Segmenting and recognizing human action using low-level video features D Buchsbaum, K Canini, T Griffiths Proceedings of the Annual Meeting of the Cognitive Science Society 33 (33), 2011 | 16 | 2011 |
| System and method for identifying users relevant to a topic of interest B Suh, KR Canini, PL Pirolli, EH Chi US Patent 8,275,769, 2012 | 12 | 2012 |
| Finding relevant sources in twitter based on content and social structure KR Canini, B Suh, P Pirolli NIPS Workshop, 2010 | 12 | 2010 |
| Launch and iterate: Reducing prediction churn Q Cormier, MM Fard, K Canini, MR Gupta Proceedings of the 30th International Conference on Neural Information …, 2016 | 6 | 2016 |
Thomas L. GriffithsProfessor of Psychology and Computer Science, Princeton UniversityVahvistettu sähköpostiosoite verkkotunnuksessa princeton.edu
