Daniel M. Roy

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Gintare Karolina DziugaiteGoogle DeepMindVerified email at google.com
Joshua B. TenenbaumMITVerified email at mit.edu
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMindVerified email at stats.ox.ac.uk
Vikash K. MansinghkaMIT, Probabilistic Computing ProjectVerified email at mit.edu
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleVerified email at eng.cam.ac.uk
martin rinardMITVerified email at lcs.mit.edu
Noah D. GoodmanStanford UniversityVerified email at stanford.edu
Balaji LakshminarayananSenior Staff Research Scientist at Google BrainVerified email at google.com
Cameron E. FreerResearch Scientist, MIT and Project Associate Professor, Keio UniversityVerified email at mit.edu
Jonathan FrankleMassachusetts Institute of TechnologyVerified email at mit.edu
Michael CarbinMassachusetts Institute of TechnologyVerified email at csail.mit.edu
Jeffrey NegreaUniversity of TorontoVerified email at utstat.toronto.edu
Mahdi HaghifamPh.D. Candidate, University of TorontoVerified email at mail.utoronto.ca
Cristian CadarImperial College LondonVerified email at imperial.ac.uk
Nate AckermanHarvard UniversityVerified email at math.harvard.edu
Kallista A. BonawitzGoogleVerified email at google.com
Victor VeitchUniversity of Chicago, GoogleVerified email at uchicago.edu
Peter OrbanzColumbia UniversityVerified email at stat.columbia.edu
Tudor LeuVerified email at google.com
Ashish KhistiProfessor, University of TorontoVerified email at comm.utoronto.ca

Daniel M. Roy

University of Toronto, Dept of Statistical Sciences; Computer Science

Verified email at utoronto.ca - Homepage

Statistics Theoretical Computer Science Probability Machine Learning Artificial Intelligence


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Church: a language for generative models ND Goodman, VK Mansinghka, D Roy, K Bonawitz, JB Tenenbaum Uncertainty in Artificial Intelligence 22, 23, 2008	946*	2008
Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data GK Dziugaite, DM Roy arXiv preprint arXiv:1703.11008, 2017	600	2017
Training generative neural networks via maximum mean discrepancy optimization GK Dziugaite, DM Roy, Z Ghahramani arXiv preprint arXiv:1505.03906, 2015	540	2015
Enhancing server availability and security through failure-oblivious computing M Rinard, C Cadar, D Dumitran, DM Roy, T Leu, WS Beebee Jr Proceedings of the 6th conference on Symposium on Opearting Systems Design …, 2004	470	2004
A study of the effect of jpg compression on adversarial images GK Dziugaite, Z Ghahramani, DM Roy arXiv preprint arXiv:1608.00853, 2016	430	2016
Linear mode connectivity and the lottery ticket hypothesis J Frankle, GK Dziugaite, D Roy, M Carbin International Conference on Machine Learning, 3259-3269, 2020	268	2020
Bayesian models of graphs, arrays and other exchangeable random structures P Orbanz, DM Roy IEEE transactions on pattern analysis and machine intelligence 37 (2), 437-461, 2014	266	2014
Mondrian forests: Efficient online random forests B Lakshminarayanan, DM Roy, YW Teh Advances in neural information processing systems 27, 2014	246	2014
Stabilizing the lottery ticket hypothesis J Frankle, GK Dziugaite, DM Roy, M Carbin arXiv preprint arXiv:1903.01611, 2019	214	2019
The Mondrian process DM Roy, YW Teh Adv. in Neural Inform. Processing Syst 21, 27, 2009	189	2009
Neural network matrix factorization GK Dziugaite, DM Roy arXiv preprint arXiv:1511.06443, 2015	145	2015
Complexity of Inference in Latent Dirichlet Allocation D Sontag, DM Roy	140*
A dynamic technique for eliminating buffer overflow vulnerabilities (and other memory errors) M Rinard, C Cadar, D Dumitran, DM Roy, T Leu 20th Annual Computer Security Applications Conference, 82-90, 2004	136	2004
Bayesian agglomerative clustering with coalescents YW Teh, H Daumé III, D Roy Arxiv preprint arXiv:0907.0781, 2009	135	2009
Pruning neural networks at initialization: Why are we missing the mark? J Frankle, GK Dziugaite, DM Roy, M Carbin arXiv preprint arXiv:2009.08576, 2020	134	2020
Random function priors for exchangeable arrays with applications to graphs and relational data J Lloyd, P Orbanz, Z Ghahramani, DM Roy Advances in Neural Information Processing Systems 25, 2012	134	2012
Probabilistically accurate program transformations S Misailovic, D Roy, M Rinard Static Analysis, 316-333, 2011	114	2011
Information-theoretic generalization bounds for SGLD via data-dependent estimates J Negrea, M Haghifam, GK Dziugaite, A Khisti, DM Roy Advances in Neural Information Processing Systems 32, 2019	107	2019
Data-dependent PAC-Bayes priors via differential privacy GK Dziugaite, DM Roy Advances in neural information processing systems 31, 2018	103	2018
The class of random graphs arising from exchangeable random measures V Veitch, DM Roy arXiv preprint arXiv:1512.03099, 2015	100	2015

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