Gintare Karolina Dziugaite

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Daniel M. RoyUniversity of Toronto, Dept of Statistical Sciences; Computer ScienceVerified email at utoronto.ca
Jonathan FrankleMassachusetts Institute of TechnologyVerified email at mit.edu
Michael CarbinMassachusetts Institute of TechnologyVerified email at csail.mit.edu
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleVerified email at eng.cam.ac.uk
Jeffrey NegreaUniversity of TorontoVerified email at utstat.toronto.edu
Mahdi HaghifamPh.D. Candidate, University of TorontoVerified email at mail.utoronto.ca
Ashish KhistiProfessor, University of TorontoVerified email at comm.utoronto.ca
Surya GanguliAssociate Professor, Stanford UniversityVerified email at stanford.edu
Waseem GharbiehAI Research Scientist, LG ElectronicsVerified email at lge.com
Kyle HsuStanford UniversityVerified email at cs.stanford.edu
Stanislav FortAnthropic / Stanford University / Google Brain / DeepMindVerified email at stanford.edu
Wei Ji Ma / Whee Ky MaProfessor of Neuroscience and Psychology at New York UniversityVerified email at nyu.edu
Ronald van den BergAssociate professor, University of StockholmVerified email at psychology.su.se
Shan ShenBaylor College of MedicineVerified email at bcm.edu
Marc-Etienne BrunetUniversity of Toronto, ServiceNow ResearchVerified email at cs.toronto.edu
Elnaz BarshanGoogleVerified email at google.com
Alexandre LacosteStaff Research Scientist, ServiceNow ResearchVerified email at servicenow.com
Chin-Wei HuangMicrosoft ResearchVerified email at microsoft.com
Aaron CourvilleAssociate Professor, DIRO, Université de Montréal, Mila, Cifar CAI chairVerified email at umontreal.ca
Pascal VincentFacebook AI Research; U. Montreal (Professor, Computer Sc. & Op. Res.); MILA; CIFARVerified email at iro.umontreal.ca

Gintare Karolina Dziugaite

Google Brain

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Deep Learning Generative Models Statistical Learning theory Neural Network Models for Matrices Generalization Theory


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Training generative neural networks via maximum mean discrepancy optimization GK Dziugaite, DM Roy, Z Ghahramani arXiv preprint arXiv:1505.03906, 2015	479	2015
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	447	2017
A study of the effect of jpg compression on adversarial images GK Dziugaite, Z Ghahramani, DM Roy arXiv preprint arXiv:1608.00853, 2016	335	2016
Stabilizing the lottery ticket hypothesis J Frankle, GK Dziugaite, DM Roy, M Carbin arXiv preprint arXiv:1903.01611, 2019	159*	2019
Linear mode connectivity and the lottery ticket hypothesis J Frankle, GK Dziugaite, D Roy, M Carbin International Conference on Machine Learning, 3259-3269, 2020	150	2020
Neural network matrix factorization GK Dziugaite, DM Roy arXiv preprint arXiv:1511.06443, 2015	148*	2015
Data-dependent PAC-Bayes priors via differential privacy GK Dziugaite, DM Roy Advances in neural information processing systems 31, 2018	82	2018
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	77	2020
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	74	2019
Entropy-SGD optimizes the prior of a PAC-Bayes bound: Data-dependent PAC-Bayes priors via differential privacy GK Dziugaite, DM Roy	52*	2018
Sharpened generalization bounds based on conditional mutual information and an application to noisy, iterative algorithms M Haghifam, J Negrea, A Khisti, DM Roy, GK Dziugaite Advances in Neural Information Processing Systems 33, 9925-9935, 2020	49	2020
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the neural tangent kernel S Fort, GK Dziugaite, M Paul, S Kharaghani, DM Roy, S Ganguli Advances in Neural Information Processing Systems 33, 5850-5861, 2020	41	2020
In search of robust measures of generalization GK Dziugaite, A Drouin, B Neal, N Rajkumar, E Caballero, L Wang, ... Advances in Neural Information Processing Systems 33, 11723-11733, 2020	32	2020
Relatif: Identifying explanatory training samples via relative influence E Barshan, ME Brunet, GK Dziugaite International Conference on Artificial Intelligence and Statistics, 1899-1909, 2020	30	2020
Requiem for the max rule? WJ Ma, S Shen, G Dziugaite, R van den Berg Vision Research 116, 179-193, 2015	30	2015
On the role of data in PAC-Bayes bounds GK Dziugaite, K Hsu, W Gharbieh, G Arpino, D Roy International Conference on Artificial Intelligence and Statistics, 604-612, 2021	27	2021
In defense of uniform convergence: Generalization via derandomization with an application to interpolating predictors J Negrea, GK Dziugaite, D Roy International Conference on Machine Learning, 7263-7272, 2020	22	2020
Neurips 2020 competition: Predicting generalization in deep learning Y Jiang, P Foret, S Yak, DM Roy, H Mobahi, GK Dziugaite, S Bengio, ... arXiv preprint arXiv:2012.07976, 2020	19	2020
On the role of data in PAC-Bayes bounds GK Dziugaite, K Hsu, W Gharbieh, DM Roy arXiv preprint arXiv:2006.10929, 2020	19	2020
Deep learning on a data diet: Finding important examples early in training M Paul, S Ganguli, GK Dziugaite Advances in Neural Information Processing Systems 34, 20596-20607, 2021	10	2021

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