Gintare Karolina Dziugaite

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Daniel M. RoyUniversity of Toronto, Dept of Statistical Sciences; Computer ScienceVerified email at utstat.toronto.edu
Jonathan FrankleMassachusetts Institute of TechnologyVerified email at mit.edu
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleVerified email at eng.cam.ac.uk
Michael CarbinMassachusetts Institute of TechnologyVerified email at csail.mit.edu
Jeffrey NegreaUniversity of TorontoVerified email at utstat.toronto.edu
Ashish KhistiProfessor, University of TorontoVerified email at comm.utoronto.ca
Mahdi HaghifamPh.D. Candidate, University of TorontoVerified email at mail.utoronto.ca
Waseem GharbiehAI Research Scientist, LG ElectronicsVerified email at lge.com
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 ShenPostdoctoral Associate, Baylor College of MedicineVerified email at bcm.edu
Kyle HsuStanford UniversityVerified email at cs.stanford.edu
Marc-Etienne BrunetUniversity of TorontoVerified email at cs.toronto.edu
Elnaz BarshanGoogleVerified email at google.com
Stanislav FortGraduate student, Stanford University / Google AIVerified email at stanford.edu
Surya GanguliAssociate Professor, Stanford UniversityVerified email at stanford.edu
Alexandre LacosteResearch Scientist, Element AIVerified email at elementai.com
Chin-Wei HuangPhD Student at Mila (University of Montreal)Verified email at umontreal.ca
Aaron CourvilleUniversité de MontréalVerified 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

Research Scientist, Element AI

Verified email at elementai.com - Homepage

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


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Training generative neural networks via maximum mean discrepancy optimization GK Dziugaite, DM Roy, Z Ghahramani arXiv preprint arXiv:1505.03906, 2015	353	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	295	2017
A study of the effect of jpg compression on adversarial images GK Dziugaite, Z Ghahramani, DM Roy arXiv preprint arXiv:1608.00853, 2016	199	2016
Neural network matrix factorization GK Dziugaite, DM Roy arXiv preprint arXiv:1511.06443, 2015	102*	2015
Data-dependent PAC-Bayes priors via differential privacy GK Dziugaite, DM Roy arXiv preprint arXiv:1802.09583, 2018	54	2018
The lottery ticket hypothesis at scale J Frankle, GK Dziugaite, DM Roy, M Carbin arXiv preprint arXiv:1903.01611 8, 2019	48*	2019
Entropy-SGD optimizes the prior of a PAC-Bayes bound: Data-dependent PAC-Bayes priors via differential privacy GK Dziugaite, DM Roy	34*	2018
Linear mode connectivity and the lottery ticket hypothesis J Frankle, GK Dziugaite, D Roy, M Carbin International Conference on Machine Learning, 3259-3269, 2020	32	2020
Roy, and Michael Carbin. The lottery ticket hypothesis at scale J Frankle, GK Dziugaite, M Daniel arXiv preprint arXiv:1903.01611 2 (3), 2019	32	2019
Information-theoretic generalization bounds for sgld via data-dependent estimates J Negrea, M Haghifam, GK Dziugaite, A Khisti, DM Roy arXiv preprint arXiv:1911.02151, 2019	29	2019
Requiem for the max rule? WJ Ma, S Shen, G Dziugaite, R van den Berg Vision Research 116, 179-193, 2015	27	2015
Stabilizing the lottery ticket hypothesis J Frankle, GK Dziugaite, DM Roy, M Carbin arXiv preprint arXiv:1903.01611, 2019	21	2019
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 arXiv preprint arXiv:2004.12983, 2020	11	2020
On the role of data in PAC-Bayes bounds GK Dziugaite, K Hsu, W Gharbieh, DM Roy arXiv preprint arXiv:2006.10929, 2020	9	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	7	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 arXiv preprint arXiv:2010.15110, 2020	6	2020
In search of robust measures of generalization GK Dziugaite, A Drouin, B Neal, N Rajkumar, E Caballero, L Wang, ... arXiv preprint arXiv:2010.11924, 2020	6	2020
Revisiting generalization for deep learning: PAC-Bayes, flat minima, and generative models GK Dziugaite University of Cambridge, 2020	6	2020
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	5	2020
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	3	2020

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