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
Mahdi HaghifamNortheastern UniversityVerified email at northeastern.edu
Surya GanguliAssociate Professor, Stanford UniversityVerified email at stanford.edu
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleVerified email at eng.cam.ac.uk
Jeffrey NegreaUniversity of WaterlooVerified email at uwaterloo.ca
Ashish KhistiProfessor, University of TorontoVerified email at comm.utoronto.ca
Marc-Etienne BrunetUniversity of Toronto, ServiceNow ResearchVerified email at cs.toronto.edu
Stanislav FortGoogle DeepMind / Stability AI / Anthropic / Stanford University / Google BrainVerified email at stanford.edu
Kyle HsuStanford UniversityVerified email at cs.stanford.edu
Waseem GharbiehMeta PlatformsVerified email at fb.com
Elnaz BarshanGoogleVerified email at google.com
Wei Ji 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
Alexandre LacosteStaff Research Scientist, ServiceNow ResearchVerified email at servicenow.com
Chin-Wei HuangMicrosoft ResearchVerified email at microsoft.com
Aaron CourvilleProfessor, 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 DeepMind

Verified email at google.com - Homepage

Deep Learning Statistical Learning theory Generalization Theory Neural Network Sparsity Data Sparsity


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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	668	2017
Training generative neural networks via maximum mean discrepancy optimization GK Dziugaite, DM Roy, Z Ghahramani arXiv preprint arXiv:1505.03906, 2015	599	2015
A study of the effect of jpg compression on adversarial images GK Dziugaite, Z Ghahramani, DM Roy arXiv preprint arXiv:1608.00853, 2016	484	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	359	2020
Stabilizing the lottery ticket hypothesis J Frankle, GK Dziugaite, DM Roy, M Carbin arXiv preprint arXiv:1903.01611, 2019	322	2019
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	170	2021
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	165	2020
Neural network matrix factorization GK Dziugaite, DM Roy arXiv preprint arXiv:1511.06443, 2015	156	2015
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	124	2019
Data-dependent PAC-Bayes priors via differential privacy GK Dziugaite, DM Roy Advances in neural information processing systems 31, 2018	124	2018
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	117	2020
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	96	2020
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	84	2021
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	77	2020
Entropy-SGD optimizes the prior of a PAC-Bayes bound: Data-dependent PAC-Bayes priors via differential privacy GK Dziugaite, DM Roy	66*	2018
Relatif: Identifying explanatory training samples via relative influence E Barshan, ME Brunet, GK Dziugaite International Conference on Artificial Intelligence and Statistics, 1899-1909, 2020	65	2020
Information-theoretic generalization bounds for stochastic gradient descent G Neu, GK Dziugaite, M Haghifam, DM Roy Conference on Learning Theory, 3526-3545, 2021	58	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	52	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	44	2020
Requiem for the max rule? WJ Ma, S Shen, G Dziugaite, R van den Berg Vision Research 116, 179-193, 2015	33	2015

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