Surbhi Goel

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Adam KlivansProfessor of Computer Science, University of Texas at AustinVerified email at cs.utexas.edu
Sushrut KarmalkarUniversity of Wisconsin-MadisonVerified email at cs.utexas.edu
Justin ThalerGeorgetown UniversityVerified email at georgetown.edu
Varun KanadeUniversity of OxfordVerified email at cs.ox.ac.uk
Cyril ZhangMicrosoft ResearchVerified email at microsoft.com
Jordan T. AshMicrosoft Research NYCVerified email at princeton.edu
Akshay KrishnamurthyUniversity of Massachusetts AmherstVerified email at cs.umass.edu
Sham M KakadeHarvard UniversityVerified email at seas.harvard.edu
Ilias DiakonikolasUniversity of Wisconsin-MadisonVerified email at wisc.edu
Raghu MekaUniversity of California, Los AngelesVerified email at cs.ucla.edu
Dipendra MisraMicrosoft Research New YorkVerified email at microsoft.com
Zhihan JinETHVerified email at student.ethz.ch
Mahdi SoltanolkotabiUniversity of Southern CaliforniaVerified email at usc.edu
Omar MontasserPhD Student, TTI-ChicagoVerified email at ttic.edu
Nathan SrebroProfessor, TTIC and University of ChicagoVerified email at ttic.edu
Alexandros G DimakisProfessor, ECE University of Texas at AustinVerified email at austin.utexas.edu
Naren Sarayu ManojStudent, Toyota Technological Institute ChicagoVerified email at ttic.edu
Matt JordanGraduate Research Assistant, UT AustinVerified email at cs.utexas.edu
Daniel KaneUniversity of California, San DiegoVerified email at ucsd.edu
Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonVerified email at cs.washington.edu

Surbhi Goel

Postdoctoral Researcher, Microsoft Research NYC

Verified email at microsoft.com - Homepage

Learning Theory Machine Learning


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Reliably learning the relu in polynomial time S Goel, V Kanade, A Klivans, J Thaler Conference on Learning Theory (COLT) 2017, 2016	110	2016
Learning neural networks with two nonlinear layers in polynomial time S Goel, A Klivans Conference on Learning Theory (COLT) 2019, 2017	86*	2017
Learning one convolutional layer with overlapping patches S Goel, A Klivans, R Meka International Conference on Machine Learning (ICML) 2018, 2018	68	2018
Superpolynomial lower bounds for learning one-layer neural networks using gradient descent S Goel, A Gollakota, Z Jin, S Karmalkar, A Klivans International Conference on Machine Learning, 3587-3596, 2020	36	2020
Time/accuracy tradeoffs for learning a relu with respect to gaussian marginals S Goel, S Karmalkar, A Klivans Advances in Neural Information Processing Systems 32, 2019	35	2019
Approximation schemes for relu regression I Diakonikolas, S Goel, S Karmalkar, AR Klivans, M Soltanolkotabi Conference on Learning Theory, 1452-1485, 2020	31	2020
Statistical-query lower bounds via functional gradients S Goel, A Gollakota, A Klivans Advances in Neural Information Processing Systems 33, 2147-2158, 2020	28	2020
Eigenvalue decay implies polynomial-time learnability for neural networks S Goel, A Klivans Advances in Neural Information Processing Systems 30, 2017	27	2017
Efficiently learning adversarially robust halfspaces with noise O Montasser, S Goel, I Diakonikolas, N Srebro International Conference on Machine Learning, 7010-7021, 2020	25	2020
Quantifying perceptual distortion of adversarial examples M Jordan, N Manoj, S Goel, AG Dimakis arXiv preprint arXiv:1902.08265, 2019	20	2019
Learning Ising models with independent failures S Goel, DM Kane, AR Klivans Conference on Learning Theory (COLT) 2019, 2019	16	2019
Improved learning of one-hidden-layer convolutional neural networks with overlaps SS Du, S Goel arXiv preprint arXiv:1805.07798, 2018	15	2018
Tight hardness results for training depth-2 ReLU networks S Goel, A Klivans, P Manurangsi, D Reichman arXiv preprint arXiv:2011.13550, 2020	13	2020
Investigating the role of negatives in contrastive representation learning JT Ash, S Goel, A Krishnamurthy, D Misra arXiv preprint arXiv:2106.09943, 2021	10	2021
Learning ising and potts models with latent variables S Goel International Conference on Artificial Intelligence and Statistics, 3557-3566, 2020	9*	2020
Acceleration via fractal learning rate schedules N Agarwal, S Goel, C Zhang International Conference on Machine Learning, 87-99, 2021	7	2021
Understanding contrastive learning requires incorporating inductive biases N Saunshi, J Ash, S Goel, D Misra, C Zhang, S Arora, S Kakade, ... arXiv preprint arXiv:2202.14037, 2022	6	2022
Learning mixtures of graphs from epidemic cascades J Hoffmann, S Basu, S Goel, C Caramanis International Conference on Machine Learning, 4342-4352, 2020	5	2020
Gone fishing: Neural active learning with fisher embeddings J Ash, S Goel, A Krishnamurthy, S Kakade Advances in Neural Information Processing Systems 34, 8927-8939, 2021	4	2021
Statistical Estimation from Dependent Data Y Dagan, C Daskalakis, N Dikkala, S Goel, AV Kandiros arXiv preprint arXiv:2107.09773, 2021	3*	2021

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