Surbhi Goel

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Adam KlivansProfessor of Computer Science, University of Texas at AustinVerified email at cs.utexas.edu
Cyril ZhangMicrosoft Research NYCVerified email at microsoft.com
Sham M KakadeHarvard UniversityVerified email at seas.harvard.edu
Jordan T. AshMicrosoft Research NYCVerified email at princeton.edu
Akshay KrishnamurthyUniversity of Massachusetts AmherstVerified email at cs.umass.edu
Sushrut KarmalkarUniversity of Wisconsin-MadisonVerified email at cs.utexas.edu
Benjamin L. EdelmanHarvard UniversityVerified email at g.harvard.edu
Justin ThalerGeorgetown UniversityVerified email at georgetown.edu
Varun KanadeUniversity of OxfordVerified email at cs.ox.ac.uk
Aravind GollakotaPhD Student, University of Texas at AustinVerified email at cs.utexas.edu
Dipendra MisraMicrosoft Research New YorkVerified email at microsoft.com
Raghu MekaUniversity of California, Los AngelesVerified email at cs.ucla.edu
Ilias DiakonikolasUniversity of Wisconsin-MadisonVerified email at wisc.edu
Eran MalachSchool of Computer Science, Hebrew UniversityVerified email at mail.huji.ac.il
Bingbin LiuPhD Student in Machine Learning Department, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Nikunj SaunshiResearch Scientist, GoogleVerified email at google.com
Sanjeev AroraProfessor of Computer Science, Princeton UniversityVerified email at cs.princeton.edu
Zhihan JinETH ZurichVerified email at student.ethz.ch
Boaz BarakHarvard UniversityVerified email at boazbarak.org
Mahdi SoltanolkotabiUniversity of Southern CaliforniaVerified email at usc.edu

Surbhi Goel

Assistant Professor, University of Pennsylvania

Verified email at cis.upenn.edu - Homepage

Learning Theory Machine Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Reliably learning the relu in polynomial time S Goel, V Kanade, A Klivans, J Thaler Conference on Learning Theory (COLT) 2017, 2016	134	2016
Learning neural networks with two nonlinear layers in polynomial time S Goel, A Klivans Conference on Learning Theory (COLT) 2019, 2017	99*	2017
Learning one convolutional layer with overlapping patches S Goel, A Klivans, R Meka International Conference on Machine Learning (ICML) 2018, 2018	79	2018
Understanding contrastive learning requires incorporating inductive biases N Saunshi, J Ash, S Goel, D Misra, C Zhang, S Arora, S Kakade, ... International Conference on Machine Learning, 19250-19286, 2022	71	2022
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	67	2020
Hidden progress in deep learning: Sgd learns parities near the computational limit B Barak, B Edelman, S Goel, S Kakade, E Malach, C Zhang Advances in Neural Information Processing Systems 35, 21750-21764, 2022	59	2022
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	54	2019
Inductive biases and variable creation in self-attention mechanisms BL Edelman, S Goel, S Kakade, C Zhang International Conference on Machine Learning, 5793-5831, 2022	51	2022
Approximation schemes for relu regression I Diakonikolas, S Goel, S Karmalkar, AR Klivans, M Soltanolkotabi Conference on learning theory, 1452-1485, 2020	49	2020
Statistical-query lower bounds via functional gradients S Goel, A Gollakota, A Klivans Advances in Neural Information Processing Systems 33, 2147-2158, 2020	49	2020
Transformers learn shortcuts to automata B Liu, JT Ash, S Goel, A Krishnamurthy, C Zhang arXiv preprint arXiv:2210.10749, 2022	44	2022
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	39	2021
Investigating the role of negatives in contrastive representation learning JT Ash, S Goel, A Krishnamurthy, D Misra arXiv preprint arXiv:2106.09943, 2021	38	2021
Efficiently learning adversarially robust halfspaces with noise O Montasser, S Goel, I Diakonikolas, N Srebro International Conference on Machine Learning, 7010-7021, 2020	34	2020
Quantifying perceptual distortion of adversarial examples M Jordan, N Manoj, S Goel, AG Dimakis arXiv preprint arXiv:1902.08265, 2019	29	2019
Eigenvalue decay implies polynomial-time learnability for neural networks S Goel, A Klivans Advances in Neural Information Processing Systems 30, 2017	27	2017
Tight hardness results for training depth-2 ReLU networks S Goel, A Klivans, P Manurangsi, D Reichman arXiv preprint arXiv:2011.13550, 2020	26	2020
Learning Ising models with independent failures S Goel, DM Kane, AR Klivans Conference on Learning Theory (COLT) 2019, 2019	18	2019
Improved learning of one-hidden-layer convolutional neural networks with overlaps SS Du, S Goel arXiv preprint arXiv:1805.07798, 2018	18	2018
Acceleration via fractal learning rate schedules N Agarwal, S Goel, C Zhang International Conference on Machine Learning, 87-99, 2021	17	2021

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