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ORCIDSurbhi Goel
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- affiliation: Microsoft Research New York, USA
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2020 – today
- 2024
[c30]Guanwen Qiu, Da Kuang, Surbhi Goel:
Complexity Matters: Feature Learning in the Presence of Spurious Correlations. ICML 2024- [c29]Kan Xu, Hamsa Bastani, Surbhi Goel, Osbert Bastani:
Stochastic Bandits with ReLU Neural Networks. ICML 2024 - [i39]Benjamin L. Edelman, Ezra Edelman, Surbhi Goel, Eran Malach, Nikolaos Tsilivis:
The Evolution of Statistical Induction Heads: In-Context Learning Markov Chains. CoRR abs/2402.11004 (2024) - [i38]Guanwen Qiu, Da Kuang, Surbhi Goel:
Complexity Matters: Dynamics of Feature Learning in the Presence of Spurious Correlations. CoRR abs/2403.03375 (2024) - [i37]Kan Xu, Hamsa Bastani, Surbhi Goel, Osbert Bastani:
Stochastic Bandits with ReLU Neural Networks. CoRR abs/2405.07331 (2024) - [i36]Mahdi Sabbaghi, George J. Pappas, Hamed Hassani, Surbhi Goel:
Explicitly Encoding Structural Symmetry is Key to Length Generalization in Arithmetic Tasks. CoRR abs/2406.01895 (2024) - [i35]Surbhi Goel, Abhishek Shetty, Konstantinos Stavropoulos, Arsen Vasilyan:
Tolerant Algorithms for Learning with Arbitrary Covariate Shift. CoRR abs/2406.02742 (2024) - [i34]Anton Xue, Avishree Khare, Rajeev Alur, Surbhi Goel, Eric Wong
:
Logicbreaks: A Framework for Understanding Subversion of Rule-based Inference. CoRR abs/2407.00075 (2024) - [i33]Abhishek Panigrahi, Bingbin Liu, Sadhika Malladi, Andrej Risteski, Surbhi Goel:
Progressive distillation induces an implicit curriculum. CoRR abs/2410.05464 (2024) - [i32]Natalie Collina, Surbhi Goel, Varun Gupta, Aaron Roth:
Tractable Agreement Protocols. CoRR abs/2411.19791 (2024) - 2023
- [c28]Sitan Chen, Zehao Dou, Surbhi Goel, Adam R. Klivans, Raghu Meka:
Learning Narrow One-Hidden-Layer ReLU Networks. COLT 2023: 5580-5614 - [c27]Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang:
Transformers Learn Shortcuts to Automata. ICLR 2023 - [c26]Benjamin L. Edelman, Surbhi Goel, Sham M. Kakade, Eran Malach, Cyril Zhang:
Pareto Frontiers in Deep Feature Learning: Data, Compute, Width, and Luck. NeurIPS 2023 - [c25]Surbhi Goel, Steve Hanneke, Shay Moran, Abhishek Shetty:
Adversarial Resilience in Sequential Prediction via Abstention. NeurIPS 2023 - [c24]Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang:
Exposing Attention Glitches with Flip-Flop Language Modeling. NeurIPS 2023 - [i31]Sitan Chen, Zehao Dou, Surbhi Goel, Adam R. Klivans, Raghu Meka:
Learning Narrow One-Hidden-Layer ReLU Networks. CoRR abs/2304.10524 (2023) - [i30]Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang:
Exposing Attention Glitches with Flip-Flop Language Modeling. CoRR abs/2306.00946 (2023) - [i29]Surbhi Goel, Steve Hanneke, Shay Moran, Abhishek Shetty:
Adversarial Resilience in Sequential Prediction via Abstention. CoRR abs/2306.13119 (2023) - [i28]Benjamin L. Edelman, Surbhi Goel, Sham M. Kakade, Eran Malach, Cyril Zhang:
Pareto Frontiers in Neural Feature Learning: Data, Compute, Width, and Luck. CoRR abs/2309.03800 (2023) - 2022
- [c23]Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Dipendra Misra:
Investigating the Role of Negatives in Contrastive Representation Learning. AISTATS 2022: 7187-7209 - [c22]Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade:
Anti-Concentrated Confidence Bonuses For Scalable Exploration. ICLR 2022 - [c21]Benjamin L. Edelman, Surbhi Goel, Sham M. Kakade, Cyril Zhang:
Inductive Biases and Variable Creation in Self-Attention Mechanisms. ICML 2022: 5793-5831 - [c20]Nikunj Saunshi, Jordan T. Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham M. Kakade, Akshay Krishnamurthy:
Understanding Contrastive Learning Requires Incorporating Inductive Biases. ICML 2022: 19250-19286 - [c19]Boaz Barak, Benjamin L. Edelman, Surbhi Goel, Sham M. Kakade, Eran Malach, Cyril Zhang:
Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit. NeurIPS 2022 - [c18]Surbhi Goel, Sham M. Kakade, Adam Kalai, Cyril Zhang:
Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms. NeurIPS 2022 - [i27]Nikunj Saunshi, Jordan T. Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham M. Kakade, Akshay Krishnamurthy:
Understanding Contrastive Learning Requires Incorporating Inductive Biases. CoRR abs/2202.14037 (2022) - [i26]Boaz Barak, Benjamin L. Edelman, Surbhi Goel, Sham M. Kakade, Eran Malach, Cyril Zhang:
Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit. CoRR abs/2207.08799 (2022) - [i25]Surbhi Goel, Sham M. Kakade, Adam Tauman Kalai, Cyril Zhang:
Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms. CoRR abs/2209.00735 (2022) - [i24]Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang:
Transformers Learn Shortcuts to Automata. CoRR abs/2210.10749 (2022) - 2021
- [c17]Naman Agarwal, Surbhi Goel, Cyril Zhang:
Acceleration via Fractal Learning Rate Schedules. ICML 2021: 87-99 - [c16]Anthimos Vardis Kandiros, Yuval Dagan, Nishanth Dikkala, Surbhi Goel, Constantinos Daskalakis:
Statistical Estimation from Dependent Data. ICML 2021: 5269-5278 - [c15]Surbhi Goel, Adam R. Klivans, Pasin Manurangsi, Daniel Reichman:
Tight Hardness Results for Training Depth-2 ReLU Networks. ITCS 2021: 22:1-22:14 - [c14]Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade:
Gone Fishing: Neural Active Learning with Fisher Embeddings. NeurIPS 2021: 8927-8939 - [i23]Naman Agarwal, Surbhi Goel, Cyril Zhang:
Acceleration via Fractal Learning Rate Schedules. CoRR abs/2103.01338 (2021) - [i22]Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade:
Gone Fishing: Neural Active Learning with Fisher Embeddings. CoRR abs/2106.09675 (2021) - [i21]Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Dipendra Misra:
Investigating the Role of Negatives in Contrastive Representation Learning. CoRR abs/2106.09943 (2021) - [i20]Yuval Dagan, Constantinos Daskalakis, Nishanth Dikkala, Surbhi Goel, Anthimos Vardis Kandiros:
Statistical Estimation from Dependent Data. CoRR abs/2107.09773 (2021) - [i19]Benjamin L. Edelman, Surbhi Goel, Sham M. Kakade, Cyril Zhang:
Inductive Biases and Variable Creation in Self-Attention Mechanisms. CoRR abs/2110.10090 (2021) - [i18]Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade:
Anti-Concentrated Confidence Bonuses for Scalable Exploration. CoRR abs/2110.11202 (2021) - 2020
- [c13]Surbhi Goel:
Learning Ising and Potts Models with Latent Variables. AISTATS 2020: 3557-3566 - [c12]Ilias Diakonikolas, Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans, Mahdi Soltanolkotabi:
Approximation Schemes for ReLU Regression. COLT 2020: 1452-1485 - [c11]Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam R. Klivans:
Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent. ICML 2020: 3587-3596 - [c10]Jessica Hoffmann, Soumya Basu, Surbhi Goel, Constantine Caramanis:
Learning Mixtures of Graphs from Epidemic Cascades. ICML 2020: 4342-4352 - [c9]Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro:
Efficiently Learning Adversarially Robust Halfspaces with Noise. ICML 2020: 7010-7021 - [c8]Surbhi Goel, Aravind Gollakota, Adam R. Klivans:
Statistical-Query Lower Bounds via Functional Gradients. NeurIPS 2020 - [c7]Surbhi Goel, Adam R. Klivans, Frederic Koehler:
From Boltzmann Machines to Neural Networks and Back Again. NeurIPS 2020 - [i17]Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro:
Efficiently Learning Adversarially Robust Halfspaces with Noise. CoRR abs/2005.07652 (2020) - [i16]Ilias Diakonikolas, Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans, Mahdi Soltanolkotabi:
Approximation Schemes for ReLU Regression. CoRR abs/2005.12844 (2020) - [i15]Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam R. Klivans:
Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent. CoRR abs/2006.12011 (2020) - [i14]Surbhi Goel, Aravind Gollakota, Adam R. Klivans:
Statistical-Query Lower Bounds via Functional Gradients. CoRR abs/2006.15812 (2020) - [i13]Surbhi Goel, Adam R. Klivans, Frederic Koehler:
From Boltzmann Machines to Neural Networks and Back Again. CoRR abs/2007.12815 (2020) - [i12]Surbhi Goel, Adam R. Klivans, Pasin Manurangsi, Daniel Reichman:
Tight Hardness Results for Training Depth-2 ReLU Networks. CoRR abs/2011.13550 (2020)
2010 – 2019
- 2019
- [c6]Surbhi Goel, Daniel M. Kane, Adam R. Klivans:
Learning Ising Models with Independent Failures. COLT 2019: 1449-1469 - [c5]Surbhi Goel, Adam R. Klivans:
Learning Neural Networks with Two Nonlinear Layers in Polynomial Time. COLT 2019: 1470-1499 - [c4]Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans:
Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals. NeurIPS 2019: 8582-8591 - [i11]Surbhi Goel, Daniel M. Kane, Adam R. Klivans:
Learning Ising Models with Independent Failures. CoRR abs/1902.04728 (2019) - [i10]Matt Jordan, Naren Manoj, Surbhi Goel, Alexandros G. Dimakis:
Quantifying Perceptual Distortion of Adversarial Examples. CoRR abs/1902.08265 (2019) - [i9]Surbhi Goel, Rina Panigrahy:
Learning Two layer Networks with Multinomial Activation and High Thresholds. CoRR abs/1903.09231 (2019) - [i8]Jessica Hoffmann, Soumya Basu, Surbhi Goel, Constantine Caramanis:
Disentangling Mixtures of Epidemics on Graphs. CoRR abs/1906.06057 (2019) - [i7]Surbhi Goel:
Learning Restricted Boltzmann Machines with Arbitrary External Fields. CoRR abs/1906.06595 (2019) - [i6]Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans:
Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals. CoRR abs/1911.01462 (2019) - 2018
- [c3]Surbhi Goel, Adam R. Klivans, Raghu Meka:
Learning One Convolutional Layer with Overlapping Patches. ICML 2018: 1778-1786 - [i5]Surbhi Goel, Adam R. Klivans, Raghu Meka:
Learning One Convolutional Layer with Overlapping Patches. CoRR abs/1802.02547 (2018) - [i4]Simon S. Du, Surbhi Goel:
Improved Learning of One-hidden-layer Convolutional Neural Networks with Overlaps. CoRR abs/1805.07798 (2018) - 2017
- [c2]Surbhi Goel, Varun Kanade, Adam R. Klivans, Justin Thaler:
Reliably Learning the ReLU in Polynomial Time. COLT 2017: 1004-1042 - [c1]Surbhi Goel, Adam R. Klivans:
Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks. NIPS 2017: 2192-2202 - [i3]Surbhi Goel, Adam R. Klivans:
Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks. CoRR abs/1708.03708 (2017) - [i2]Surbhi Goel, Adam R. Klivans:
Learning Depth-Three Neural Networks in Polynomial Time. CoRR abs/1709.06010 (2017) - 2016
- [i1]Surbhi Goel, Varun Kanade, Adam R. Klivans, Justin Thaler:
Reliably Learning the ReLU in Polynomial Time. CoRR abs/1611.10258 (2016)
Coauthor Index
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