Surbhi Goel

According to our database, Surbhi Goel authored at least 43 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Progressive distillation induces an implicit curriculum.

Abhishek Panigrahi, Bingbin Liu, Sadhika Malladi, Andrej Risteski, Surbhi Goel

CoRR, 2024

Logicbreaks: A Framework for Understanding Subversion of Rule-based Inference.

Anton Xue, Avishree Khare, Rajeev Alur, Surbhi Goel, Eric Wong

CoRR, 2024

Tolerant Algorithms for Learning with Arbitrary Covariate Shift.

Surbhi Goel, Abhishek Shetty, Konstantinos Stavropoulos, Arsen Vasilyan

CoRR, 2024

Explicitly Encoding Structural Symmetry is Key to Length Generalization in Arithmetic Tasks.

Mahdi Sabbaghi, George J. Pappas, Hamed Hassani, Surbhi Goel

CoRR, 2024

Complexity Matters: Dynamics of Feature Learning in the Presence of Spurious Correlations.

Guanwen Qiu, Da Kuang, Surbhi Goel

CoRR, 2024

The Evolution of Statistical Induction Heads: In-Context Learning Markov Chains.

Benjamin L. Edelman, Ezra Edelman, Surbhi Goel, Eran Malach, Nikolaos Tsilivis

CoRR, 2024

Stochastic Bandits with ReLU Neural Networks.

Kan Xu, Hamsa Bastani, Surbhi Goel, Osbert Bastani

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Complexity Matters: Feature Learning in the Presence of Spurious Correlations.

Guanwen Qiu, Da Kuang, Surbhi Goel

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Pareto Frontiers in Neural Feature Learning: Data, Compute, Width, and Luck.

Benjamin L. Edelman, Surbhi Goel, Sham M. Kakade, Eran Malach, Cyril Zhang

CoRR, 2023

Exposing Attention Glitches with Flip-Flop Language Modeling.

Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Adversarial Resilience in Sequential Prediction via Abstention.

Surbhi Goel, Steve Hanneke, Shay Moran, Abhishek Shetty

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Pareto Frontiers in Deep Feature Learning: Data, Compute, Width, and Luck.

Benjamin L. Edelman, Surbhi Goel, Sham M. Kakade, Eran Malach, Cyril Zhang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Transformers Learn Shortcuts to Automata.

Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Learning Narrow One-Hidden-Layer ReLU Networks.

Sitan Chen, Zehao Dou, Surbhi Goel, Adam R. Klivans, Raghu Meka

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms.

Surbhi Goel, Sham M. Kakade, Adam Kalai, Cyril Zhang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit.

Boaz Barak, Benjamin L. Edelman, Surbhi Goel, Sham M. Kakade, Eran Malach, Cyril Zhang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Understanding Contrastive Learning Requires Incorporating Inductive Biases.

Nikunj Saunshi, Jordan T. Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham M. Kakade, Akshay Krishnamurthy

Proceedings of the International Conference on Machine Learning, 2022

Inductive Biases and Variable Creation in Self-Attention Mechanisms.

Benjamin L. Edelman, Surbhi Goel, Sham M. Kakade, Cyril Zhang

Proceedings of the International Conference on Machine Learning, 2022

Anti-Concentrated Confidence Bonuses For Scalable Exploration.

Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade

Proceedings of the Tenth International Conference on Learning Representations, 2022

Investigating the Role of Negatives in Contrastive Representation Learning.

Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Dipendra Misra

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Gone Fishing: Neural Active Learning with Fisher Embeddings.

Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Tight Hardness Results for Training Depth-2 ReLU Networks.

Surbhi Goel, Adam R. Klivans, Pasin Manurangsi, Daniel Reichman

Proceedings of the 12th Innovations in Theoretical Computer Science Conference, 2021

Statistical Estimation from Dependent Data.

Anthimos Vardis Kandiros, Yuval Dagan, Nishanth Dikkala, Surbhi Goel, Constantinos Daskalakis

Proceedings of the 38th International Conference on Machine Learning, 2021

Acceleration via Fractal Learning Rate Schedules.

Naman Agarwal, Surbhi Goel, Cyril Zhang

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

From Boltzmann Machines to Neural Networks and Back Again.

Surbhi Goel, Adam R. Klivans, Frederic Koehler

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Statistical-Query Lower Bounds via Functional Gradients.

Surbhi Goel, Aravind Gollakota, Adam R. Klivans

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Efficiently Learning Adversarially Robust Halfspaces with Noise.

Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro

Proceedings of the 37th International Conference on Machine Learning, 2020

Learning Mixtures of Graphs from Epidemic Cascades.

Jessica Hoffmann, Soumya Basu, Surbhi Goel, Constantine Caramanis

Proceedings of the 37th International Conference on Machine Learning, 2020

Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent.

Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam R. Klivans

Proceedings of the 37th International Conference on Machine Learning, 2020

Approximation Schemes for ReLU Regression.

Ilias Diakonikolas, Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans, Mahdi Soltanolkotabi

Proceedings of the Conference on Learning Theory, 2020

Learning Ising and Potts Models with Latent Variables.

Surbhi Goel

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Learning Restricted Boltzmann Machines with Arbitrary External Fields.

Surbhi Goel

CoRR, 2019

Disentangling Mixtures of Epidemics on Graphs.

Jessica Hoffmann, Soumya Basu, Surbhi Goel, Constantine Caramanis

CoRR, 2019

Learning Two layer Networks with Multinomial Activation and High Thresholds.

Surbhi Goel, Rina Panigrahy

CoRR, 2019

Quantifying Perceptual Distortion of Adversarial Examples.

Matt Jordan, Naren Manoj, Surbhi Goel, Alexandros G. Dimakis

CoRR, 2019

Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals.

Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Learning Ising Models with Independent Failures.

Surbhi Goel, Daniel M. Kane, Adam R. Klivans

Proceedings of the Conference on Learning Theory, 2019

Learning Neural Networks with Two Nonlinear Layers in Polynomial Time.

Surbhi Goel, Adam R. Klivans

Proceedings of the Conference on Learning Theory, 2019

2018

Improved Learning of One-hidden-layer Convolutional Neural Networks with Overlaps.

Simon S. Du, Surbhi Goel

CoRR, 2018

Learning One Convolutional Layer with Overlapping Patches.

Surbhi Goel, Adam R. Klivans, Raghu Meka

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Learning Depth-Three Neural Networks in Polynomial Time.

Surbhi Goel, Adam R. Klivans

CoRR, 2017

Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks.

Surbhi Goel, Adam R. Klivans

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Reliably Learning the ReLU in Polynomial Time.

Surbhi Goel, Varun Kanade, Adam R. Klivans, Justin Thaler

Proceedings of the 30th Conference on Learning Theory, 2017