Reliably Learning the ReLU in Polynomial Time. - Surbhi Goel; Varun Kanade; Adam R. Klivans; Justin Thaler (COLT, 2017)

Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks. - Surbhi Goel; Adam R. Klivans (NIPS, 2017)

Learning Depth-Three Neural Networks in Polynomial Time. - Surbhi Goel; Adam R. Klivans (CoRR, 2017)

Learning One Convolutional Layer with Overlapping Patches. - Surbhi Goel; Adam R. Klivans; Raghu Meka (ICML, 2018)

Improved Learning of One-hidden-layer Convolutional Neural Networks with Overlaps. - Simon S. Du; Surbhi Goel (CoRR, 2018)

Learning Neural Networks with Two Nonlinear Layers in Polynomial Time. - Surbhi Goel; Adam R. Klivans (COLT, 2019)

Learning Ising Models with Independent Failures. - Surbhi Goel; Daniel M. Kane; Adam R. Klivans (COLT, 2019)

Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals. - Surbhi Goel; Sushrut Karmalkar; Adam R. Klivans (NeurIPS, 2019)

Quantifying Perceptual Distortion of Adversarial Examples. - Matt Jordan; Naren Manoj; Surbhi Goel; Alexandros G. Dimakis (CoRR, 2019)

Learning Two layer Networks with Multinomial Activation and High Thresholds. - Surbhi Goel; Rina Panigrahy (CoRR, 2019)

Disentangling Mixtures of Epidemics on Graphs. - Jessica Hoffmann; Soumya Basu 001; Surbhi Goel; Constantine Caramanis (CoRR, 2019)

Learning Restricted Boltzmann Machines with Arbitrary External Fields. - Surbhi Goel (CoRR, 2019)

Learning Ising and Potts Models with Latent Variables. - Surbhi Goel (AISTATS, 2020)

Approximation Schemes for ReLU Regression. - Ilias Diakonikolas; Surbhi Goel; Sushrut Karmalkar; Adam R. Klivans; Mahdi Soltanolkotabi (COLT, 2020)

Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent. - Surbhi Goel; Aravind Gollakota; Zhihan Jin; Sushrut Karmalkar; Adam R. Klivans (ICML, 2020)

Learning Mixtures of Graphs from Epidemic Cascades. - Jessica Hoffmann; Soumya Basu 001; Surbhi Goel; Constantine Caramanis (ICML, 2020)

Efficiently Learning Adversarially Robust Halfspaces with Noise. - Omar Montasser; Surbhi Goel; Ilias Diakonikolas; Nathan Srebro (ICML, 2020)

Statistical-Query Lower Bounds via Functional Gradients. - Surbhi Goel; Aravind Gollakota; Adam R. Klivans (NeurIPS, 2020)

From Boltzmann Machines to Neural Networks and Back Again. - Surbhi Goel; Adam R. Klivans; Frederic Koehler (NeurIPS, 2020)

Acceleration via Fractal Learning Rate Schedules. - Naman Agarwal; Surbhi Goel; Cyril Zhang (ICML, 2021)

Statistical Estimation from Dependent Data. - Anthimos Vardis Kandiros; Yuval Dagan; Nishanth Dikkala; Surbhi Goel; Constantinos Daskalakis (ICML, 2021)

Tight Hardness Results for Training Depth-2 ReLU Networks. - Surbhi Goel; Adam R. Klivans; Pasin Manurangsi; Daniel Reichman 001 (ITCS, 2021)

Gone Fishing: Neural Active Learning with Fisher Embeddings. - Jordan T. Ash; Surbhi Goel; Akshay Krishnamurthy; Sham M. Kakade (NeurIPS, 2021)

Investigating the Role of Negatives in Contrastive Representation Learning. - Jordan T. Ash; Surbhi Goel; Akshay Krishnamurthy; Dipendra Misra (AISTATS, 2022)

Anti-Concentrated Confidence Bonuses For Scalable Exploration. - Jordan T. Ash; Cyril Zhang; Surbhi Goel; Akshay Krishnamurthy; Sham M. Kakade (ICLR, 2022)

Inductive Biases and Variable Creation in Self-Attention Mechanisms. - Benjamin L. Edelman; Surbhi Goel; Sham M. Kakade; Cyril Zhang (ICML, 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 (ICML, 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 (NeurIPS, 2022)

Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms. - Surbhi Goel; Sham M. Kakade; Adam Kalai; Cyril Zhang (NeurIPS, 2022)

Learning Narrow One-Hidden-Layer ReLU Networks. - Sitan Chen; Zehao Dou; Surbhi Goel; Adam R. Klivans; Raghu Meka (COLT, 2023)

Transformers Learn Shortcuts to Automata. - Bingbin Liu; Jordan T. Ash; Surbhi Goel; Akshay Krishnamurthy; Cyril Zhang (ICLR, 2023)

Pareto Frontiers in Deep Feature Learning: Data, Compute, Width, and Luck. - Benjamin L. Edelman; Surbhi Goel; Sham M. Kakade; Eran Malach; Cyril Zhang (NeurIPS, 2023)

Adversarial Resilience in Sequential Prediction via Abstention. - Surbhi Goel; Steve Hanneke; Shay Moran; Abhishek Shetty (NeurIPS, 2023)

Exposing Attention Glitches with Flip-Flop Language Modeling. - Bingbin Liu; Jordan T. Ash; Surbhi Goel; Akshay Krishnamurthy; Cyril Zhang (NeurIPS, 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)

Complexity Matters: Feature Learning in the Presence of Spurious Correlations. - Guanwen Qiu; Da Kuang; Surbhi Goel (ICML, 2024)

Stochastic Bandits with ReLU Neural Networks. - Kan Xu; Hamsa Bastani; Surbhi Goel; Osbert Bastani (ICML, 2024)

The Evolution of Statistical Induction Heads: In-Context Learning Markov Chains. - Ezra Edelman; Nikolaos Tsilivis 002; Benjamin L. Edelman; Eran Malach; Surbhi Goel (NeurIPS, 2024)

Tolerant Algorithms for Learning with Arbitrary Covariate Shift. - Surbhi Goel; Abhishek Shetty; Konstantinos Stavropoulos; Arsen Vasilyan (NeurIPS, 2024)

Complexity Matters: Dynamics of Feature Learning in the Presence of Spurious Correlations. - Guanwen Qiu; Da Kuang; Surbhi Goel (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)

A Theory of Learning with Autoregressive Chain of Thought. - Nirmit Joshi; Gal Vardi; Adam Block; Surbhi Goel; Zhiyuan Li 005; Theodor Misiakiewicz; Nathan Srebro (COLT, 2025)

Progressive distillation induces an implicit curriculum. - Abhishek Panigrahi; Bingbin Liu; Sadhika Malladi; Andrej Risteski; Surbhi Goel (ICLR, 2025)

Conformal Language Model Reasoning with Coherent Factuality. - Maxon Rubin-Toles; Maya Gambhir; Keshav Ramji; Aaron Roth 001; Surbhi Goel (ICLR, 2025)

Logicbreaks: A Framework for Understanding Subversion of Rule-based Inference. - Anton Xue; Avishree Khare; Rajeev Alur; Surbhi Goel; Eric Wong 001 (ICLR, 2025)

Tractable Agreement Protocols. - Natalie Collina; Surbhi Goel; Varun Gupta 006; Aaron Roth 001 (STOC, 2025)

Testing Noise Assumptions of Learning Algorithms. - Surbhi Goel; Adam R. Klivans; Konstantinos Stavropoulos; Arsen Vasilyan (CoRR, 2025)

Collaborative Prediction: Tractable Information Aggregation via Agreement. - Natalie Collina; Ira Globus-Harris; Surbhi Goel; Varun Gupta 006; Aaron Roth 001; Mirah Shi (CoRR, 2025)

Probabilistic Stability Guarantees for Feature Attributions. - Helen Jin; Anton Xue; Weiqiu You; Surbhi Goel; Eric Wong 001 (CoRR, 2025)