Research

Reliable Abstention under Adversarial Injections: Tight Lower Bounds and New Upper Bounds (α-β) Ezra Edelman, Surbhi Goel NeurIPS 2026

Narrowing the Collaboration Gap, Probably Mirah Shi, Marcel Hussing, Natalie Collina, Ira Globus-Harris, Aaron Roth, Surbhi Goel NeurIPS 2026

Learning When to Stop: Selective Imitation Learning Under Arbitrary Dynamics Shift (α-β) Surbhi Goel, Jonathan Pei, James Wang NeurIPS 2026

Testing Noise Assumptions of Learning Algorithms (α-β) Surbhi Goel, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan COLT 2026 Best paper award, Reliable ML Workshop, NeurIPS 2025

Model Agreement via Anchoring (α-β) Eric Eaton, Surbhi Goel, Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell COLT 2026

Emergent Alignment via Competition (α-β) Natalie Collina, Surbhi Goel, Aaron Roth, Emily Ryu, Mirah Shi ICML 2026

Less Data, Faster Training: Repeating Smaller Datasets Speeds up Learning via Sampling Biases Jingwen Liu, Ezra Edelman, Surbhi Goel, Bingbin Liu ICML 2026 Contributed talk, Workshop on Scientific Methods for Understanding Deep Learning, ICLR 2026

Is Code Better than Language for Algorithmic Reasoning? Terry Tong, Yu Feng, Surbhi Goel, Dan Roth ICML 2026

In Good GRACES: Principled Teacher Selection for Knowledge Distillation Abhishek Panigrahi, Bingbin Liu, Sadhika Malladi, Sham M. Kakade, Surbhi Goel ICLR 2026

Collaborative Prediction: Tractable Information Aggregation via Agreement (α-β) Natalie Collina, Ira Globus-Harris, Surbhi Goel, Varun Gupta, Aaron Roth, Mirah Shi SODA 2026 Spotlight presentation, EC 2025 Workshop on Human-AI CollaborationSpotlight presentation, 2025 TTIC Workshop on Incentives for Collaborative Learning and Data Sharing

Probabilistic Stability Guarantees for Feature Attributions Helen Jin, Anton Xue, Weiqiu You, Surbhi Goel, Eric Wong NeurIPS 2025

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

Tractable Agreement Protocols (α-β) Natalie Collina, Surbhi Goel, Varun Gupta, Aaron Roth STOC 2025

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

Progressive Distillation Induces an Implicit Curriculum Abhishek Panigrahi, Bingbin Liu, Sadhika Malladi, Andrej Risteski, Surbhi Goel Oral presentation, ICLR 2025

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

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

Tolerant Algorithms for Learning with Arbitrary Covariate Shift (α-β) Surbhi Goel, Abhishek Shetty, Konstantinos Stavropoulos, Arsen Vasilyan Spotlight presentation, NeurIPS 2024

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

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

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

Exposing Attention Glitches with Flip-Flop Language Modeling Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang Spotlight presentation, NeurIPS 2023

Learning Narrow One-Hidden-Layer ReLU Networks (α-β) 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 Notable top-5% paper, ICLR 2023

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

Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit (α-β) Boaz Barak, Benjamin L. Edelman, Surbhi Goel, Sham M. Kakade, Eran Malach, Cyril Zhang NeurIPS 2022

Inductive Biases and Variable Creation in Self-Attention Mechanisms (α-β) 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

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

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

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

Acceleration via Fractal Learning Rate Schedules (α-β) 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 Learning One-Layer ReLU Networks (α-β) Surbhi Goel, Adam R. Klivans, Pasin Manurangsi, Daniel Reichman ITCS 2021

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

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

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

Efficiently Learning Adversarially Robust Halfspaces with Noise Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro ICML 2020

Learning Mixtures of Graphs from Epidemic Cascades Jessica Hoffmann, Soumya Basu, Surbhi Goel, Constantine Caramanis ICML 2020

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

Learning Ising and Potts Models with Latent Variables Surbhi Goel AISTATS 2020

Time/Accuracy Trade-offs for Learning a ReLU with respect to Gaussian Marginals (α-β) Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans Spotlight presentation, NeurIPS 2019

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

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

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

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

Reliably Learning the ReLU in Polynomial Time (α-β) Surbhi Goel, Varun Kanade, Adam R. Klivans, Justin Thaler COLT 2017 Oral presentation, Optimization for Machine Learning (OPT-ML) Workshop, NeurIPS 2016