Publications

Note: (α-β) indicates alphabetical ordering

Preprints

• Personalization Aids Pluralistic Alignment Under Competition
  (α-β) Natalie Collina, Surbhi Goel, Aaron Roth, and Mirah Shi

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

• Why Do Transformers Fail to Forecast Time Series In-Context?
  Yufa Zhou, Yixiao Wang, Surbhi Goel, Anru Zhang
  Spotlight presentation, What Can(‘t) Transformers Do? Workshop, NeurIPS 2025

• Reliable Abstention under Adversarial Injections: Tight Lower Bounds and New Upper Bounds
  Ezra Edelman, Surbhi Goel
  

• Weight Clipping for Robust Conformal Inference under Unbounded Covariate Shifts
  James Wang, Surbhi Goel

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

Conference Papers

• 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 Collaboration
  Spotlight 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 Panigrahy, 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

Reports

• Encoding Structural Symmetry is Key for Length Generalization in Arithmetic Tasks
  Mahdi Sabbaghi, George J. Pappas, Hamed Hassani, Surbhi Goel

• Recovering the Lowest Layer of Deep Networks with High Threshold Activations
  (α-β) Surbhi Goel, Rina Panigrahy

• Quantifying Perceptual Distortion of Adversarial Examples
  Matthew Jordan, Naren Manoj, Surbhi Goel, Alexandros Dimakis

• Improved Learning of One-hidden-layer Convolutional Neural Networks with Overlaps
  (α-β) Simon Du, Surbhi Goel