Sanford, Clayton

10 publications

ICML 2025 Best of Both Worlds: Advantages of Hybrid Graph Sequence Models Ali Behrouz, Ali Parviz, Mahdi Karami, Clayton Sanford, Bryan Perozzi, Vahab Mirrokni

NeurIPS 2025 Depth-Width Tradeoffs for Transformers on Graph Tasks Gilad Yehudai, Clayton Sanford, Maya Bechler-Speicher, Orr Fischer, Ran Gilad-Bachrach, Amir Globerson

NeurIPS 2025 Fast Attention Mechanisms: A Tale of Parallelism Jingwen Liu, Hantao Yu, Clayton Sanford, Alexandr Andoni, Daniel Hsu

NeurIPS 2025 When Do Transformers Outperform Feedforward and Recurrent Networks? a Statistical Perspective Alireza Mousavi-Hosseini, Clayton Sanford, Denny Wu, Murat A Erdogdu

ICML 2024 Transformers, Parallel Computation, and Logarithmic Depth Clayton Sanford, Daniel Hsu, Matus Telgarsky

NeurIPS 2024 Understanding Transformer Reasoning Capabilities via Graph Algorithms Clayton Sanford, Bahare Fatemi, Ethan Hall, Anton Tsitsulin, Mehran Kazemi, Jonathan Halcrow, Bryan Perozzi, Vahab Mirrokni

NeurIPS 2023 Representational Strengths and Limitations of Transformers Clayton Sanford, Daniel J. Hsu, Matus J. Telgarsky

NeurIPS 2022 Learning Single-Index Models with Shallow Neural Networks Alberto Bietti, Joan Bruna, Clayton Sanford, Min Jae Song

NeurIPS 2022 On Scrambling Phenomena for Randomly Initialized Recurrent Networks Vaggos Chatziafratis, Ioannis Panageas, Clayton Sanford, Stelios Stavroulakis

NeurIPS 2021 Support Vector Machines and Linear Regression Coincide with Very High-Dimensional Features Navid Ardeshir, Clayton Sanford, Daniel J. Hsu