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ORCIDClayton Sanford
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- affiliation: Columbia University, NY, USA
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2020 – today
- 2024
[c9]Clayton Sanford, Daniel Hsu, Matus Telgarsky:
Transformers, parallel computation, and logarithmic depth. ICML 2024- [i13]Clayton Sanford, Daniel Hsu, Matus Telgarsky:
Transformers, parallel computation, and logarithmic depth. CoRR abs/2402.09268 (2024) - [i12]Clayton Sanford, Bahare Fatemi, Ethan Hall, Anton Tsitsulin, Seyed Mehran Kazemi, Jonathan Halcrow, Bryan Perozzi, Vahab Mirrokni:
Understanding Transformer Reasoning Capabilities via Graph Algorithms. CoRR abs/2405.18512 (2024) - [i11]Clayton Sanford, Daniel Hsu, Matus Telgarsky:
One-layer transformers fail to solve the induction heads task. CoRR abs/2408.14332 (2024) - [i10]Ali Behrouz, Ali Parviz, Mahdi Karami, Clayton Sanford, Bryan Perozzi, Vahab Mirrokni:
Best of Both Worlds: Advantages of Hybrid Graph Sequence Models. CoRR abs/2411.15671 (2024) - 2023
- [c8]Navid Ardeshir, Daniel J. Hsu, Clayton Hendrick Sanford:
Intrinsic dimensionality and generalization properties of the R-norm inductive bias. COLT 2023: 3264-3303 - [c7]Clayton Sanford, Daniel J. Hsu, Matus Telgarsky:
Representational Strengths and Limitations of Transformers. NeurIPS 2023 - [i9]Clayton Sanford, Daniel Hsu, Matus Telgarsky:
Representational Strengths and Limitations of Transformers. CoRR abs/2306.02896 (2023) - 2022
- [c6]Clayton Hendrick Sanford, Vaggos Chatziafratis:
Expressivity of Neural Networks via Chaotic Itineraries beyond Sharkovsky's Theorem. AISTATS 2022: 9505-9549 - [c5]Daniel J. Hsu, Clayton Hendrick Sanford, Rocco A. Servedio, Emmanouil-Vasileios Vlatakis-Gkaragkounis
:
Near-Optimal Statistical Query Lower Bounds for Agnostically Learning Intersections of Halfspaces with Gaussian Marginals. COLT 2022: 283-312 - [c4]Alberto Bietti, Joan Bruna, Clayton Sanford, Min Jae Song:
Learning single-index models with shallow neural networks. NeurIPS 2022 - [c3]Vaggos Chatziafratis, Ioannis Panageas, Clayton Sanford, Stelios Stavroulakis:
On Scrambling Phenomena for Randomly Initialized Recurrent Networks. NeurIPS 2022 - [i8]Daniel Hsu, Clayton Sanford, Rocco A. Servedio, Emmanouil V. Vlatakis-Gkaragkounis:
Near-Optimal Statistical Query Lower Bounds for Agnostically Learning Intersections of Halfspaces with Gaussian Marginals. CoRR abs/2202.05096 (2022) - [i7]Clayton Sanford, Navid Ardeshir, Daniel Hsu:
Intrinsic dimensionality and generalization properties of the R-norm inductive bias. CoRR abs/2206.05317 (2022) - [i6]Vaggos Chatziafratis, Ioannis Panageas, Clayton Sanford, Stelios Andrew Stavroulakis:
On Scrambling Phenomena for Randomly Initialized Recurrent Networks. CoRR abs/2210.05212 (2022) - [i5]Alberto Bietti, Joan Bruna, Clayton Sanford, Min Jae Song:
Learning Single-Index Models with Shallow Neural Networks. CoRR abs/2210.15651 (2022) - 2021
- [c2]Daniel Hsu, Clayton Sanford, Rocco A. Servedio, Emmanouil V. Vlatakis-Gkaragkounis:
On the Approximation Power of Two-Layer Networks of Random ReLUs. COLT 2021: 2423-2461 - [c1]Navid Ardeshir, Clayton Sanford, Daniel J. Hsu:
Support vector machines and linear regression coincide with very high-dimensional features. NeurIPS 2021: 4907-4918 - [i4]Daniel Hsu, Clayton Sanford, Rocco A. Servedio, Emmanouil V. Vlatakis-Gkaragkounis:
On the Approximation Power of Two-Layer Networks of Random ReLUs. CoRR abs/2102.02336 (2021) - [i3]Navid Ardeshir, Clayton Sanford, Daniel Hsu:
Support vector machines and linear regression coincide with very high-dimensional features. CoRR abs/2105.14084 (2021) - [i2]Clayton Sanford, Vaggos Chatziafratis:
Expressivity of Neural Networks via Chaotic Itineraries beyond Sharkovsky's Theorem. CoRR abs/2110.10295 (2021)
2010 – 2019
- 2018
- [i1]Clayton Sanford, Cyrus Cousins, Eli Upfal:
Uniform Convergence Bounds for Codec Selection. CoRR abs/1812.07568 (2018) - 2017
- [j1]Kamil Jan Cygan, Clayton Hendrick Sanford, William G. Fairbrother:
Spliceman2: a computational web server that predicts defects in pre-mRNA splicing. Bioinform. 33(18): 2943-2945 (2017)
Coauthor Index
aka: Daniel J. Hsu
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