Lechao Xiao - RankMe

41 papers A* 17Journal 23Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks. ICML Shikai Qiu, Lechao Xiao, Andrew Gordon Wilson, Jeffrey Pennington, Atish Agarwala
2025	J	`jnl`	Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks. CoRR Shikai Qiu, Lechao Xiao, Andrew Gordon Wilson, Jeffrey Pennington, Atish Agarwala
2024	A*	`conf`	4+3 Phases of Compute-Optimal Neural Scaling Laws. NeurIPS Elliot Paquette, Courtney Paquette, Lechao Xiao, Jeffrey Pennington
2024	J	`jnl`	4+3 Phases of Compute-Optimal Neural Scaling Laws. CoRR Elliot Paquette, Courtney Paquette, Lechao Xiao, Jeffrey Pennington
2024	J	`jnl`	Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. Trans. Mach. Learn. Res. Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron T. Parisi, Abhishek Kumar, Alexander A. Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin Fathy Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel
2024	J	`jnl`	Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling. CoRR Lechao Xiao
2024	A*	`conf`	Scaling Exponents Across Parameterizations and Optimizers. ICML Katie E. Everett, Lechao Xiao, Mitchell Wortsman, Alexander A. Alemi, Roman Novak, Peter J. Liu, Izzeddin Gur, Jascha Sohl-Dickstein, Leslie Pack Kaelbling, Jaehoon Lee, Jeffrey Pennington
2024	J	`jnl`	Scaling Exponents Across Parameterizations and Optimizers. CoRR Katie Everett, Lechao Xiao, Mitchell Wortsman, Alexander A. Alemi, Roman Novak, Peter J. Liu, Izzeddin Gur, Jascha Sohl-Dickstein, Leslie Pack Kaelbling, Jaehoon Lee, Jeffrey Pennington
2024	A*	`conf`	Small-scale proxies for large-scale Transformer training instabilities. ICLR Mitchell Wortsman, Peter J. Liu, Lechao Xiao, Katie E. Everett, Alexander A. Alemi, Ben Adlam, John D. Co-Reyes, Izzeddin Gur, Abhishek Kumar, Roman Novak, Jeffrey Pennington, Jascha Sohl-Dickstein, Kelvin Xu, Jaehoon Lee, Justin Gilmer, Simon Kornblith
2024	J	`jnl`	Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability. CoRR Jiri Hron, Laura Culp, Gamaleldin F. Elsayed, Rosanne Liu, Ben Adlam, Maxwell L. Bileschi, Bernd Bohnet, JD Co-Reyes, Noah Fiedel, C. Daniel Freeman, Izzeddin Gur, Kathleen Kenealy, Jaehoon Lee, Peter J. Liu, Gaurav Mishra, Igor Mordatch, Azade Nova, Roman Novak, Aaron Parisi, Jeffrey Pennington, Alex Rizkowsky, Isabelle Simpson, Hanie Sedghi, Jascha Sohl-Dickstein, Kevin Swersky, Sharad Vikram, Tris Warkentin, Lechao Xiao, Kelvin Xu, Jasper Snoek, Simon Kornblith
2023	J	`jnl`	Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. CoRR Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron Parisi, Abhishek Kumar, Alex Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin F. Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel
2023	J	`jnl`	Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5? CoRR C. Daniel Freeman, Laura Culp, Aaron Parisi, Maxwell L. Bileschi, Gamaleldin F. Elsayed, Alex Rizkowsky, Isabelle Simpson, Alex Alemi, Azade Nova, Ben Adlam, Bernd Bohnet, Gaurav Mishra, Hanie Sedghi, Igor Mordatch, Izzeddin Gur, Jaehoon Lee, John D. Co-Reyes, Jeffrey Pennington, Kelvin Xu, Kevin Swersky, Kshiteej Mahajan, Lechao Xiao, Rosanne Liu, Simon Kornblith, Noah Constant, Peter J. Liu, Roman Novak, Yundi Qian, Noah Fiedel, Jascha Sohl-Dickstein
2023	J	`jnl`	Small-scale proxies for large-scale Transformer training instabilities. CoRR Mitchell Wortsman, Peter J. Liu, Lechao Xiao, Katie Everett, Alex Alemi, Ben Adlam, John D. Co-Reyes, Izzeddin Gur, Abhishek Kumar, Roman Novak, Jeffrey Pennington, Jascha Sohl-Dickstein, Kelvin Xu, Jaehoon Lee, Justin Gilmer, Simon Kornblith
2022	A*	`conf`	Eigenspace Restructuring: A Principle of Space and Frequency in Neural Networks. COLT Lechao Xiao
2022	A*	`conf`	Fast Neural Kernel Embeddings for General Activations. NeurIPS Insu Han, Amir Zandieh, Jaehoon Lee, Roman Novak, Lechao Xiao, Amin Karbasi
2022	J	`jnl`	Fast Neural Kernel Embeddings for General Activations. CoRR Insu Han, Amir Zandieh, Jaehoon Lee, Roman Novak, Lechao Xiao, Amin Karbasi
2022	J	`jnl`	Precise Learning Curves and Higher-Order Scaling Limits for Dot Product Kernel Regression. CoRR Lechao Xiao, Jeffrey Pennington
2022	A*	`conf`	Precise Learning Curves and Higher-Order Scalings for Dot-product Kernel Regression. NeurIPS Lechao Xiao, Hong Hu, Theodor Misiakiewicz, Yue Lu, Jeffrey Pennington
2022	A*	`conf`	Synergy and Symmetry in Deep Learning: Interactions between the Data, Model, and Inference Algorithm. ICML Lechao Xiao, Jeffrey Pennington
2022	J	`jnl`	Synergy and Symmetry in Deep Learning: Interactions between the Data, Model, and Inference Algorithm. CoRR Lechao Xiao, Jeffrey Pennington
2021	A*	`conf`	Dataset Distillation with Infinitely Wide Convolutional Networks. NeurIPS Timothy Nguyen, Roman Novak, Lechao Xiao, Jaehoon Lee
2021	J	`jnl`	Dataset Distillation with Infinitely Wide Convolutional Networks. CoRR Timothy Nguyen, Roman Novak, Lechao Xiao, Jaehoon Lee
2021	J	`jnl`	Eigenspace Restructuring: a Principle of Space and Frequency in Neural Networks. CoRR Lechao Xiao
2021	A*	`conf`	Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit. ICLR Ben Adlam, Jaehoon Lee, Lechao Xiao, Jeffrey Pennington, Jasper Snoek
2020	A*	`conf`	Disentangling Trainability and Generalization in Deep Neural Networks. ICML Lechao Xiao, Jeffrey Pennington, Samuel Stern Schoenholz
2020	J	`jnl`	Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit. CoRR Ben Adlam, Jaehoon Lee, Lechao Xiao, Jeffrey Pennington, Jasper Snoek
2020	A*	`conf`	Finite Versus Infinite Neural Networks: an Empirical Study. NeurIPS Jaehoon Lee, Samuel S. Schoenholz, Jeffrey Pennington, Ben Adlam, Lechao Xiao, Roman Novak, Jascha Sohl-Dickstein
2020	J	`jnl`	Finite Versus Infinite Neural Networks: an Empirical Study. CoRR Jaehoon Lee, Samuel S. Schoenholz, Jeffrey Pennington, Ben Adlam, Lechao Xiao, Roman Novak, Jascha Sohl-Dickstein
2020	A*	`conf`	Neural Tangents: Fast and Easy Infinite Neural Networks in Python. ICLR Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, Samuel S. Schoenholz
2020	A*	`conf`	Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks. ICLR Wei Hu, Lechao Xiao, Jeffrey Pennington
2020	J	`jnl`	Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks. CoRR Wei Hu, Lechao Xiao, Jeffrey Pennington
2020	A*	`conf`	The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks. NeurIPS Wei Hu, Lechao Xiao, Ben Adlam, Jeffrey Pennington
2020	J	`jnl`	The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks. CoRR Wei Hu, Lechao Xiao, Ben Adlam, Jeffrey Pennington
2019	—	`conf`	Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes. ICLR (Poster) Roman Novak, Lechao Xiao, Yasaman Bahri, Jaehoon Lee, Greg Yang, Jiri Hron, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein
2019	J	`jnl`	Disentangling trainability and generalization in deep learning. CoRR Lechao Xiao, Jeffrey Pennington, Samuel S. Schoenholz
2019	J	`jnl`	Neural Tangents: Fast and Easy Infinite Neural Networks in Python. CoRR Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, Samuel S. Schoenholz
2019	A*	`conf`	Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent. NeurIPS Jaehoon Lee, Lechao Xiao, Samuel S. Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, Jeffrey Pennington
2019	J	`jnl`	Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent. CoRR Jaehoon Lee, Lechao Xiao, Samuel S. Schoenholz, Yasaman Bahri, Jascha Sohl-Dickstein, Jeffrey Pennington
2018	J	`jnl`	Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes. CoRR Roman Novak, Lechao Xiao, Jaehoon Lee, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein
2018	A*	`conf`	Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10, 000-Layer Vanilla Convolutional Neural Networks. ICML Lechao Xiao, Yasaman Bahri, Jascha Sohl-Dickstein, Samuel S. Schoenholz, Jeffrey Pennington
2018	J	`jnl`	Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10, 000-Layer Vanilla Convolutional Neural Networks. CoRR Lechao Xiao, Yasaman Bahri, Jascha Sohl-Dickstein, Samuel S. Schoenholz, Jeffrey Pennington