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Kaifeng Lyu
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2020 – today
- 2024
- [c16]Xinran Gu, Kaifeng Lyu, Sanjeev Arora, Jingzhao Zhang, Longbo Huang:
A Quadratic Synchronization Rule for Distributed Deep Learning. ICLR 2024 - [c15]Kaifeng Lyu, Jikai Jin, Zhiyuan Li, Simon Shaolei Du, Jason D. Lee, Wei Hu:
Dichotomy of Early and Late Phase Implicit Biases Can Provably Induce Grokking. ICLR 2024 - [c14]Runzhe Wang, Sadhika Malladi, Tianhao Wang, Kaifeng Lyu, Zhiyuan Li:
The Marginal Value of Momentum for Small Learning Rate SGD. ICLR 2024 - [c13]Yongchao Zhou, Kaifeng Lyu, Ankit Singh Rawat, Aditya Krishna Menon, Afshin Rostamizadeh, Sanjiv Kumar, Jean-François Kagy, Rishabh Agarwal:
DistillSpec: Improving Speculative Decoding via Knowledge Distillation. ICLR 2024 - [i21]Abhishek Panigrahi, Nikunj Saunshi, Kaifeng Lyu, Sobhan Miryoosefi, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar:
Efficient Stagewise Pretraining via Progressive Subnetworks. CoRR abs/2402.05913 (2024) - [i20]Kaiyue Wen, Xingyu Dang, Kaifeng Lyu:
RNNs are not Transformers (Yet): The Key Bottleneck on In-context Retrieval. CoRR abs/2402.18510 (2024) - [i19]Kaifeng Lyu, Haoyu Zhao, Xinran Gu, Dingli Yu, Anirudh Goyal, Sanjeev Arora:
Keeping LLMs Aligned After Fine-tuning: The Crucial Role of Prompt Templates. CoRR abs/2402.18540 (2024) - [i18]Xiangyu Qi, Ashwinee Panda, Kaifeng Lyu, Xiao Ma, Subhrajit Roy, Ahmad Beirami, Prateek Mittal, Peter Henderson:
Safety Alignment Should Be Made More Than Just a Few Tokens Deep. CoRR abs/2406.05946 (2024) - [i17]Vedant Shah, Dingli Yu, Kaifeng Lyu, Simon Park, Nan Rosemary Ke, Michael Mozer, Yoshua Bengio, Sanjeev Arora, Anirudh Goyal:
AI-Assisted Generation of Difficult Math Questions. CoRR abs/2407.21009 (2024) - 2023
- [c12]Xinran Gu, Kaifeng Lyu, Longbo Huang, Sanjeev Arora:
Why (and When) does Local SGD Generalize Better than SGD? ICLR 2023 - [c11]Jikai Jin, Zhiyuan Li, Kaifeng Lyu, Simon Shaolei Du, Jason D. Lee:
Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing. ICML 2023: 15200-15238 - [i16]Jikai Jin, Zhiyuan Li, Kaifeng Lyu, Simon S. Du, Jason D. Lee:
Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing. CoRR abs/2301.11500 (2023) - [i15]Xinran Gu, Kaifeng Lyu, Longbo Huang, Sanjeev Arora:
Why (and When) does Local SGD Generalize Better than SGD? CoRR abs/2303.01215 (2023) - [i14]Runzhe Wang, Sadhika Malladi, Tianhao Wang, Kaifeng Lyu, Zhiyuan Li:
The Marginal Value of Momentum for Small Learning Rate SGD. CoRR abs/2307.15196 (2023) - [i13]Yongchao Zhou, Kaifeng Lyu, Ankit Singh Rawat, Aditya Krishna Menon, Afshin Rostamizadeh, Sanjiv Kumar, Jean-François Kagy, Rishabh Agarwal:
DistillSpec: Improving Speculative Decoding via Knowledge Distillation. CoRR abs/2310.08461 (2023) - [i12]Xinran Gu, Kaifeng Lyu, Sanjeev Arora, Jingzhao Zhang, Longbo Huang:
A Quadratic Synchronization Rule for Distributed Deep Learning. CoRR abs/2310.14423 (2023) - [i11]Kaifeng Lyu, Jikai Jin, Zhiyuan Li, Simon S. Du, Jason D. Lee, Wei Hu:
Dichotomy of Early and Late Phase Implicit Biases Can Provably Induce Grokking. CoRR abs/2311.18817 (2023) - 2022
- [c10]Arushi Gupta, Nikunj Saunshi, Dingli Yu, Kaifeng Lyu, Sanjeev Arora:
New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound. NeurIPS 2022 - [c9]Kaifeng Lyu, Zhiyuan Li, Sanjeev Arora:
Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction. NeurIPS 2022 - [c8]Sadhika Malladi, Kaifeng Lyu, Abhishek Panigrahi, Sanjeev Arora:
On the SDEs and Scaling Rules for Adaptive Gradient Algorithms. NeurIPS 2022 - [i10]Sadhika Malladi, Kaifeng Lyu, Abhishek Panigrahi, Sanjeev Arora:
On the SDEs and Scaling Rules for Adaptive Gradient Algorithms. CoRR abs/2205.10287 (2022) - [i9]Kaifeng Lyu, Zhiyuan Li, Sanjeev Arora:
Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction. CoRR abs/2206.07085 (2022) - [i8]Arushi Gupta, Nikunj Saunshi, Dingli Yu, Kaifeng Lyu, Sanjeev Arora:
New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound. CoRR abs/2211.02912 (2022) - 2021
- [c7]Zhiyuan Li, Yuping Luo, Kaifeng Lyu:
Towards Resolving the Implicit Bias of Gradient Descent for Matrix Factorization: Greedy Low-Rank Learning. ICLR 2021 - [c6]Kaifeng Lyu, Zhiyuan Li, Runzhe Wang, Sanjeev Arora:
Gradient Descent on Two-layer Nets: Margin Maximization and Simplicity Bias. NeurIPS 2021: 12978-12991 - [i7]Kaifeng Lyu, Zhiyuan Li, Runzhe Wang, Sanjeev Arora:
Gradient Descent on Two-layer Nets: Margin Maximization and Simplicity Bias. CoRR abs/2110.13905 (2021) - 2020
- [c5]Kaifeng Lyu, Jian Li:
Gradient Descent Maximizes the Margin of Homogeneous Neural Networks. ICLR 2020 - [c4]Zhiyuan Li, Kaifeng Lyu, Sanjeev Arora:
Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate. NeurIPS 2020 - [i6]Zhiyuan Li, Kaifeng Lyu, Sanjeev Arora:
Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate. CoRR abs/2010.02916 (2020) - [i5]Zhiyuan Li, Yuping Luo, Kaifeng Lyu:
Towards Resolving the Implicit Bias of Gradient Descent for Matrix Factorization: Greedy Low-Rank Learning. CoRR abs/2012.09839 (2020)
2010 – 2019
- 2019
- [c3]Sanjeev Arora, Zhiyuan Li, Kaifeng Lyu:
Theoretical Analysis of Auto Rate-Tuning by Batch Normalization. ICLR (Poster) 2019 - [c2]Lijie Chen, Shafi Goldwasser, Kaifeng Lyu, Guy N. Rothblum, Aviad Rubinstein:
Fine-grained Complexity Meets IP = PSPACE. SODA 2019: 1-20 - [i4]Kaifeng Lyu, Jian Li:
Gradient Descent Maximizes the Margin of Homogeneous Neural Networks. CoRR abs/1906.05890 (2019) - 2018
- [c1]Ran Duan, Kaifeng Lyu, Yuanhang Xie:
Single-Source Bottleneck Path Algorithm Faster than Sorting for Sparse Graphs. ICALP 2018: 43:1-43:14 - [i3]Lijie Chen, Shafi Goldwasser, Kaifeng Lyu, Guy N. Rothblum, Aviad Rubinstein:
Fine-grained Complexity Meets IP = PSPACE. CoRR abs/1805.02351 (2018) - [i2]Ran Duan, Kaifeng Lyu, Hongxun Wu, Yuanhang Xie:
Single-Source Bottleneck Path Algorithm Faster than Sorting for Sparse Graphs. CoRR abs/1808.10658 (2018) - [i1]Sanjeev Arora, Zhiyuan Li, Kaifeng Lyu:
Theoretical Analysis of Auto Rate-Tuning by Batch Normalization. CoRR abs/1812.03981 (2018)
Coauthor Index
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last updated on 2024-10-07 01:27 CEST by the dblp team
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