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Zixiang Chen
Zixiang Chen
Graduate Student of CS, UCLA
Bestätigte E-Mail-Adresse bei cs.ucla.edu - Startseite
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Zitiert von
Zitiert von
Jahr
Self-play fine-tuning converts weak language models to strong language models
Z Chen, Y Deng, H Yuan, K Ji, Q Gu
arXiv preprint arXiv:2401.01335, 2024
1882024
How much over-parameterization is sufficient to learn deep ReLU networks?
Z Chen, Y Cao, D Zou, Q Gu
arXiv preprint arXiv:1911.12360, 2019
1482019
Towards understanding the mixture-of-experts layer in deep learning
Z Chen, Y Deng, Y Wu, Q Gu, Y Li
Advances in neural information processing systems 35, 23049-23062, 2022
134*2022
Benign overfitting in two-layer convolutional neural networks
Y Cao, Z Chen, M Belkin, Q Gu
Advances in neural information processing systems 35, 25237-25250, 2022
1212022
A generalized neural tangent kernel analysis for two-layer neural networks
Z Chen, Y Cao, Q Gu, T Zhang
Advances in Neural Information Processing Systems 33, 13363-13373, 2020
102*2020
Almost optimal algorithms for two-player zero-sum linear mixture markov games
Z Chen, D Zhou, Q Gu
International Conference on Algorithmic Learning Theory, 227-261, 2022
64*2022
Rephrase and respond: Let large language models ask better questions for themselves
Y Deng, W Zhang, Z Chen, Q Gu
arXiv preprint arXiv:2311.04205, 2023
592023
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression?
J Wu, D Zou, Z Chen, V Braverman, Q Gu, PL Bartlett
arXiv preprint arXiv:2310.08391, 2023
592023
Stein neural sampler
T Hu, Z Chen, H Sun, J Bai, M Ye, G Cheng
arXiv preprint arXiv:1810.03545, 2018
542018
Benign overfitting in two-layer ReLU convolutional neural networks
Y Kou, Z Chen, Y Chen, Q Gu
International Conference on Machine Learning, 17615-17659, 2023
402023
A general framework for sample-efficient function approximation in reinforcement learning
Z Chen, CJ Li, A Yuan, Q Gu, MI Jordan
arXiv preprint arXiv:2209.15634, 2022
382022
Self-training converts weak learners to strong learners in mixture models
S Frei, D Zou, Z Chen, Q Gu
International Conference on Artificial Intelligence and Statistics, 8003-8021, 2022
222022
Why does sharpness-aware minimization generalize better than SGD?
Z Chen, J Zhang, Y Kou, X Chen, CJ Hsieh, Q Gu
Advances in neural information processing systems 36, 2024
162024
Implicit bias of gradient descent for two-layer reLU and leaky reLU networks on nearly-orthogonal data
Y Kou, Z Chen, Q Gu
Advances in Neural Information Processing Systems 36, 2024
132024
Understanding transferable representation learning and zero-shot transfer in clip
Z Chen, Y Deng, Y Li, Q Gu
arXiv preprint arXiv:2310.00927, 2023
122023
Self-play fine-tuning of diffusion models for text-to-image generation
H Yuan, Z Chen, K Ji, Q Gu
arXiv preprint arXiv:2402.10210, 2024
92024
How Does Semi-supervised learing with Pseudo-labelers Work? A Case Study
Y Kou, Z Chen, Y Cao, Q Gu
International Conference on Learning Representations, 2023
92023
Learning high-dimensional single-neuron relu networks with finite samples
J Wu, D Zou, Z Chen, V Braverman, Q Gu, SM Kakade
arXiv preprint arXiv:2303.02255, 2023
8*2023
Matching the Statistical Query Lower Bound for k-sparse Parity Problems with Stochastic Gradient Descent
Y Kou, Z Chen, Q Gu, SM Kakade
arXiv preprint arXiv:2404.12376, 2024
52024
Fast Sampling via De-randomization for Discrete Diffusion Models
Z Chen, H Yuan, Y Li, Y Kou, J Zhang, Q Gu
arXiv preprint arXiv:2312.09193, 2023
52023
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