Peizhong Ju

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All published works

Action	Title	Year	Authors
+ PDF Chat	BeST -- A Novel Source Selection Metric for Transfer Learning	2025	Ashutosh Soni Peizhong Ju Atilla Eryılmaz Ness B. Shroff
+ PDF Chat	PSMGD: Periodic Stochastic Multi-Gradient Descent for Fast Multi-Objective Optimization	2024	Minghua Xu Peizhong Ju Jia Liu Haibo Yang
+ PDF Chat	How to Find the Exact Pareto Front for Multi-Objective MDPs?	2024	Yining Li Peizhong Ju Ness B. Shroff
+ PDF Chat	Theory on Score-Mismatched Diffusion Models and Zero-Shot Conditional Samplers	2024	Yuchen Liang Peizhong Ju Yingbin Liang Ness B. Shroff
+ PDF Chat	Can We Theoretically Quantify the Impacts of Local Updates on the Generalization Performance of Federated Learning?	2024	Peizhong Ju Haibo Yang Jia Liu Yingbin Liang Ness B. Shroff
+ PDF Chat	Non-asymptotic Convergence of Discrete-time Diffusion Models: New Approach and Improved Rate	2024	Yuchen Liang Peizhong Ju Yingbin Liang Ness B. Shroff
+	Theory on Forgetting and Generalization of Continual Learning	2023	Sen Lin Peizhong Ju Yingbin Liang Ness B. Shroff
+	Theoretical Characterization of the Generalization Performance of Overfitted Meta-Learning	2023	Peizhong Ju Yingbin Liang Ness B. Shroff
+	Achieving Fairness in Multi-Agent Markov Decision Processes Using Reinforcement Learning	2023	Peizhong Ju Arnob Ghosh Ness B. Shroff
+	Generalization Performance of Transfer Learning: Overparameterized and Underparameterized Regimes	2023	Peizhong Ju Sen Lin Mark S. Squillante Yingbin Liang Ness B. Shroff
+	Achieving Sample and Computational Efficient Reinforcement Learning by Action Space Reduction via Grouping	2023	Yining Li Peizhong Ju Ness B. Shroff
+	On the Generalization Power of the Overfitted Three-Layer Neural Tangent Kernel Model	2022	Peizhong Ju Xiaojun Lin Ness B. Shroff
+	On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models	2021	Peizhong Ju Xiaojun Lin Ness B. Shroff
+	Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree	2020	Peizhong Ju Xiaojun Lin Jia Liu

Common Coauthors

Coauthor	Papers Together
Ness B. Shroff	12
Yingbin Liang	5
Xiaojun Lin	2
Jia Liu	2
Sen Lin	2
Yining Li	2
Yuchen Liang	2
Minghua Xu	1
Ashutosh Soni	1
Mark S. Squillante	1
Jia Liu	1
Haibo Yang	1
Arnob Ghosh	1
Yingbin Liang	1
Atilla Eryılmaz	1
Xiaojun Lin	1
Haibo Yang	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+ PDF Chat	Two Models of Double Descent for Weak Features	2020	Mikhail A. Belkin Daniel Hsu Ji Xu	2
+	Understanding deep learning requires rethinking generalization	2016	Chiyuan Zhang Samy Bengio Moritz Hardt Benjamin Recht Oriol Vinyals	2
+	The generalization error of random features regression: Precise asymptotics and double descent curve	2019	Mei Song Andrea Montanari	2
+	High-dimensional dynamics of generalization error in neural networks	2020	Madhu Advani Andrew Saxe Haim Sompolinsky	2
+	On the stability of inverse problems	1943	Andreĭ Nikolaevich Tikhonov	2
+ PDF Chat	Harmless interpolation of noisy data in regression	2019	V. Sai Muthukumar Kailas Vodrahalli Anant Sahai	2
+	Benign overfitting in linear regression	2020	Peter L. Bartlett Philip M. Long Gábor Lugosi Alexander Tsigler	2
+ PDF Chat	Simultaneous analysis of Lasso and Dantzig selector	2009	Peter J. Bickel Ya’acov Ritov Alexandre B. Tsybakov	1
+	Special Functions and the Theory of Group Representations	1968	N. I︠a︡. Vilenkin	1
+	Regression Shrinkage and Selection Via the Lasso	1996	Robert Tibshirani	1
+	On Model Selection Consistency of Lasso	2006	Peng Zhao Bin Yu	1
+	Concise Formulas for the Area and Volume of a Hyperspherical Cap	2010	S. Li	1
+	High-dimensional dynamics of generalization error in neural networks	2017	Madhu Advani Andrew Saxe	1
+	Neural Tangent Kernel: Convergence and Generalization in Neural Networks	2018	Arthur Paul Jacot Franck Gabriel Clément Hongler	1
+	Reconciling modern machine learning and the bias-variance trade-off	2018	Mikhail Belkin Daniel Hsu Siyuan Ma Soumik Mandal	1
+	Uniform Laws of Large Numbers	1996	Luc Devroye László Györfi Gábor Lugosi	1
+	Decoupling Gating from Linearity	2019	Jonathan Fiat Eran Malach Shai Shalev‐Shwartz	1
+	Surprises in high-dimensional ridgeless least squares interpolation	2022	Trevor Hastie Andrea Montanari Saharon Rosset Ryan J. Tibshirani	1
+	Harmless interpolation of noisy data in regression	2019	V. Sai Muthukumar Kailas Vodrahalli Vignesh Subramanian Anant Sahai	1
+	Linearized two-layers neural networks in high dimension	2019	Behrooz Ghorbani Mei Song Theodor Misiakiewicz Andrea Montanari	1
+	The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies	2019	Ronen Basri David Jacobs Yoni Kasten Shira Kritchman	1
+	Understanding overfitting peaks in generalization error: Analytical risk curves for $l_2$ and $l_1$ penalized interpolation	2019	Partha P. Mitra	1
+	Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data	2018	Yuanzhi Li Yingyu Liang	1
+ PDF Chat	Adaptive estimation of a quadratic functional by model selection	2000	Béatrice Laurent Pascal Massart	1
+	Benefits of depth in neural networks	2016	Matus Telgarsky	1
+ PDF Chat	A mean field view of the landscape of two-layer neural networks	2018	Mei Song Andrea Montanari Phan-Minh Nguyen	1
+	Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks	2019	Sanjeev Arora Simon S. Du Wei Hu Zhiyuan Li Ruosong Wang	1
+	Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks	2019	Ziwei Ji Matus Telgarsky	1
+	Neural tangent kernels, transportation mappings, and universal approximation	2019	Ziwei Ji Matus Telgarsky Ruicheng Xian	1
+	Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree	2020	Peizhong Ju Xiaojun Lin Jia Liu	1
+	Double Trouble in Double Descent : Bias and Variance(s) in the Lazy Regime	2020	Stéphane d’Ascoli Maria Refinetti Giulio Biroli Florent Krząkała	1
+	Integral and series representations of the dirac delta function	2008	Yutian Li R. Wong	1
+ PDF Chat	A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent*	2021	Zhenyu Liao Romain Couillet Michael W. Mahoney	1
+ PDF Chat	Lasso-type recovery of sparse representations for high-dimensional data	2009	Nicolai Meinshausen Bin Yu	1
+	The Dynamics of Gradient Descent for Overparametrized Neural Networks	2021	Siddhartha Satpathi R. Srikant	1
+	Surprises in High-Dimensional Ridgeless Least Squares Interpolation	2019	Trevor Hastie Andrea Montanari Saharon Rosset Ryan J. Tibshirani	1
+	Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers	2018	Zeyuan Allen-Zhu Yuanzhi Li Yingyu Liang	1
+	Gradient Descent Provably Optimizes Over-parameterized Neural Networks	2018	Simon S. Du Xiyu Zhai Barnabás Póczos Aarti Singh	1
+	To understand deep learning we need to understand kernel learning	2018	Mikhail Belkin Siyuan Ma Soumik Mandal	1
+	Stochastic Gradient/Mirror Descent: Minimax Optimality and Implicit Regularization	2018	Navid Azizan Babak Hassibi	1
+	A large-deviation inequality for vector-valued martingales	2003	Thomas P. Hayes	1
+	Pattern Recognition and Machine Learning	2007	Christopher Bishop	1
+	The incomplete beta function ? a historical profile	1981	Jacques Dutka	1
+	Ridge Regression: Biased Estimation for Nonorthogonal Problems	2000	Arthur E. Hoerl Robert W. Kennard	1
+	Extension of Euler's beta function	1997	M. A. Chaudhry Asghar Qadir M. Mujahid Rafique Syed M. Zubair	1
+	Uncertainty principles and ideal atomic decomposition	2001	David L. Donoho Xiaoming Huo	1