28 citations · 120 across the 12 of their papers we have counts for
12 papers
Probing Heavy Neutrinos at the LHC from Fat-jet using Machine Learning
Wei Liu, Jing Li, Zixiang Chen +1
We explore the potential to use machine learning methods to search for heavy neutrinos, from their hadronic final states including a fat-jet signal, via the processes $pp \rightarr…
Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron
Jingfeng Wu, Difan Zou, Zixiang Chen +3
This paper considers the problem of learning a single ReLU neuron with squared loss (a.k.a., ReLU regression) in the overparameterized regime, where the input dimension can exceed…
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning
Zixiang Chen, Chris Junchi Li, Angela Yuan +2
With the increasing need for handling large state and action spaces, general function approximation has become a key technique in reinforcement learning (RL). In this paper, we pro…
Towards Understanding Mixture of Experts in Deep Learning
Zixiang Chen, Yihe Deng, Yue Wu +2
The Mixture-of-Experts (MoE) layer, a sparsely-activated model controlled by a router, has achieved great success in deep learning. However, the understanding of such architecture…
Benign Overfitting in Two-layer Convolutional Neural Networks
Yuan Cao, Zixiang Chen, Mikhail Belkin +1
Modern neural networks often have great expressive power and can be trained to overfit the training data, while still achieving a good test performance. This phenomenon is referred…
Faster Perturbed Stochastic Gradient Methods for Finding Local Minima
Zixiang Chen, Dongruo Zhou, Quanquan Gu
Escaping from saddle points and finding local minimum is a central problem in nonconvex optimization. Perturbed gradient methods are perhaps the simplest approach for this problem.…
High temporal resolution total-body dynamic PET imaging based on pixel-level time-activity curve correction
Zixiang Chen, Yaping Wu, Na Zhang +5
Dynamic positron emission tomography (dPET) is currently a widely used medical imaging technique for the clinical diagnosis, staging and therapy guidance of all kinds of human canc…
Self-training Converts Weak Learners to Strong Learners in Mixture Models
Spencer Frei, Difan Zou, Zixiang Chen +1
We consider a binary classification problem when the data comes from a mixture of two rotationally symmetric distributions satisfying concentration and anti-concentration propertie…
Almost Optimal Algorithms for Two-player Zero-Sum Linear Mixture Markov Games
Zixiang Chen, Dongruo Zhou, Quanquan Gu
We study reinforcement learning for two-player zero-sum Markov games with simultaneous moves in the finite-horizon setting, where the transition kernel of the underlying Markov gam…
A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks
Zixiang Chen, Yuan Cao, Quanquan Gu +1
A recent breakthrough in deep learning theory shows that the training of over-parameterized deep neural networks can be characterized by a kernel function called \textit{neural tan…
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks?
Zixiang Chen, Yuan Cao, Difan Zou +1
A recent line of research on deep learning focuses on the extremely over-parameterized setting, and shows that when the network width is larger than a high degree polynomial of the…
Stein Neural Sampler
Tianyang Hu, Zixiang Chen, Hanxi Sun +3
We propose two novel samplers to generate high-quality samples from a given (un-normalized) probability density. Motivated by the success of generative adversarial networks, we con…