8 citations · 13 across the 9 of their papers we have counts for
9 papers
Replica Symmetry Breaking and Algorithmic Thresholds in Empirical Risk Minimization under Multi-Index Model
Andrea Montanari, Kangjie Zhou
Modern machine learning models are trained by optimizing high-dimensional non-convex empirical risk functions. Such cost functions can have a multitude of local optima and yet, gra…
Informative Path Planning of Autonomous Vehicle for Parking Occupancy Estimation
Yunze Hu, Jiaao Chen, Kangjie Zhou +3
Parking occupancy estimation holds significant potential in facilitating parking resource management and mitigating traffic congestion. Existing approaches employ robotic systems t…
Probabilistic Visibility-Aware Trajectory Planning for Target Tracking in Cluttered Environments
Han Gao, Pengying Wu, Yao Su +4
Target tracking has numerous significant civilian and military applications, and maintaining the visibility of the target plays a vital role in ensuring the success of the tracking…
Learning time-scales in two-layers neural networks
Raphaël Berthier, Andrea Montanari, Kangjie Zhou
Gradient-based learning in multi-layer neural networks displays a number of striking features. In particular, the decrease rate of empirical risk is non-monotone even after averagi…
Lower Bounds for the Convergence of Tensor Power Iteration on Random Overcomplete Models
Yuchen Wu, Kangjie Zhou
Tensor decomposition serves as a powerful primitive in statistics and machine learning, and has numerous applications in problems such as learning latent variable models or mixture…
Overparametrized linear dimensionality reductions: From projection pursuit to two-layer neural networks
Andrea Montanari, Kangjie Zhou
Given a cloud of data points in , consider all projections onto -dimensional subspaces of and, for each such projection, the empirical distribut…
Tractability from overparametrization: The example of the negative perceptron
Andrea Montanari, Yiqiao Zhong, Kangjie Zhou
In the negative perceptron problem we are given data points , where is a -dimensional vector and is a binary l…
Propensity Score Adapted Covariate Selection for Causal Inference
Kangjie Zhou, Jinzhu Jia
In this paper, we propose a propensity score adapted variable selection procedure to select covariates for inclusion in propensity score models, in order to eliminate confounding b…
Variance Reduction for Causal Inference
Kangjie Zhou, Jinzhu Jia
Propensity score methods have been shown to be powerful in obtaining efficient estimators of average treatment effect (ATE) from observational data, especially under the existence…