298 citations · 1.2k across the 24 of their papers we have counts for
25 papers
NowcastDiT: Diffusion Transformers are Effective Precipitation Nowcasters
Haoran Xu, Xingzhuo Guo, Yuchen Zhang +3
Precipitation nowcasting demands accurate short-term forecasts under strong spatiotemporal variability. Diffusion models are well suited to modeling complex precipitation distribut…
Revisiting Dimensionality Reduction Techniques for Visual Cluster Analysis: An Empirical Study
Jiazhi Xia, Yuchen Zhang, Jie Song +3
Dimensionality Reduction (DR) techniques can generate 2D projections and enable visual exploration of cluster structures of high-dimensional datasets. However, different DR techniq…
Performance Analysis for Covert Communications Under Faster-than-Nyquist Signaling
Yuan Li, Yuchen Zhang, Wanyu Xiang +4
In this letter, we analyze the performance of covert communications under faster-than-Nyquist (FTN) signaling in the Rayleigh block fading channel. Both Bayesian criterion- and Kul…
Vision-Guided Quadrupedal Locomotion in the Wild with Multi-Modal Delay Randomization
Chieko Sarah Imai, Minghao Zhang, Yuchen Zhang +4
Developing robust vision-guided controllers for quadrupedal robots in complex environments, with various obstacles, dynamical surroundings and uneven terrains, is very challenging.…
Adversarial example generation with AdaBelief Optimizer and Crop Invariance
Bo Yang, Hengwei Zhang, Yuchen Zhang +2
Deep neural networks are vulnerable to adversarial examples, which are crafted by applying small, human-imperceptible perturbations on the original images, so as to mislead deep ne…
On Localized Discrepancy for Domain Adaptation
Yuchen Zhang, Mingsheng Long, Jianmin Wang +1
We propose the discrepancy-based generalization theories for unsupervised domain adaptation. Previous theories introduced distribution discrepancies defined as the supremum over co…
Bridging Theory and Algorithm for Domain Adaptation
Yuchen Zhang, Tianle Liu, Mingsheng Long +1
This paper addresses the problem of unsupervised domain adaption from theoretical and algorithmic perspectives. Existing domain adaptation theories naturally imply minimax optimiza…
Defending against Whitebox Adversarial Attacks via Randomized Discretization
Yuchen Zhang, Percy Liang
Adversarial perturbations dramatically decrease the accuracy of state-of-the-art image classifiers. In this paper, we propose and analyze a simple and computationally efficient def…
Macro Grammars and Holistic Triggering for Efficient Semantic Parsing
Yuchen Zhang, Panupong Pasupat, Percy Liang
To learn a semantic parser from denotations, a learning algorithm must search over a combinatorially large space of logical forms for ones consistent with the annotated denotations…
A note on the approximate admissibility of regularized estimators in the Gaussian sequence model
Xi Chen, Adityanand Guntuboyina, Yuchen Zhang
We study the problem of estimating an unknown vector from an observation drawn according to the normal distribution with mean and identity covariance matrix under the k…
A Hitting Time Analysis of Stochastic Gradient Langevin Dynamics
Yuchen Zhang, Percy Liang, Moses Charikar
We study the Stochastic Gradient Langevin Dynamics (SGLD) algorithm for non-convex optimization. The algorithm performs stochastic gradient descent, where in each step it injects a…
Convexified Convolutional Neural Networks
Yuchen Zhang, Percy Liang, Martin J. Wainwright
We describe the class of convexified convolutional neural networks (CCNNs), which capture the parameter sharing of convolutional neural networks in a convex manner. By representing…
Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences
Chi Jin, Yuchen Zhang, Sivaraman Balakrishnan +2
We provide two fundamental results on the population (infinite-sample) likelihood function of Gaussian mixture models with components. Our first main result shows that t…
Learning Halfspaces and Neural Networks with Random Initialization
Yuchen Zhang, Jason D. Lee, Martin J. Wainwright +1
We study non-convex empirical risk minimization for learning halfspaces and neural networks. For loss functions that are -Lipschitz continuous, we present algorithms to learn ha…
-regularized Neural Networks are Improperly Learnable in Polynomial Time
Yuchen Zhang, Jason D. Lee, Michael I. Jordan
We study the improper learning of multi-layer neural networks. Suppose that the neural network to be learned has hidden layers and that the -norm of the incoming weight…
Splash: User-friendly Programming Interface for Parallelizing Stochastic Algorithms
Yuchen Zhang, Michael I. Jordan
Stochastic algorithms are efficient approaches to solving machine learning and optimization problems. In this paper, we propose a general framework called Splash for parallelizing…
Optimal prediction for sparse linear models? Lower bounds for coordinate-separable M-estimators
Yuchen Zhang, Martin J. Wainwright, Michael I. Jordan
For the problem of high-dimensional sparse linear regression, it is known that an -based estimator can achieve a "fast" rate on the prediction error without any condi…
Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds
Yuchen Zhang, Martin J. Wainwright, Michael I. Jordan
We study the following generalized matrix rank estimation problem: given an matrix and a constant , estimate the number of eigenvalues that are greater than…
Communication-Efficient Distributed Optimization of Self-Concordant Empirical Loss
Yuchen Zhang, Lin Xiao
We consider distributed convex optimization problems originated from sample average approximation of stochastic optimization, or empirical risk minimization in machine learning. We…
On Bayes Risk Lower Bounds
Xi Chen, Adityanand Guntuboyina, Yuchen Zhang
This paper provides a general technique for lower bounding the Bayes risk of statistical estimation, applicable to arbitrary loss functions and arbitrary prior distributions. A low…
Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization
Yuchen Zhang, Lin Xiao
We consider a generic convex optimization problem associated with regularized empirical risk minimization of linear predictors. The problem structure allows us to reformulate it as…
Optimality guarantees for distributed statistical estimation
John C. Duchi, Michael I. Jordan, Martin J. Wainwright +1
Large data sets often require performing distributed statistical estimation, with a full data set split across multiple machines and limited communication between machines. To stud…
Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing
Yuchen Zhang, Xi Chen, Dengyong Zhou +1
Crowdsourcing is a popular paradigm for effectively collecting labels at low cost. The Dawid-Skene estimator has been widely used for inferring the true labels from the noisy label…
Lower bounds on the performance of polynomial-time algorithms for sparse linear regression
Yuchen Zhang, Martin J. Wainwright, Michael I. Jordan
Under a standard assumption in complexity theory (NP not in P/poly), we demonstrate a gap between the minimax prediction risk for sparse linear regression that can be achieved by p…