12 citations · 13 across the 7 of their papers we have counts for
7 papers
Learning Kronecker-Structured Graphs from Smooth Signals
Changhao Shi, Gal Mishne
Graph learning, or network inference, is a prominent problem in graph signal processing (GSP). GSP generalizes the Fourier transform to non-Euclidean domains, and graph learning is…
Adaptive Weighted Random Isolation (AWRI): a simple design to estimate causal effects under network interference
Changhao Shi, Haoyu Yang, Yichen Qin +1
Recently, causal inference under interference has gained increasing attention in the literature. In this paper, we focus on randomized designs for estimating the total treatment ef…
Learning Cartesian Product Graphs with Laplacian Constraints
Changhao Shi, Gal Mishne
Graph Laplacian learning, also known as network topology inference, is a problem of great interest to multiple communities. In Gaussian graphical models (GM), graph learning amount…
Graph Laplacian Learning with Exponential Family Noise
Changhao Shi, Gal Mishne
Graph signal processing (GSP) is a prominent framework for analyzing signals on non-Euclidean domains. The graph Fourier transform (GFT) uses the combinatorial graph Laplacian matr…
Exploring Compositional Visual Generation with Latent Classifier Guidance
Changhao Shi, Haomiao Ni, Kai Li +3
Diffusion probabilistic models have achieved enormous success in the field of image generation and manipulation. In this paper, we explore a novel paradigm of using the diffusion m…
Conditional Image-to-Video Generation with Latent Flow Diffusion Models
Haomiao Ni, Changhao Shi, Kai Li +2
Conditional image-to-video (cI2V) generation aims to synthesize a new plausible video starting from an image (e.g., a person's face) and a condition (e.g., an action class label li…
Online Adversarial Purification based on Self-Supervision
Changhao Shi, Chester Holtz, Gal Mishne
Deep neural networks are known to be vulnerable to adversarial examples, where a perturbation in the input space leads to an amplified shift in the latent network representation. I…