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Ji Zhu

7 accepted papers

2026

Efficient Synthetic Network Generation via Latent Embedding Reconstruction

ICML 2026poster

Network data are ubiquitous across the social sciences, biology, and information systems. Generating realistic synthetic network data has broad applications from network simulation to scientific discovery. However, many existing black-box approaches for network generation tend to overfit observed da…

Cited by 0SourceScholar
2026

GSON: A Group-Based Social Navigation Framework with Large Multimodal Model

ICRA 2026poster

With the increasing presence of service robots and autonomous vehicles in human environments, navigation systems need to evolve beyond simple destination reach to incorporate social awareness. This paper introduces GSON, a novel group-based social navigation framework that leverages Large Multimodal…

2026

ReLaSH: Reconstructing Joint Latent Spaces for Efficient Generation of Synthetic Hypergraphs with Hyperlink Attributes

ICLR 2026poster

Hypergraph network data, which capture multi-way interactions among entities, have become increasingly prevalent in the big data era, spanning fields such as social science, medical research, and biology. Generating synthetic hyperlinks with attributes from an observed hypergraph has broad applicati…

Cited by 0SourceScholar
2025

GSON: A Group-Based Social Navigation Framework With Large Multimodal Model

RA-L 2025

With the increasing presence of service robots and autonomous vehicles in human environments, navigation systems need to evolve beyond simple destination reach to incorporate social awareness. This paper introduces <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/199

Cited by 10SourceScholar
2019

A Flexible Generative Framework for Graph-based Semi-supervised Learning

NeurIPS 2019poster

We consider a family of problems that are concerned about making predictions for the majority of unlabeled, graph-structured data samples based on a small proportion of labeled samples. Relational information among the data samples, often encoded in the graph/network structure, is shown to be helpf…

2018

Online Multi-Object Tracking with Dual Matching Attention Networks

ECCV 2018poster

In this paper, we propose an online Multi-Object Tracking (MOT) approach which integrates the merits of single object tracking and data association methods in a unified framework to handle noisy detections and frequent interactions between targets. Specifically, for applying single object tracking i…

Cited by 455SourcePDFScholar