activity
20242026
most citedBorrowing Treasures from Neighbors: In-Context Learning for Multimodal Learning with Missing Modalities and Data Scarcity

2 citations · 2 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2026

Deconstructing the Failure of Ideal Noise Correction: A Three-Pillar Diagnosis

Chen Feng, Zhuo Zhi, Zhao Huang +5

Statistically consistent methods based on the noise transition matrix () offer a theoretically grounded solution to Learning with Noisy Labels (LNL), with guarantees of converge…

cs.AI2025

Seeing and Reasoning with Confidence: Supercharging Multimodal LLMs with an Uncertainty-Aware Agentic Framework

Zhuo Zhi, Chen Feng, Adam Daneshmend +6

Multimodal large language models (MLLMs) show promise in tasks like visual question answering (VQA) but still face challenges in multimodal reasoning. Recent works adapt agentic fr…

cs.LG2024

Deep Learning-Based Noninvasive Screening of Type 2 Diabetes with Chest X-ray Images and Electronic Health Records

Sanjana Gundapaneni, Zhuo Zhi, Miguel Rodrigues

The imperative for early detection of type 2 diabetes mellitus (T2DM) is challenged by its asymptomatic onset and dependence on suboptimal clinical diagnostic tests, contributing t…

cs.LG2024★ 2 cited

Borrowing Treasures from Neighbors: In-Context Learning for Multimodal Learning with Missing Modalities and Data Scarcity

Zhuo Zhi, Ziquan Liu, Moe Elbadawi +5

Multimodal machine learning with missing modalities is an increasingly relevant challenge arising in various applications such as healthcare. This paper extends the current researc…

cs.LG2024

PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial Attacks

Chen Feng, Ziquan Liu, Zhuo Zhi +3

It is widely known that state-of-the-art machine learning models, including vision and language models, can be seriously compromised by adversarial perturbations. It is therefore i…

cs.LG2024

HgbNet: predicting hemoglobin level/anemia degree from EHR data

Zhuo Zhi, Moe Elbadawi, Adam Daneshmend +4

Anemia is a prevalent medical condition that typically requires invasive blood tests for diagnosis and monitoring. Electronic health records (EHRs) have emerged as valuable data so…