Advances in Transfer Learning: Handling Domains with Set-Valued Outputs and Data Streams with Continuous Covariate Shifts and Noisy Samples

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Advances in Transfer Learning: Handling Domains with Set-Valued 
Outputs and Data Streams with Continuous Covariate Shifts and Noisy Samples"

By

Mr. Xingzhi ZHOU


Abstract:

Transfer learning leverages pre-trained knowledge to address new tasks and is 
classified into two categories: inductive and transductive. Inductive 
transfer learning involves different tasks and limited labeled data in the 
target domain. In contrast, transductive transfer learning concerns tasks 
that are identical but lack labels in the target domain. Although promising, 
inductive methods often exhibit poor generalization due to scarce data. 
Meanwhile, transductive methods during inference (i.e., test-time adaptation) 
face instability caused by continuous covariate shifts, temporal 
correlations, and noisy data.

This dissertation aims to address the above challenges, and its contributions 
are organized into three distinct studies. The first study addresses 
inductive transfer learning for traditional Chinese medicine (TCM) 
prescription prediction, a task with set- valued outputs challenged by 
limited labeled data and complex symptom-herb relationships. We propose 
TCM-FTP, a fine-tuning framework that adapts large language models (LLMs) 
using a curated dataset, DigestDS, derived from clinical documentation. 
TCM-FTP integrates low-rank adaptation (LoRA) to reduce computational demands 
and utilizes herb-order randomization for data augmentation, based on the 
set-valued output property of prescriptions. We empirically show that TCM-FTP 
significantly improves herb identification and dosage estimation.

The second study explores test-time adaptation under continuous covariate 
shifts and temporal correlation. We introduce ResiTTA, a resilient test-time 
adaptation framework that mitigates overfitting by softly regularizing batch 
normalization statistics between source and target domains. To stabilize 
adaptation, ResiTTA maintains a low-entropy, class-balanced memory bank, 
enabling teacher-student self-training under an approximate i.i.d. 
assumption. Rigorous benchmarks demonstrate that ResiTTA consistently 
outperforms other baselines.

The third study explores noisy test-time settings. We propose MoTTA, a 
model-pruning- based framework that identifies noisy samples via output 
difference under model pruning (ODP), offering greater robustness than 
prediction-based filtering. Additionally, MoTTA introduces flatness-aware 
entropy minimization (FlatEM), guiding optimization toward flatter loss 
landscapes through zeroth- and first-order constraints. Empirical evaluations 
confirm that MoTTA achieves superior adaptation performance under noisy 
conditions.


Date:                   Monday, 25 August 2025

Time:                   3:00pm - 5:00pm

Venue:                  Room 2408
                        Lifts 17/18

Chairman:               Prof. Guohua CHEN (CBE)

Committee Members:      Prof. Nevin ZHANG (Supervisor)
                        Dr. Brian MAK
                        Prof. Dit-Yan YEUNG
                        Dr. Yuan LIU (ISD)
                        Prof. William Kwok Wai CHUENG (HKBU)