Reliable and Generalizable AI through Prediction-Critical Information Identification and Preservation

PhD Thesis Proposal Defence


Title: "Reliable and Generalizable AI through Prediction-Critical Information 
Identification and Preservation"

by

Miss Luyu QIU


Abstract:

Reliable and generalizable artificial intelligence requires models to use 
the right information for prediction. This proposal studies this requirement 
through the lens of prediction-critical information: the input components, 
model responses, or structural patterns that substantially influence a 
model's prediction. Although model explanation and graph learning appear to 
be different tasks, both depend on understanding what matters for prediction. 
In model explanation, the goal is to identify which input regions drive a 
trained model's decision. In graph learning, the goal is to encode and 
preserve graph structures, such as paths, cycles, motifs, and functional 
subgraphs, that determine graph-level predictions.

The first part of this proposal investigates reliable identification of 
prediction-critical information in model explanation. Perturbation-based 
explainable AI methods, such as RISE, OCCLUSION, and LIME, estimate feature 
importance by masking, removing, or modifying parts of an input and 
observing prediction changes. However, the perturbed samples used to infer 
importance may fall outside the training distribution. Classifier outputs on 
such samples can be confident but unreliable, causing saliency maps to 
reflect perturbation artifacts rather than the model's true decision logic. 
Chapter 3 addresses this problem by introducing an 
out-of-distribution-resistant framework that estimates the inlierness of 
perturbed samples and incorporates this reliability signal into explanation 
generation and evaluation. In this way, the work improves the reliability of 
identifying prediction-critical image regions under distributional 
uncertainty.

The second part studies preservation and learning of prediction-critical 
information in graph-level prediction through my accepted GEGNN work. Graph 
labels often depend on task-relevant structural patterns, including paths, 
cycles, motifs, cliques, and chemically meaningful substructures. Standard 
GNNs may fail to capture such patterns, while naive graph augmentation may 
destroy them. Chapter 4 addresses this challenge by proposing a generalizable 
and expressive graph neural network that combines a $k$-path rooted subgraph 
encoder, adaptive graph augmentation, and consistency-aware training. The 
encoder improves the ability to represent prediction-critical structural 
patterns, adaptive augmentation preferentially perturbs less important edges 
while preserving core structures, and consistency-aware training encourages 
stable predictions across structurally meaningful graph views.

Together, the two completed works support a unified thesis: reliable AI 
requires identifying, validating, and preserving prediction-critical 
information. Chapter 3 focuses on identifying critical input regions while 
validating the reliability of perturbed samples. Chapter 4 focuses on 
encoding and preserving critical graph structures while perturbing less 
important components to improve generalization. This proposal develops 
methods that make AI systems more reliable not by treating all input 
components equally, but by understanding which parts of the data matter for 
prediction and how these parts should be protected under uncertainty.


Date:                   Friday, 3 July 2026

Time:                   2:00pm - 4:00pm

Venue:                  Room 3494
                        Lifts 25/26

Committee Members:      Prof. Lei Chen (Supervisor)
                        Prof. Qian Zhang (Chairperson)
                        Dr. Xiaomin Ouyang