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Found 27 papers, 3 papers with code
Uncertainty Quantification on Graph Learning: A Survey
Graphical models, including Graph Neural Networks (GNNs) and Probabilistic Graphical Models (PGMs), have demonstrated their exceptional capabilities across numerous fields.
Incorporating Domain Differential Equations into Graph Convolutional Networks to Lower Generalization Discrepancy
We theoretically derive conditions where GCNs incorporating such domain differential equations are robust to mismatched training and testing data compared to baseline domain agnostic models.
Robust Conformal Prediction under Distribution Shift via Physics-Informed Structural Causal Model
Uncertainty is critical to reliable decision-making with machine learning.
A Differential Geometric View and Explainability of GNN on Evolving Graphs
Graphs are ubiquitous in social networks and biochemistry, where Graph Neural Networks (GNN) are the state-of-the-art models for prediction.
Implementing Recycling Methods for Linear Systems in Python with an Application to Multiple Objective Optimization
The goal of this project was to implement RMINRES in Python and PyTorch and add it to the established Pareto front code to reduce computational cost.
DetectGPT-SC: Improving Detection of Text Generated by Large Language Models through Self-Consistency with Masked Predictions
Using this observation, we subsequently proposed a new method for AI-generated texts detection based on self-consistency with masked predictions to determine whether a text is generated by LLMs.
Robust Ranking Explanations
Robust explanations of machine learning models are critical to establish human trust in the models.
Inconsistent Matters: A Knowledge-guided Dual-consistency Network for Multi-modal Rumor Detection
Rumor spreaders are increasingly utilizing multimedia content to attract the attention and trust of news consumers.
Provable Robust Saliency-based Explanations
Robust explanations of machine learning models are critical to establishing human trust in the models.
Efficient first-order predictor-corrector multiple objective optimization for fair misinformation detection
We propose to explore the Pareto front as a manifold from a few initial optima, based on a predictor-corrector method.
Are Your Reviewers Being Treated Equally? Discovering Subgroup Structures to Improve Fairness in Spam Detection
We first identify subgroup structures in the review graph that lead to discrepant accuracy in the groups.
Interpretable and Effective Reinforcement Learning for Attacking against Graph-based Rumor Detection
To discover subtle vulnerabilities, we design a powerful attacking algorithm to camouflage rumors in social networks based on reinforcement learning that can interact with and attack any black-box detectors.
Multi-objective Explanations of GNN Predictions
We design a user study to investigate such joint effects and use the findings to design a multi-objective optimization (MOO) algorithm to find Pareto optimal explanations that are well-balanced in simulatability and counterfactual.
Explaining GNN over Evolving Graphs using Information Flow
We define the problem of explaining evolving GNN predictions and propose an axiomatic attribution method to uniquely decompose the change in a prediction to paths on computation graphs.
Self-learn to Explain Siamese Networks Robustly
Learning to compare two objects are essential in applications, such as digital forensics, face recognition, and brain network analysis, especially when labeled data is scarce and imbalanced.
Truth Discovery in Sequence Labels from Crowds
The proposed Aggregation method for Sequential Labels from Crowds ($AggSLC$) jointly considers the characteristics of sequential labeling tasks, workers' reliabilities, and advanced machine learning techniques.
Robust Spammer Detection by Nash Reinforcement Learning
We experiment on three large review datasets using various state-of-the-art spamming and detection strategies and show that the optimization algorithm can reliably find an equilibrial detector that can robustly and effectively prevent spammers with any mixed spamming strategies from attaining their practical goal.
Rigorous Explanation of Inference on Probabilistic Graphical Models
There is no existing method to rigorously attribute the inference outcomes to the contributing factors of the graphical models.
Scalable Explanation of Inferences on Large Graphs
Probabilistic inferences distill knowledge from graphs to aid human make important decisions.
Semi-supervised Deep Representation Learning for Multi-View Problems
While neural networks for learning representation of multi-view data have been previously proposed as one of the state-of-the-art multi-view dimension reduction techniques, how to make the representation discriminative with only a small amount of labeled data is not well-studied.
Dimensionality Reduction
Learning Representation Of Multi-View Data
Securing Behavior-based Opinion Spam Detection
In terms of security, DETER is versatile enough to be vaccinated against diverse and unexpected evasions, is agnostic about evasion strategy and can be released without privacy concern.
Product Function Need Recognition via Semi-supervised Attention Network
Functionality is of utmost importance to customers when they purchase products.
Dual Attention Network for Product Compatibility and Function Satisfiability Analysis
Product compatibility and their functionality are of utmost importance to customers when they purchase products, and to sellers and manufacturers when they sell products.
SEVEN: Deep Semi-supervised Verification Networks
The model consists of two complementary components.
CER: Complementary Entity Recognition via Knowledge Expansion on Large Unlabeled Product Reviews
One important product feature is the complementary entity (products) that may potentially work together with the reviewed product.
Multi-source Hierarchical Prediction Consolidation
We propose a novel multi-source hierarchical prediction consolidation method to effectively exploits the complicated hierarchical label structures to resolve the noisy and conflicting information that inherently originates from multiple imperfect sources.
Multilabel Consensus Classification
Nonetheless, data nowadays are usually multilabeled, such that more than one label have to be predicted at the same time.
