Search Results for author:
Found 13 papers, 6 papers with code
Debiasing Machine Unlearning with Counterfactual Examples
This bias emerges from two main sources: (1) data-level bias, characterized by uneven data removal, and (2) algorithm-level bias, which leads to the contamination of the remaining dataset, thereby degrading model accuracy.
A Lie Group Approach to Riemannian Batch Normalization
Using the deformation concept, we generalize the existing Lie groups on SPD manifolds into three families of parameterized Lie groups.
Riemannian Multinomial Logistics Regression for SPD Neural Networks
Besides, our framework offers a novel intrinsic explanation for the most popular LogEig classifier in existing SPD networks.
The Dark Side of Explanations: Poisoning Recommender Systems with Counterfactual Examples
By reversing the learning process of the recommendation model, we thus develop a proficient greedy algorithm to generate fabricated user profiles and their associated interaction records for the aforementioned surrogate model.
Adaptive Riemannian Metrics on SPD Manifolds
Symmetric Positive Definite (SPD) matrices have received wide attention in machine learning due to their intrinsic capacity of encoding underlying structural correlation in data.
GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendations
Although powerful, it is very difficult for a GNN-based recommender system to attach tangible explanations of why a specific item ends up in the list of suggestions for a given user.
DreamNet: A Deep Riemannian Network based on SPD Manifold Learning for Visual Classification
Image set-based visual classification methods have achieved remarkable performance, via characterising the image set in terms of a non-singular covariance matrix on a symmetric positive definite (SPD) manifold.
RLOP: RL Methods in Option Pricing from a Mathematical Perspective
Abstract In this work, we build two environments, namely the modified QLBS and RLOP models, from a mathematics perspective which enables RL methods in option pricing through replicating by portfolio.
AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehension
Recent machine reading comprehension datasets such as ReClor and LogiQA require performing logical reasoning over text.
Riemannian Local Mechanism for SPD Neural Networks
The Symmetric Positive Definite (SPD) matrices have received wide attention for data representation in many scientific areas.
ReLAX: Reinforcement Learning Agent eXplainer for Arbitrary Predictive Models
However, existing CF generation methods either exploit the internals of specific models or depend on each sample's neighborhood, thus they are hard to generalize for complex models and inefficient for large datasets.
Costly Features Classification using Monte Carlo Tree Search
We consider the problem of costly feature classification, where we sequentially select the subset of features to make a balance between the classification error and the feature cost.
Item Response Theory based Ensemble in Machine Learning
In this article, we propose a novel probabilistic framework to improve the accuracy of a weighted majority voting algorithm.
