16 citations · 73 across the 29 of their papers we have counts for
12 papers · 1 filter
EGiME: Entropy-Guided Model Evaluation in Few-Shot Learning
Dequan Jin, Tao Li, Lifeng Xing +2
The feature-extraction quality of pre-trained models sets the ceiling of downstream transfer learning, yet few-shot model selection remains difficult: supervised evaluation on scar…
Cross-Platform Hate Speech Detection with Weakly Supervised Causal Disentanglement
Paras Sheth, Tharindu Kumarage, Raha Moraffah +2
Content moderation faces a challenging task as social media's ability to spread hate speech contrasts with its role in promoting global connectivity. With rapidly evolving slang an…
A Generative Approach to Surrogate-based Black-box Attacks
Raha Moraffah, Huan Liu
Surrogate-based black-box attacks have exposed the heightened vulnerability of DNNs. These attacks are designed to craft adversarial examples for any samples with black-box target…
Causal Feature Selection for Responsible Machine Learning
Raha Moraffah, Paras Sheth, Saketh Vishnubhatla +1
Machine Learning (ML) has become an integral aspect of many real-world applications. As a result, the need for responsible machine learning has emerged, focusing on aligning ML mod…
Domain Generalization -- A Causal Perspective
Paras Sheth, Raha Moraffah, K. Selçuk Candan +2
Machine learning models rely on various assumptions to attain high accuracy. One of the preliminary assumptions of these models is the independent and identical distribution, which…
Evaluation Methods and Measures for Causal Learning Algorithms
Lu Cheng, Ruocheng Guo, Raha Moraffah +3
The convenient access to copious multi-faceted data has encouraged machine learning researchers to reconsider correlation-based learning and embrace the opportunity of causality-ba…
Causal Inference for Time series Analysis: Problems, Methods and Evaluation
Raha Moraffah, Paras Sheth, Mansooreh Karami +5
Time series data is a collection of chronological observations which is generated by several domains such as medical and financial fields. Over the years, different tasks such as c…
Use of Bayesian Nonparametric methods for Estimating the Measurements in High Clutter
Bahman Moraffah, Christ Richmond, Raha Moraffah +1
Robust tracking of a target in a clutter environment is an important and challenging task. In recent years, the nearest neighbor methods and probabilistic data association filters…
Causal Adversarial Network for Learning Conditional and Interventional Distributions
Raha Moraffah, Bahman Moraffah, Mansooreh Karami +2
We propose a generative Causal Adversarial Network (CAN) for learning and sampling from conditional and interventional distributions. In contrast to the existing CausalGAN which re…
Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation
Raha Moraffah, Mansooreh Karami, Ruocheng Guo +2
Machine learning models have had discernible achievements in a myriad of applications. However, most of these models are black-boxes, and it is obscure how the decisions are made b…
Deep causal representation learning for unsupervised domain adaptation
Raha Moraffah, Kai Shu, Adrienne Raglin +1
Studies show that the representations learned by deep neural networks can be transferred to similar prediction tasks in other domains for which we do not have enough labeled data.…
Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects
Vineeth Rakesh, Ruocheng Guo, Raha Moraffah +2
Modeling spillover effects from observational data is an important problem in economics, business, and other fields of research. % It helps us infer the causality between two seemi…