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Found 22 papers, 5 papers with code
Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects
Modeling spillover effects from observational data is an important problem in economics, business, and other fields of research.
Deep causal representation learning for unsupervised domain adaptation
Recent research on deep domain adaptation proposed to mitigate this problem by forcing the deep model to learn more transferable feature representations across domains.
Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation
In this work, models that aim to answer causal questions are referred to as causal interpretable models.
Causal Adversarial Network for Learning Conditional and Interventional Distributions
The LGN is a GAN-based architecture which learns and samples from the causal model over labels.
Use of Bayesian Nonparametric methods for Estimating the Measurements in High Clutter
We robustly and accurately estimate the trajectory of the moving target in a high clutter environment with an unknown number of clutters by employing Bayesian nonparametric modeling.
Causal Inference for Time series Analysis: Problems, Methods and Evaluation
In this paper, we focus on two causal inference tasks, i. e., treatment effect estimation and causal discovery for time series data, and provide a comprehensive review of the approaches in each task.
Evaluation Methods and Measures for Causal Learning Algorithms
To bridge from conventional causal inference (i. e., based on statistical methods) to causal learning with big data (i. e., the intersection of causal inference and machine learning), in this survey, we review commonly-used datasets, evaluation methods, and measures for causal learning using an evaluation pipeline similar to conventional machine learning.
Domain Generalization -- A Causal Perspective
As a result models that rely on this assumption exhibit poor generalization capabilities.
PEACE: Cross-Platform Hate Speech Detection- A Causality-guided Framework
Hate speech detection refers to the task of detecting hateful content that aims at denigrating an individual or a group based on their religion, gender, sexual orientation, or other characteristics.
Causality Guided Disentanglement for Cross-Platform Hate Speech Detection
By disentangling input into platform-dependent features (useful for predicting hate targets) and platform-independent features (used to predict the presence of hate), we learn invariant representations resistant to distribution shifts.
ConDA: Contrastive Domain Adaptation for AI-generated Text Detection
Given the potential malicious nature in which these LLMs can be used to generate disinformation at scale, it is important to build effective detectors for such AI-generated text.
Towards LLM-guided Causal Explainability for Black-box Text Classifiers
Inspired by recent endeavors to utilize Large Language Models (LLMs) as experts, in this work, we aim to leverage the instruction-following and textual understanding capabilities of recent state-of-the-art LLMs to facilitate causal explainability via counterfactual explanation generation for black-box text classifiers.
How Reliable Are AI-Generated-Text Detectors? An Assessment Framework Using Evasive Soft Prompts
The novel universal evasive prompt is achieved in two steps: First, we create an evasive soft prompt tailored to a specific PLM through prompt tuning; and then, we leverage the transferability of soft prompts to transfer the learned evasive soft prompt from one PLM to another.
VQA-GEN: A Visual Question Answering Benchmark for Domain Generalization
validating that comprehensive multi-modal shifts are critical for robust VQA generalization.
A Generative Approach to Surrogate-based Black-box Attacks
Different from the discriminative approach, we propose a generative surrogate that learns the distribution of samples residing on or close to the target's decision boundaries.
Causal Feature Selection for Responsible Machine Learning
This survey focuses on the current study of causal feature selection: what it is and how it can reinforce the four aspects of responsible ML.
Exploiting Class Probabilities for Black-box Sentence-level Attacks
Sentence-level attacks craft adversarial sentences that are synonymous with correctly-classified sentences but are misclassified by the text classifiers.
Adversarial Text Purification: A Large Language Model Approach for Defense
Adversarial purification is a defense mechanism for safeguarding classifiers against adversarial attacks without knowing the type of attacks or training of the classifier.
The Wolf Within: Covert Injection of Malice into MLLM Societies via an MLLM Operative
Unlike direct harmful output generation for MLLMs, our research demonstrates how a single MLLM agent can be subtly influenced to generate prompts that, in turn, induce other MLLM agents in the society to output malicious content.
A Survey of AI-generated Text Forensic Systems: Detection, Attribution, and Characterization
We have witnessed lately a rapid proliferation of advanced Large Language Models (LLMs) capable of generating high-quality text.
EAGLE: A Domain Generalization Framework for AI-generated Text Detection
With the advancement in capabilities of Large Language Models (LLMs), one major step in the responsible and safe use of such LLMs is to be able to detect text generated by these models.
Cross-Platform Hate Speech Detection with Weakly Supervised Causal Disentanglement
Content moderation faces a challenging task as social media's ability to spread hate speech contrasts with its role in promoting global connectivity.
