Search Results for author:
Found 17 papers, 9 papers with code
Referee-Meta-Learning for Fast Adaptation of Locational Fairness
When dealing with data from distinct locations, machine learning algorithms tend to demonstrate an implicit preference of some locations over the others, which constitutes biases that sabotage the spatial fairness of the algorithm.
Benchmarking the Robustness of Image Watermarks
We present WAVES (Watermark Analysis Via Enhanced Stress-testing), a novel benchmark for assessing watermark robustness, overcoming the limitations of current evaluation methods. WAVES integrates detection and identification tasks, and establishes a standardized evaluation protocol comprised of a diverse range of stress tests.
Explore Spurious Correlations at the Concept Level in Language Models for Text Classification
We find that LMs, when encountering spurious correlations between a concept and a label in training or prompts, resort to shortcuts for predictions.
C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder
Representation learning assumes that real-world data is generated by a few semantically meaningful generative factors (i. e., sources of variation) and aims to discover them in the latent space.
AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language Models
Safety alignment of Large Language Models (LLMs) can be compromised with manual jailbreak attacks and (automatic) adversarial attacks.
Talking Models: Distill Pre-trained Knowledge to Downstream Models via Interactive Communication
In this paper, we extend KD with an interactive communication process to help students of downstream tasks learn effectively from pre-trained foundation models.
AceGPT, Localizing Large Language Models in Arabic
This paper is devoted to the development of a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models.
PerceptionCLIP: Visual Classification by Inferring and Conditioning on Contexts
Inspired by it, we observe that providing CLIP with contextual attributes improves zero-shot image classification and mitigates reliance on spurious features.
GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hint
Given the growing concerns about fairness in machine learning and the impressive performance of Graph Neural Networks (GNNs) on graph data learning, algorithmic fairness in GNNs has attracted significant attention.
On the Possibilities of AI-Generated Text Detection
Our work addresses the critical issue of distinguishing text generated by Large Language Models (LLMs) from human-produced text, a task essential for numerous applications.
Transferring Fairness under Distribution Shifts via Fair Consistency Regularization
In many real-world applications, however, such an assumption is often violated as previously trained fair models are often deployed in a different environment, and the fairness of such models has been observed to collapse.
HintNet: Hierarchical Knowledge Transfer Networks for Traffic Accident Forecasting on Heterogeneous Spatio-Temporal Data
However, this problem is challenging due to the spatial heterogeneity of the environment and the sparsity of accidents in space and time.
Understanding the Generalization Benefit of Model Invariance from a Data Perspective
Based on this notion, we refine the generalization bound for invariant models and characterize the suitability of a set of data transformations by the sample covering number induced by transformations, i. e., the smallest size of its induced sample covers.
Adaptive Transfer Learning on Graph Neural Networks
We design an adaptive auxiliary loss weighting model to learn the weights of auxiliary tasks by quantifying the consistency between auxiliary tasks and the target task.
Repulsive Attention: Rethinking Multi-head Attention as Bayesian Inference
The neural attention mechanism plays an important role in many natural language processing applications.
Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints
Text generation from a knowledge base aims to translate knowledge triples to natural language descriptions.
Guess First to Enable Better Compression and Adversarial Robustness
Machine learning models are generally vulnerable to adversarial examples, which is in contrast to the robustness of humans.
