A Scalable Algorithm for Individually Fair K-means Clustering
We present a scalable algorithm for the individually fair ($p$, $k$)-clustering problem introduced by Jung et al. and Mahabadi et al. Given $n$ points $P$ in a metric space, let $\delta(x)$ for $x\in...
View ArticleLearning-augmented Online Algorithm for Two-level Ski-rental Problem
In this paper, we study the two-level ski-rental problem,where a user needs to fulfill a sequence of demands for multiple items by choosing one of the three payment options: paying for the on-demand...
View ArticleCoRe-GD: A Hierarchical Framework for Scalable Graph Visualization with GNNs
Graph Visualization, also known as Graph Drawing, aims to find geometric embeddings of graphs that optimize certain criteria. Stress is a widely used metric; stress is minimized when every pair of...
View ArticlePrivacy Profiles for Private Selection
Private selection mechanisms (e.g., Report Noisy Max, Sparse Vector) are fundamental primitives of differentially private (DP) data analysis with wide applications to private query release, voting, and...
View ArticleIntegrating LLMs for Explainable Fault Diagnosis in Complex Systems
This paper introduces an integrated system designed to enhance the explainability of fault diagnostics in complex systems, such as nuclear power plants, where operator understanding is critical for...
View ArticleHistoHDR-Net: Histogram Equalization for Single LDR to HDR Image Translation
High Dynamic Range (HDR) imaging aims to replicate the high visual quality and clarity of real-world scenes. Due to the high costs associated with HDR imaging, the literature offers various data-driven...
View ArticleNeural Models for Source Code Synthesis and Completion
Natural language (NL) to code suggestion systems assist developers in Integrated Development Environments (IDEs) by translating NL utterances into compilable code snippet. The current approaches mainly...
View ArticleA Study on Stock Forecasting Using Deep Learning and Statistical Models
Predicting a fast and accurate model for stock price forecasting is been a challenging task and this is an active area of research where it is yet to be found which is the best way to forecast the...
View ArticleAi4Fapar: How artificial intelligence can help to forecast the seasonal earth...
This paper investigated the potential of a multivariate Transformer model to forecast the temporal trajectory of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) for short (1 month)...
View ArticleSound Source Separation Using Latent Variational Block-Wise Disentanglement
While neural network approaches have made significant strides in resolving classical signal processing problems, it is often the case that hybrid approaches that draw insight from both signal...
View ArticlePrivate Knowledge Sharing in Distributed Learning: A Survey
The rise of Artificial Intelligence (AI) has revolutionized numerous industries and transformed the way society operates. Its widespread use has led to the distribution of AI and its underlying data...
View ArticleSocial Physics Informed Diffusion Model for Crowd Simulation
Crowd simulation holds crucial applications in various domains, such as urban planning, architectural design, and traffic arrangement. In recent years, physics-informed machine learning methods have...
View ArticleCan machine learning predict citizen-reported angler behavior?
Prediction of angler behaviors, such as catch rates and angler pressure, is essential to maintaining fish populations and ensuring angler satisfaction. Angler behavior can partly be tracked by online...
View ArticleUnderstanding Practical Membership Privacy of Deep Learning
We apply a state-of-the-art membership inference attack (MIA) to systematically test the practical privacy vulnerability of fine-tuning large image classification models.We focus on understanding the...
View ArticleWeather Prediction with Diffusion Guided by Realistic Forecast Processes
Weather forecasting remains a crucial yet challenging domain, where recently developed models based on deep learning (DL) have approached the performance of traditional numerical weather prediction...
View ArticleThe Essential Role of Causality in Foundation World Models for Embodied AI
Recent advances in foundation models, especially in large multi-modal models and conversational agents, have ignited interest in the potential of generally capable embodied agents. Such agents would...
View ArticleAuthentication and integrity of smartphone videos through multimedia...
Nowadays, mobile devices have become the natural substitute for the digital camera, as they capture everyday situations easily and quickly, encouraging users to express themselves through images and...
View ArticleShadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models
Vision-Language Models (VLMs) excel in generating textual responses from visual inputs, yet their versatility raises significant security concerns. This study takes the first step in exposing VLMs'...
View ArticleDiffsFormer: A Diffusion Transformer on Stock Factor Augmentation
Machine learning models have demonstrated remarkable efficacy and efficiency in a wide range of stock forecasting tasks. However, the inherent challenges of data scarcity, including low signal-to-noise...
View ArticleAdversarial 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. These techniques characterize...
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