5 citations · 8 across the 11 of their papers we have counts for
13 papers
Topic Analysis with Side Information: A Neural-Augmented LDA Approach
Biyi Fang, Truong Vo, Kripa Rajshekhar +1
Traditional topic models such as Latent Dirichlet Allocation (LDA) have been widely used to uncover latent structures in text corpora, but they often struggle to integrate auxiliar…
Zero-shot Graph Reasoning via Retrieval Augmented Framework with LLMs
Hanqing Li, Kiran Sheena Jyothi, Henry Liang +2
We propose a new, training-free method, Graph Reasoning via Retrieval Augmented Framework (GRRAF), that harnesses retrieval-augmented generation (RAG) alongside the code-generation…
Tricks and Plug-ins for Gradient Boosting with Transformers
Biyi Fang, Truong Vo, Jean Utke +1
Transformer architectures dominate modern NLP but often demand heavy computational resources and intricate hyperparameter tuning. To mitigate these challenges, we propose a novel f…
Tricks and Plug-ins for Gradient Boosting in Image Classification
Biyi Fang, Truong Vo, Jean Utke +1
Convolutional Neural Networks (CNNs) have achieved remarkable success across a wide range of machine learning tasks by leveraging hierarchical feature learning through deep archite…
FedGA-Tree: Federated Decision Tree using Genetic Algorithm
Anh V Nguyen, Diego Klabjan
In recent years, with rising concerns for data privacy, Federated Learning has gained prominence, as it enables collaborative training without the aggregation of raw data from part…
BEAR: BGP Event Analysis and Reporting
Hanqing Li, Melania Fedeli, Vinay Kolar +1
The Internet comprises of interconnected, independently managed Autonomous Systems (AS) that rely on the Border Gateway Protocol (BGP) for inter-domain routing. BGP anomalies--such…
Federated Low-Rank Tensor Estimation for Multimodal Image Reconstruction
Anh Van Nguyen, Diego Klabjan, Minseok Ryu +2
Low-rank tensor estimation offers a powerful approach to addressing high-dimensional data challenges and can substantially improve solutions to ill-posed inverse problems, such as…
Continuous-Time Analysis of Federated Averaging
Tom Overman, Diego Klabjan
Federated averaging (FedAvg) is a popular algorithm for horizontal federated learning (FL), where samples are gathered across different clients and are not shared with each other o…
Federated Automated Feature Engineering
Tom Overman, Diego Klabjan
Automated feature engineering (AutoFE) is used to automatically create new features from original features to improve predictive performance without needing significant human inter…
Reverse Prompt Engineering
Hanqing Li, Diego Klabjan
We explore a new language model inversion problem under strict black-box, zero-shot, and limited data conditions. We propose a novel training-free framework that reconstructs promp…
Video to Video Generative Adversarial Network for Few-shot Learning Based on Policy Gradient
Yintai Ma, Diego Klabjan, Jean Utke
The development of sophisticated models for video-to-video synthesis has been facilitated by recent advances in deep reinforcement learning and generative adversarial networks (GAN…
IIFE: Interaction Information Based Automated Feature Engineering
Tom Overman, Diego Klabjan, Jean Utke
Automated feature engineering (AutoFE) is the process of automatically building and selecting new features that help improve downstream predictive performance. While traditional fe…
A Stochastic Large-scale Machine Learning Algorithm for Distributed Features and Observations
Biyi Fang, Diego Klabjan
As the size of modern data sets exceeds the disk and memory capacities of a single computer, machine learning practitioners have resorted to parallel and distributed computing. Giv…