Search Results for author:
Found 17 papers, 7 papers with code
Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation
The key technical contribution is a novel LLM agent framework that accounts for individual activity patterns and motivations, including a self-consistency approach to align LLMs with real-world activity data and a retrieval-augmented strategy for interpretable activity generation.
Shall We Talk: Exploring Spontaneous Collaborations of Competing LLM Agents
Recent advancements have shown that agents powered by large language models (LLMs) possess capabilities to simulate human behaviors and societal dynamics.
Probabilistic Routing for Graph-Based Approximate Nearest Neighbor Search
Approximate nearest neighbor search (ANNS) in high-dimensional spaces is a pivotal challenge in the field of machine learning.
Jellyfish: A Large Language Model for Data Preprocessing
This paper explores the utilization of LLMs for data preprocessing (DP), a crucial step in the data mining pipeline that transforms raw data into a clean format conducive to easy processing.
BClean: A Bayesian Data Cleaning System
By evaluating on both real-world and synthetic datasets, we demonstrate that BClean is capable of achieving an F-measure of up to 0. 9 in data cleaning, outperforming existing Bayesian methods by 2% and other data cleaning methods by 15%.
Smart Agent-Based Modeling: On the Use of Large Language Models in Computer Simulations
ABM's strength lies in its bottom-up methodology, illuminating emergent phenomena by modeling the behaviors of individual components of a system.
Large Language Models as Data Preprocessors
Large Language Models (LLMs), typified by OpenAI's GPT series and Meta's LLaMA variants, have marked a significant advancement in artificial intelligence.
"Guinea Pig Trials" Utilizing GPT: A Novel Smart Agent-Based Modeling Approach for Studying Firm Competition and Collusion
Our results demonstrate that, in the absence of communication, smart agents consistently reach tacit collusion, leading to prices converging at levels higher than the Bertrand equilibrium price but lower than monopoly or cartel prices.
Scardina: Scalable Join Cardinality Estimation by Multiple Density Estimators
In recent years, machine learning-based cardinality estimation methods are replacing traditional methods.
DeepJoin: Joinable Table Discovery with Pre-trained Language Models
They are either exact solutions whose running time is linear in the sizes of query column and target table repository or approximate solutions lacking precision.
An Empirical Study of Personalized Federated Learning
Federated learning is a distributed machine learning approach in which a single server and multiple clients collaboratively build machine learning models without sharing datasets on clients.
Similarity Search on Computational Notebooks
In this paper, we propose a similarity search on computational notebooks and develop a new framework for the similarity search.
FedMe: Federated Learning via Model Exchange
First, to optimize the model architectures for local data, clients tune their own personalized models by comparing to exchanged models and picking the one that yields the best performance.
Efficient Joinable Table Discovery in Data Lakes: A High-Dimensional Similarity-Based Approach
Finding joinable tables in data lakes is key procedure in many applications such as data integration, data augmentation, data analysis, and data market.
Consistent and Flexible Selectivity Estimation for High-Dimensional Data
Selectivity estimation aims at estimating the number of database objects that satisfy a selection criterion.
Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach
The feature extraction model transforms original data and threshold to a Hamming space, in which a deep learning-based regression model is utilized to exploit the incremental property of cardinality w. r. t.
Pigeonring: A Principle for Faster Thresholded Similarity Search
Many solutions to these problems utilize the pigeonhole principle to find candidates that satisfy a filtering condition.
