no code implementations • 1 Mar 2024 • Sana Ebrahimi, Kaiwen Chen, Abolfazl Asudeh, Gautam Das, Nick Koudas
Pre-trained Large Language Models (LLMs) have significantly advanced natural language processing capabilities but are susceptible to biases present in their training data, leading to unfair outcomes in various applications.
no code implementations • 14 Jul 2023 • Suraj Shetiya, Shohedul Hasan, Abolfazl Asudeh, Gautam Das
Linear Regression is a seminal technique in statistics and machine learning, where the objective is to build linear predictive models between a response (i. e., dependent) variable and one or more predictor (i. e., independent) variables.
1 code implementation • 2020 • Zhuojie Zhou, Nan Zhang, Gautam Das
Random walk fits naturally with this problem because, for most online social networks, the only query we can issue through the interface is to retrieve the neighbors of a given node (i. e., no access to the full graph topology).
no code implementations • 24 Mar 2019 • Saravanan Thirumuruganathan, Shohedul Hasan, Nick Koudas, Gautam Das
We use deep generative models, an unsupervised learning based approach, to learn the data distribution faithfully such that aggregate queries could be answered approximately by generating samples from the learned model.
no code implementations • 24 Mar 2019 • Shohedul Hasan, Saravanan Thirumuruganathan, Jees Augustine, Nick Koudas, Gautam Das
Selectivity estimation - the problem of estimating the result size of queries - is a fundamental problem in databases.