no code implementations • 23 Feb 2024 • Artem Vysogorets, Achintya Gopal
Fine-tuning Large Language Models (LLMs) is now a common approach for text classification in a wide range of applications.
no code implementations • 28 Dec 2023 • Agathe Sadeghi, Achintya Gopal, Mohammad Fesanghary
A deeper comprehension of financial markets necessitates understanding not only the statistical dependencies among various entities but also the causal dependencies.
no code implementations • 21 Nov 2023 • Ruslan Tepelyan, Achintya Gopal
The use of machine learning to generate synthetic data has grown in popularity with the proliferation of text-to-image models and especially large language models.
no code implementations • 19 Jul 2023 • Rodrigo Castellon, Achintya Gopal, Brian Bloniarz, David Rosenberg
The generation of synthetic tabular data that preserves differential privacy is a problem of growing importance.
no code implementations • 13 Dec 2021 • Achintya Gopal
Normalizing flows have grown more popular over the last few years; however, they continue to be computationally expensive, making them difficult to be accepted into the broader machine learning community.
no code implementations • 2 Dec 2021 • Achintya Gopal
Within the last few years, there has been a move towards using statistical models in conjunction with neural networks with the end goal of being able to better answer the question, "what do our models know?".
no code implementations • 2 Nov 2021 • Achintya Gopal, Chunho Chang
One of the key components in analyzing the risk of a company is understanding a company's supply chain.
no code implementations • 9 Sep 2021 • You Han, Achintya Gopal, Liwen Ouyang, Aaron Key
By training a machine learning model on disclosed GHG emissions, we are able to estimate the emissions of other companies globally who do not disclose their emissions.
no code implementations • 1 Jan 2021 • Achintya Gopal, Aaron Key
One approach to fix this is isotonic regression, in which a monotonic function is fit on a validation set to map the model's CDF to an optimally calibrated CDF.
no code implementations • 16 Sep 2020 • Achintya Gopal
Normalizing Flows are a powerful technique for learning and modeling probability distributions given samples from those distributions.