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Found 10 papers, 0 papers with code
Towards Efficient Active Learning in NLP via Pretrained Representations
Fine-tuning Large Language Models (LLMs) is now a common approach for text classification in a wide range of applications.
Causal Discovery in Financial Markets: A Framework for Nonstationary Time-Series Data
A deeper comprehension of financial markets necessitates understanding not only the statistical dependencies among various entities but also the causal dependencies.
Generative Machine Learning for Multivariate Equity Returns
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.
DP-TBART: A Transformer-based Autoregressive Model for Differentially Private Tabular Data Generation
The generation of synthetic tabular data that preserves differential privacy is a problem of growing importance.
ELF: Exact-Lipschitz Based Universal Density Approximator Flow
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.
Why Calibration Error is Wrong Given Model Uncertainty: Using Posterior Predictive Checks with Deep Learning
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?".
Discovering Supply Chain Links with Augmented Intelligence
One of the key components in analyzing the risk of a company is understanding a company's supply chain.
Estimation of Corporate Greenhouse Gas Emissions via Machine Learning
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.
Normalizing Flows for Calibration and Recalibration
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.
Quasi-Autoregressive Residual (QuAR) Flows
Normalizing Flows are a powerful technique for learning and modeling probability distributions given samples from those distributions.
