65 citations · 158 across the 27 of their papers we have counts for
6 papers · 1 filter
Are Logistic Models Really Interpretable?
Danial Dervovic, Freddy Lécué, Nicolás Marchesotti +1
The demand for open and trustworthy AI models points towards widespread publishing of model weights. Consumers of these model weights must be able to act accordingly with the infor…
Cross-Domain Graph Data Scaling: A Showcase with Diffusion Models
Wenzhuo Tang, Haitao Mao, Danial Dervovic +4
Models for natural language and images benefit from data scaling behavior: the more data fed into the model, the better they perform. This 'better with more' phenomenon enables the…
Characterizing Multimodal Long-form Summarization: A Case Study on Financial Reports
Tianyu Cao, Natraj Raman, Danial Dervovic +1
As large language models (LLMs) expand the power of natural language processing to handle long inputs, rigorous and systematic analyses are necessary to understand their abilities…
Surrogate Assisted Monte Carlo Tree Search in Combinatorial Optimization
Saeid Amiri, Parisa Zehtabi, Danial Dervovic +1
Industries frequently adjust their facilities network by opening new branches in promising areas and closing branches in areas where they expect low profits. In this paper, we exam…
Balancing Fairness and Accuracy in Data-Restricted Binary Classification
Zachary McBride Lazri, Danial Dervovic, Antigoni Polychroniadou +3
Applications that deal with sensitive information may have restrictions placed on the data available to a machine learning (ML) classifier. For example, in some applications, a cla…
Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data
Yvonne Zhou, Mingyu Liang, Ivan Brugere +4
The growing use of machine learning (ML) has raised concerns that an ML model may reveal private information about an individual who has contributed to the training dataset. To pre…