5 citations · 15 across the 16 of their papers we have counts for
16 papers
MaskSQL: Safeguarding Privacy for LLM-Based Text-to-SQL via Abstraction
Sepideh Abedini, Shubhankar Mohapatra, D. B. Emerson +3
Large language models (LLMs) have shown promising performance on tasks that require reasoning, such as text-to-SQL, code generation, and debugging. However, regulatory frameworks w…
Adaptive Latent-Space Constraints in Personalized Federated Learning
Sana Ayromlou, Fatemeh Tavakoli, D. B. Emerson
Federated learning (FL) is an effective and widely used approach to training deep learning models on decentralized datasets held by distinct clients. FL also strengthens both secur…
Online Federation For Mixtures of Proprietary Agents with Black-Box Encoders
Xuwei Yang, Fatemeh Tavakoli, David B. Emerson +1
Most industry-standard generative AIs and feature encoders are proprietary, offering only black-box access: their outputs are observable, but their internal parameters and architec…
Template-Based Probes Are Imperfect Lenses for Counterfactual Bias Evaluation in LLMs
Farnaz Kohankhaki, D. B. Emerson, Jacob-Junqi Tian +2
Bias in large language models (LLMs) has many forms, from overt discrimination to implicit stereotypes. Counterfactual bias evaluation is a widely used approach to quantifying bias…
FlexModel: A Framework for Interpretability of Distributed Large Language Models
Matthew Choi, Muhammad Adil Asif, John Willes +1
With the growth of large language models, now incorporating billions of parameters, the hardware prerequisites for their training and deployment have seen a corresponding increase.…
A Comprehensive View of Personalized Federated Learning on Heterogeneous Clinical Datasets
Fatemeh Tavakoli, D. B. Emerson, Sana Ayromlou +5
Federated learning (FL) is increasingly being recognized as a key approach to overcoming the data silos that so frequently obstruct the training and deployment of machine-learning…
On The Role of Reasoning in the Identification of Subtle Stereotypes in Natural Language
Jacob-Junqi Tian, Omkar Dige, D. B. Emerson +1
Large language models (LLMs) are trained on vast, uncurated datasets that contain various forms of biases and language reinforcing harmful stereotypes that may be subsequently inhe…
Can Instruction Fine-Tuned Language Models Identify Social Bias through Prompting?
Omkar Dige, Jacob-Junqi Tian, David Emerson +1
As the breadth and depth of language model applications continue to expand rapidly, it is increasingly important to build efficient frameworks for measuring and mitigating the lear…
Soft-prompt Tuning for Large Language Models to Evaluate Bias
Jacob-Junqi Tian, David Emerson, Sevil Zanjani Miyandoab +3
Prompting large language models has gained immense popularity in recent years due to the advantage of producing good results even without the need for labelled data. However, this…
An A Posteriori Error Estimator for Electrically Coupled Liquid Crystal Equilibrium Configurations
J. H. Adler, D. B. Emerson
This paper derives an a posteriori error estimator for the nonlinear first-order optimality conditions associated with the electrically and flexoelectrically coupled Frank-Oseen mo…
A Posteriori Error Estimators for the Frank-Oseen Model of Liquid Crystals
D. B. Emerson
This paper derives a posteriori error estimators for the nonlinear first-order optimality conditions associated with the Frank-Oseen elastic free-energy model of nematic and choles…
Computing equilibrium states of cholesteric liquid crystals in elliptical channels with deflation algorithms
David B. Emerson, Patrick E. Farrell, James H. Adler +2
We study the problem of a cholesteric liquid crystal confined to an elliptical channel. The system is geometrically frustrated because the cholesteric prefers to adopt a uniform ra…
Competition of Lattice and Basis for Alignment of Nematic Liquid Crystals
Andrew DeBenedictis, Candy Anquetil-Deck, Douglas J. Cleaver +4
Due to elastic anisotropy, two-dimensional patterning of substrates can promote weak azimuthal alignment of adjacent nematic liquid crystals. Here, we consider how such alignment c…
Constrained Optimization for Liquid Crystal Equilibria: Extended Results
J. H. Adler, D. B. Emerson, S. P. MacLachlan +1
This paper investigates energy-minimization finite-element approaches for the computation of nematic liquid crystal equilibrium configurations. We compare the performance of these…
Energy Minimization for Liquid Crystal Equilibrium with Electric and Flexoelectric Effects
J. H. Adler, T. J. Atherton, T. R. Benson +2
This paper outlines an energy-minimization finite-element approach to the modeling of equilibrium configurations for nematic liquid crystals in the presence of internal and externa…
An Energy-Minimization Finite-Element Approach for the Frank-Oseen Model of Nematic Liquid Crystals: Continuum and Discrete Analysis
J. H. Adler, T. J. Atherton, D. B. Emerson +1
This paper outlines an energy-minimization finite-element approach to the computational modeling of equilibrium configurations for nematic liquid crystals under free elastic effect…