4 citations · 4 across the 7 of their papers we have counts for
7 papers
Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration
Amir Asiaee, Kaveh Aryan
Workload-based differentially private (DP) synthetic data methods privately measure aggregate queries and post-process the noisy answers into synthetic records. Generic workloads c…
Projected Boosting with Fairness Constraints: Quantifying the Cost of Fair Training Distributions
Amir Asiaee, Kaveh Aryan
Boosting algorithms enjoy strong theoretical guarantees: when weak learners maintain positive edge, AdaBoost achieves geometric decrease of exponential loss. We study how to incorp…
Fix Representation (Optimally) Before Fairness: Finite-Sample Shrinkage Population Correction and the True Price of Fairness Under Subpopulation Shift
Amir Asiaee, Kaveh Aryan
Machine learning practitioners frequently observe tension between predictive accuracy and group fairness constraints -- yet sometimes fairness interventions appear to improve accur…
Fairness Under Group-Conditional Prior Probability Shift: Invariance, Drift, and Target-Aware Post-Processing
Amir Asiaee, Kaveh Aryan
Machine learning systems are often trained and evaluated for fairness on historical data, yet deployed in environments where conditions have shifted. A particularly common form of…
DAG DECORation: Continuous Optimization for Structure Learning under Hidden Confounding
Samhita Pal, James O'quinn, Kaveh Aryan +3
We study structure learning for linear Gaussian SEMs in the presence of latent confounding. Existing continuous methods excel when errors are independent, while deconfounding-first…
Conversation Games and a Strategic View of the Turing Test
Kaveh Aryan
Although many game-theoretic models replicate real interactions that often rely on natural language, explicit study of games where language is central to strategic interaction rema…
Using Large Language Models for OntoClean-based Ontology Refinement
Yihang Zhao, Neil Vetter, Kaveh Aryan
This paper explores the integration of Large Language Models (LLMs) such as GPT-3.5 and GPT-4 into the ontology refinement process, specifically focusing on the OntoClean methodolo…