An improved outer bound on the admissible source region for broadcast channels with correlated sources. - Kia Khezeli; Jun Chen 005 (ISIT, 2014)

Outer Bounds on the Admissible Source Region for Broadcast Channels with Correlated Sources. - Kia Khezeli; Jun Chen (CoRR, 2015)

Outer Bounds on the Admissible Source Region for Broadcast Channels With Correlated Sources. - Kia Khezeli; Jun Chen 005 (IEEE Trans. Inf. Theory, 2015)

Data-driven pricing of demand response. - Kia Khezeli; Eilyan Bitar (SmartGridComm, 2016)

A Source-Channel Separation Theorem with Application to the Source Broadcast Problem. - Kia Khezeli; Jun Chen (CoRR, 2016)

A Source-Channel Separation Theorem With Application to the Source Broadcast Problem. - Kia Khezeli; Jun Chen 005 (IEEE Trans. Inf. Theory, 2016)

An Online Learning Approach to Buying and Selling Demand Response. - Kia Khezeli; Eilyan Bitar (CoRR, 2017)

Risk-Sensitive Learning and Pricing for Demand Response. - Kia Khezeli; Eilyan Bitar (IEEE Trans. Smart Grid, 2018)

Safe Linear Stochastic Bandits. - Kia Khezeli; Eilyan Bitar (AAAI, 2020)

On Information Gain and Regret Bounds in Gaussian Process Bandits. - Sattar Vakili; Kia Khezeli; Victor Picheny (AISTATS, 2021)

Prototyping CRISP: A Causal Relation and Inference Search Platform applied to Colorectal Cancer Data. - Samuel Budd; Arno Blaas; Adrienne Hoarfrost; Kia Khezeli; Krittika D'Silva; Frank Soboczenski; Graham Mackintosh; Nicholas Chia; John Kalantari (LifeTech, 2021)

On Invariance Penalties for Risk Minimization. - Kia Khezeli; Arno Blaas; Frank Soboczenski; Nicholas Chia; John Kalantari (CoRR, 2021)

Universally Rank Consistent Ordinal Regression in Neural Networks. - W. Garrett Jenkinson; Kia Khezeli; Gavin R. Oliver; John Kalantari; Eric W. Klee (CoRR, 2021)

Invariant Risk Minimisation for Cross-Organism Inference: Substituting Mouse Data for Human Data in Human Risk Factor Discovery. - Odhran O'Donoghue; Paul Duckworth; Giuseppe Ughi; Linus Scheibenreif; Kia Khezeli; Adrienne Hoarfrost; Samuel Budd; Patrick Foley; Nicholas Chia; John Kalantari; Graham Mackintosh; Frank Soboczenski; et al. (CoRR, 2021)

Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health. - Ryan T. Scott; Erik L. Antonsen; Lauren M. Sanders; Jaden J. A. Hastings; Seung-Min Park; Graham Mackintosh; Robert J. Reynolds; Adrienne L. Hoarfrost; Aenor Sawyer; Casey S. Greene; Benjamin S. Glicksberg; Corey A. Theriot; et al. (CoRR, 2021)

Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs. - Lauren M. Sanders; Jason H. Yang; Ryan T. Scott; Amina Ann Qutub; Héctor García Martín; Daniel C. Berrios; Jaden J. A. Hastings; Jon Rask; Graham Mackintosh; Adrienne L. Hoarfrost; Stuart J. Chalk; John Kalantari; et al. (CoRR, 2021)

End-to-End Machine Learning Framework for Facial AU Detection in Intensive Care Units. - Subhash Nerella; Kia Khezeli; Andrea Davidson; Patrick James Tighe; Azra Bihorac; Parisa Rashidi (CoRR, 2022)

Multi-dimensional patient acuity estimation with longitudinal EHR tokenization and flexible transformer networks. - Benjamin Shickel; Brandon Silva; Tezcan Ozrazgat-Baslanti; Yuanfang Ren; Kia Khezeli; Ziyuan Guan; Patrick James Tighe; Azra Bihorac; Parisa Rashidi (Frontiers Digit. Health, 2022)

Dynamic Delirium Prediction in the Intensive Care Unit using Machine Learning on Electronic Health Records. - Miguel Contreras; Brandon Silva; Benjamin Shickel; Sabyasachi Bandyopadhyay; Ziyuan Guan; Yuanfang Ren; Tezcan Ozrazgat-Baslanti; Kia Khezeli; Azra Bihorac; Parisa Rashidi (BHI, 2023)

AI-Enhanced Intensive Care Unit: Revolutionizing Patient Care with Pervasive Sensing. - Subhash Nerella; Ziyuan Guan; Scott Siegel; Jiaqing Zhang; Kia Khezeli; Azra Bihorac; Parisa Rashidi (CoRR, 2023)

Predicting risk of delirium from ambient noise and light information in the ICU. - Sabyasachi Bandyopadhyay; Ahna Cecil; Jessica Sena; Andrea Davidson; Ziyuan Guan; Subhash Nerella; Jiaqing Zhang; Kia Khezeli; Brooke Armfield; Azra Bihorac; Parisa Rashidi (CoRR, 2023)

Transformer Models for Acute Brain Dysfunction Prediction. - Brandon Silva; Miguel Contreras; Tezcan Ozrazgat-Baslanti; Yuanfang Ren; Ziyuan Guan; Kia Khezeli; Azra Bihorac; Parisa Rashidi (CoRR, 2023)

Transformers in Healthcare: A Survey. - Subhash Nerella; Sabyasachi Bandyopadhyay; Jiaqing Zhang; Miguel Contreras; Scott Siegel; Aysegul Bumin; Brandon Silva; Jessica Sena; Benjamin Shickel; Azra Bihorac; Kia Khezeli; Parisa Rashidi (CoRR, 2023)

APRICOT: Acuity Prediction in Intensive Care Unit (ICU): Predicting Stability, Transitions, and Life-Sustaining Therapies. - Miguel Contreras; Brandon Silva; Benjamin Shickel; Tezcan Ozrazgat-Baslanti; Yuanfang Ren; Ziyuan Guan; Sabyasachi Bandyopadhyay; Kia Khezeli; Azra Bihorac; Parisa Rashidi (CoRR, 2023)

Biological research and self-driving labs in deep space supported by artificial intelligence. - Lauren M. Sanders; Ryan T. Scott; Jason H. Yang; Amina Ann Qutub; Héctor García Martín; Daniel C. Berrios; Jaden J. A. Hastings; Jon Rask; Graham Mackintosh; Adrienne L. Hoarfrost; Stuart J. Chalk; John Kalantari; et al. (Nat. Mac. Intell., 2023)

Biomonitoring and precision health in deep space supported by artificial intelligence. - Ryan T. Scott; Lauren M. Sanders; Erik L. Antonsen; Jaden J. A. Hastings; Seung-Min Park; Graham Mackintosh; Robert J. Reynolds; Adrienne L. Hoarfrost; Aenor Sawyer; Casey S. Greene; Benjamin S. Glicksberg; Corey A. Theriot; et al. (Nat. Mac. Intell., 2023)

Transformers and large language models in healthcare: A review. - Subhash Nerella; Sabyasachi Bandyopadhyay; Jiaqing Zhang; Miguel Contreras; Scott Siegel; Aysegul Bumin; Brandon Silva; Jessica Sena; Benjamin Shickel; Azra Bihorac; Kia Khezeli; Parisa Rashidi (Artif. Intell. Medicine, 2024)

A multi-cohort study on prediction of acute brain dysfunction states using selective state space models. - Brandon Silva; Miguel Contreras; Sabyasachi Bandyopadhyay; Yuanfang Ren; Ziyuan Guan; Jeremy A. Balch; Kia Khezeli; Tezcan Ozrazgat-Baslanti; Benjamin Shickel; Azra Bihorac; Parisa Rashidi (CoRR, 2024)