Search Results for author: Kia Khezeli

Found 13 papers, 1 papers with code

AI-Enhanced Intensive Care Unit: Revolutionizing Patient Care with Pervasive Sensing

no code implementations11 Mar 2023 Subhash Nerella, Ziyuan Guan, Scott Siegel, Jiaqing Zhang, Kia Khezeli, Azra Bihorac, Parisa Rashidi

However, the extent of patient monitoring in the ICU is limited due to time constraints and the workload on healthcare providers.

End-to-End Machine Learning Framework for Facial AU Detection in Intensive Care Units

no code implementations12 Nov 2022 Subhash Nerella, Kia Khezeli, Andrea Davidson, Patrick Tighe, Azra Bihorac, Parisa Rashidi

In this work, we evaluated two vision transformer models, namely ViT and SWIN, for AU detection on our Pain-ICU dataset and also external datasets.

Universally Rank Consistent Ordinal Regression in Neural Networks

2 code implementations14 Oct 2021 Garrett Jenkinson, Gavin R. Oliver, Kia Khezeli, John Kalantari, Eric W. Klee

Despite the pervasiveness of ordinal labels in supervised learning, it remains common practice in deep learning to treat such problems as categorical classification using the categorical cross entropy loss.

Binary Classification regression

On Invariance Penalties for Risk Minimization

no code implementations17 Jun 2021 Kia Khezeli, Arno Blaas, Frank Soboczenski, Nicholas Chia, John Kalantari

We discuss the role of its eigenvalues in the relationship between the risk and the invariance penalty, and demonstrate that it is ill-conditioned for said counterexamples.

Domain Generalization

On Information Gain and Regret Bounds in Gaussian Process Bandits

no code implementations15 Sep 2020 Sattar Vakili, Kia Khezeli, Victor Picheny

For the Mat\'ern family of kernels, where the lower bounds on $\gamma_T$, and regret under the frequentist setting, are known, our results close a huge polynomial in $T$ gap between the upper and lower bounds (up to logarithmic in $T$ factors).

Safe Linear Stochastic Bandits

no code implementations21 Nov 2019 Kia Khezeli, Eilyan Bitar

We introduce the safe linear stochastic bandit framework---a generalization of linear stochastic bandits---where, in each stage, the learner is required to select an arm with an expected reward that is no less than a predetermined (safe) threshold with high probability.

An Online Learning Approach to Buying and Selling Demand Response

no code implementations23 Jul 2017 Kia Khezeli, Eilyan Bitar

Assuming that both the parameters of the demand curve and the distribution of the random shocks are initially unknown to the aggregator, we investigate the extent to which the aggregator might dynamically adapt its offered prices and forward contracts to maximize its expected profit over a time window of $T$ days.

Risk-Sensitive Learning and Pricing for Demand Response

no code implementations21 Nov 2016 Kia Khezeli, Eilyan Bitar

Assuming that both the parameters of the demand curve and the distribution of the random shocks are initially unknown to the utility, we investigate the extent to which the utility might dynamically adjust its offered prices to maximize its cumulative risk-sensitive payoff over a finite number of $T$ days.

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