Search Results for author: Nikos Arechiga

Found 10 papers, 3 papers with code

A Preference Learning Approach to Develop Safe and Personalizable Autonomous Vehicles

no code implementations30 Oct 2023 Ruya Karagulle, Nikos Arechiga, Andrew Best, Jonathan DeCastro, Necmiye Ozay

Our approach incorporates priority ordering of signal temporal logic (STL) formulas, describing traffic rules, into a learning framework.

Autonomous Vehicles

Training Towards Critical Use: Learning to Situate AI Predictions Relative to Human Knowledge

no code implementations30 Aug 2023 Anna Kawakami, Luke Guerdan, Yanghuidi Cheng, Matthew Lee, Scott Carter, Nikos Arechiga, Kate Glazko, Haiyi Zhu, Kenneth Holstein

A growing body of research has explored how to support humans in making better use of AI-based decision support, including via training and onboarding.

Decision Making

Surrogate Modeling of Car Drag Coefficient with Depth and Normal Renderings

no code implementations26 May 2023 Binyang Song, Chenyang Yuan, Frank Permenter, Nikos Arechiga, Faez Ahmed

Generative AI models have made significant progress in automating the creation of 3D shapes, which has the potential to transform car design.

Image Generation

Second-Order Sensitivity Analysis for Bilevel Optimization

3 code implementations4 May 2022 Robert Dyro, Edward Schmerling, Nikos Arechiga, Marco Pavone

Many existing approaches to bilevel optimization employ first-order sensitivity analysis, based on the implicit function theorem (IFT), for the lower problem to derive a gradient of the lower problem solution with respect to its parameters; this IFT gradient is then used in a first-order optimization method for the upper problem.

Bilevel Optimization

Understanding and Shifting Preferences for Battery Electric Vehicles

no code implementations9 Feb 2022 Nikos Arechiga, Francine Chen, Rumen Iliev, Emily Sumner, Scott Carter, Alex Filipowicz, Nayeli Bravo, Monica Van, Kate Glazko, Kalani Murakami, Laurent Denoue, Candice Hogan, Katharine Sieck, Charlene Wu, Kent Lyons

In this work, we focus on methods for personalizing interventions based on an individual's demographics to shift the preferences of consumers to be more positive towards Battery Electric Vehicles (BEVs).

Reinforcement Learning (RL)

Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization

1 code implementation ICLR 2021 Kaidi Cao, Yining Chen, Junwei Lu, Nikos Arechiga, Adrien Gaidon, Tengyu Ma

Real-world large-scale datasets are heteroskedastic and imbalanced -- labels have varying levels of uncertainty and label distributions are long-tailed.

Image Classification

Better AI through Logical Scaffolding

no code implementations12 Sep 2019 Nikos Arechiga, Jonathan DeCastro, Soonho Kong, Karen Leung

We describe the concept of logical scaffolds, which can be used to improve the quality of software that relies on AI components.

Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss

7 code implementations NeurIPS 2019 Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, Tengyu Ma

Deep learning algorithms can fare poorly when the training dataset suffers from heavy class-imbalance but the testing criterion requires good generalization on less frequent classes.

Long-tail learning with class descriptors Test

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