Search Results for author: Nikos Arechiga

Found 16 papers, 4 papers with code

Stylish and Functional: Guided Interpolation Subject to Physical Constraints

no code implementations20 Dec 2024 Yan-Ying Chen, Nikos Arechiga, Chenyang Yuan, Matthew Hong, Matt Klenk, Charlene Wu

We consider the problem of generating a design inspired by two input designs, and propose a zero-shot framework toward enforcing physical, functional requirements over the generation process by leveraging a pretrained diffusion model as the backbone.

Image Generation

Parametric-ControlNet: Multimodal Control in Foundation Models for Precise Engineering Design Synthesis

no code implementations6 Dec 2024 Rui Zhou, Yanxia Zhang, Chenyang Yuan, Frank Permenter, Nikos Arechiga, Matt Klenk, Faez Ahmed

Firstly, it handles both partial and complete parametric inputs using a diffusion model that acts as a design autocomplete co-pilot, coupled with a parametric encoder to process the information.

Design Synthesis

Leveraging Language Models and Bandit Algorithms to Drive Adoption of Battery-Electric Vehicles

no code implementations30 Oct 2024 Keiichi Namikoshi, David A. Shamma, Rumen Iliev, Jingchao Fang, Alexandre Filipowicz, Candice L Hogan, Charlene Wu, Nikos Arechiga

Prior work has demonstrated that interventions for behavior must be personalized, and that the intervention that is most effective on average across a large group can result in a backlash effect that strengthens opposition among some subgroups.

On LLM Wizards: Identifying Large Language Models' Behaviors for Wizard of Oz Experiments

no code implementations10 Jul 2024 Jingchao Fang, Nikos Arechiga, Keiichi Namaoshi, Nayeli Bravo, Candice Hogan, David A. Shamma

The Wizard of Oz (WoZ) method is a widely adopted research approach where a human Wizard ``role-plays'' a not readily available technology and interacts with participants to elicit user behaviors and probe the design space.

Bridging Design Gaps: A Parametric Data Completion Approach With Graph Guided Diffusion Models

no code implementations17 Jun 2024 Rui Zhou, Chenyang Yuan, Frank Permenter, Yanxia Zhang, Nikos Arechiga, Matt Klenk, Faez Ahmed

This model functions as an AI design co-pilot, providing multiple design options for incomplete designs, which we demonstrate using the bicycle design CAD dataset.

Decision Making Graph Attention +1

A Safe Preference Learning Approach for Personalization with Applications to Autonomous Vehicles

1 code implementation30 Oct 2023 Ruya Karagulle, Nikos Arechiga, Andrew Best, Jonathan DeCastro, Necmiye Ozay

By leveraging Parametric Weighted Signal Temporal Logic (PWSTL), we formulate the problem of safety-guaranteed preference learning based on pairwise comparisons and propose an approach to solve this learning problem.

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.

Deep Learning 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

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