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Found 11 papers, 4 papers with code
Using LLMs to Model the Beliefs and Preferences of Targeted Populations
We consider the problem of aligning a large language model (LLM) to model the preferences of a human population.
A Safe Preference Learning Approach for Personalization with Applications to Autonomous Vehicles
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.
Training Towards Critical Use: Learning to Situate AI Predictions Relative to Human Knowledge
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.
Drag-guided diffusion models for vehicle image generation
Denoising diffusion models trained at web-scale have revolutionized image generation.
Surrogate Modeling of Car Drag Coefficient with Depth and Normal Renderings
Generative AI models have made significant progress in automating the creation of 3D shapes, which has the potential to transform car design.
Second-Order Sensitivity Analysis for Bilevel Optimization
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.
Understanding and Shifting Preferences for Battery Electric Vehicles
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).
Accelerating Understanding of Scientific Experiments with End to End Symbolic Regression
We develop a deep neural network (MACSYMA) to address the symbolic regression problem as an end-to-end supervised learning problem.
Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization
Real-world large-scale datasets are heteroskedastic and imbalanced -- labels have varying levels of uncertainty and label distributions are long-tailed.
Ranked #11 on
Image Classification
on WebVision-1000
Better AI through Logical Scaffolding
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
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.
Ranked #4 on
Long-tail learning with class descriptors
on CUB-LT
