activity
20242026
most citedBESTOpt: A Modular, Physics-Informed Machine Learning based Building Modeling, Control and Optimization Framework

1 citations · 2 across the 6 of their papers we have counts for

collaborators

13 papers

cs.CV2026

Beyond Cropping and Rotation: Automated Evolution of Powerful Task-Specific Augmentations with Generative Models

Judah Goldfeder, Shreyes Kaliyur, Vaibhav Sourirajan +5

Data augmentation has long been a cornerstone for reducing overfitting in vision models, with methods like AutoAugment automating the design of task-specific augmentations. Recent…

eess.SY20261 cited

BESTOpt: A Modular, Physics-Informed Machine Learning based Building Modeling, Control and Optimization Framework

Zixin Jiang, Ruizhi Song, Guowen Li +5

Modern buildings are increasingly interconnected with occupancy, heating, ventilation, and air-conditioning (HVAC) systems, distributed energy resources (DERs), and power grids. Mo…

cs.CL20251 cited

A superpersuasive autonomous policy debating system

Allen Roush, Devin Gonier, John Hines +4

The capacity for highly complex, evidence-based, and strategically adaptive persuasion remains a formidable great challenge for artificial intelligence. Previous work, like IBM Pro…

cs.LG2025

Exploring Human-AI Conceptual Alignment through the Prism of Chess

Semyon Lomasov, Judah Goldfeder, Mehmet Hamza Erol +5

Do AI systems truly understand human concepts or merely mimic surface patterns? We investigate this through chess, where human creativity meets precise strategic concepts. Analyzin…

cs.LG2025

Generating Auxiliary Tasks with Reinforcement Learning

Judah Goldfeder, Matthew So, Hod Lipson

Auxiliary Learning (AL) is a form of multi-task learning in which a model trains on auxiliary tasks to boost performance on a primary objective. While AL has improved generalizatio…

cs.CV2025

Bi-Encoder Contrastive Learning for Fingerprint and Iris Biometrics

Matthew So, Judah Goldfeder, Mark Lis +1

There has been a historic assumption that the biometrics of an individual are statistically uncorrelated. We test this assumption by training Bi-Encoder networks on three verificat…