3 citations · 4 across the 9 of their papers we have counts for
9 papers
CLaaS: Continual learning as a service for sample efficient online learning
Kion Fallah, Silen Naihin, Barak Widawsky +1
Deployed large language model agents must adapt to distribution shift in dynamic environments. Ideally, adaptation can be performed from accumulated agent experiences and retain pr…
Efficient Equivariant Transformer for Self-Driving Agent Modeling
Scott Xu, Dian Chen, Kelvin Wong +3
Accurately modeling agent behaviors is an important task in self-driving. It is also a task with many symmetries, such as equivariance to the order of agents and objects in the sce…
Learning to Drive via Asymmetric Self-Play
Chris Zhang, Sourav Biswas, Kelvin Wong +5
Large-scale data is crucial for learning realistic and capable driving policies. However, it can be impractical to rely on scaling datasets with real data alone. The majority of dr…
Manifold Contrastive Learning with Variational Lie Group Operators
Kion Fallah, Alec Helbling, Kyle A. Johnsen +1
Self-supervised learning of deep neural networks has become a prevalent paradigm for learning representations that transfer to a variety of downstream tasks. Similar to proposed mo…
PrefGen: Preference Guided Image Generation with Relative Attributes
Alec Helbling, Christopher J. Rozell, Matthew O'Shaughnessy +1
Deep generative models have the capacity to render high fidelity images of content like human faces. Recently, there has been substantial progress in conditionally generating image…
Active Learning of Ordinal Embeddings: A User Study on Football Data
Christoffer Loeffler, Kion Fallah, Stefano Fenu +4
Humans innately measure distance between instances in an unlabeled dataset using an unknown similarity function. Distance metrics can only serve as proxy for similarity in informat…
Variational Sparse Coding with Learned Thresholding
Kion Fallah, Christopher J. Rozell
Sparse coding strategies have been lauded for their parsimonious representations of data that leverage low dimensional structure. However, inference of these codes typically relies…
Oracle Guided Image Synthesis with Relative Queries
Alec Helbling, Christopher John Rozell, Matthew O'Shaughnessy +1
Isolating and controlling specific features in the outputs of generative models in a user-friendly way is a difficult and open-ended problem. We develop techniques that allow an or…
Learning Identity-Preserving Transformations on Data Manifolds
Marissa Connor, Kion Fallah, Christopher Rozell
Many machine learning techniques incorporate identity-preserving transformations into their models to generalize their performance to previously unseen data. These transformations…