1 citations · 3 across the 51 of their papers we have counts for
77 papers
Amortized Low-Rank Adaptation for Model-Based Reinforcement Learning
Fernando Palafox, David Fridovich-Keil
World models let agents plan by predicting the consequences of their actions, but changes in the environment can make them inaccurate. We study the problem of adapting a world mode…
MixedComplementarityProblems.jl: A Fast, Batched, Open-Source Interior Point Solver for Mixed Complementarity Problems
David Fridovich-Keil
Mixed complementarity problems (MCPs) arise as the first-order optimality conditions of nonlinear programs and noncooperative games, and provide a natural formulation for multi-age…
Secure Coordination for Vertiport Sequencing in Advanced Air Mobility
Jaehan Im, Filippos Fotiadis, Ufuk Topcu +1
Advanced air mobility operations will require reliable coordination mechanisms for managing dense traffic near vertiports. However, sequencing decisions may become vulnerable when…
Controllability in preference-conditioned multi-objective reinforcement learning
Pau de las Heras Molins, Beyazit Yalcinkaya, Lasse Peters +2
Multi-objective reinforcement learning (MORL) allows a user to express preference over outcomes in terms of the relative importance of the objectives, but standard metrics cannot c…
A Flow Matching Algorithm for Many-Shot Adaptation to Unseen Distributions
Tyler Ingebrand, Ruihan Zhao, Kushagra Gupta +3
While generative modeling has achieved remarkable success on tasks like natural language-conditioned image generation, enabling model adaptation from example data points remains a…
Scaling Pretrained Representations Enables Label-Free Out-of-Distribution Detection Without Fine-Tuning
Brett Barkley, Preston Culbertson, David Fridovich-Keil
Models trained with deep learning often fail to signal when inputs fall outside their training data manifold, leading to unreliable predictions under distribution shift. Prior work…