15 papers
Human-Machine Collaboration on Generative Meta-Learning: Model and Algorithm
Midhun Parakkal Unni, Samuel Kaski
Generalizing machine learning models to environments that differ from their training distribution remains a critical hurdle, particularly when data from the target domain is entire…
Softly Constrained Denoisers for Diffusion Models Applied to Partial Differential Equations
Victor M. Yeom-Song, Severi Rissanen, Arno Solin +2
Diffusion models have become a powerful generative prior for solutions of partial differential equations (PDEs). Existing approaches enforce physical constraints either by adding t…
Gradient Regularized Natural Gradients
Satya Prakash Dash, Hossein Abdi, Wei Pan +2
Gradient regularization (GR) has been shown to improve the generalizability of trained models. While Natural Gradient Descent has been shown to accelerate optimization in the initi…
Concept-based Adversarial Attack: a Probabilistic Perspective
Andi Zhang, Xuan Ding, Steven McDonagh +1
We propose a concept-based adversarial attack framework that extends beyond single-image perturbations by adopting a probabilistic perspective. Rather than modifying a single image…
Rank-1 Approximation of Inverse Fisher for Natural Policy Gradients in Deep Reinforcement Learning
Yingxiao Huo, Satya Prakash Dash, Radu Stoican +2
Natural gradients have long been studied in deep reinforcement learning due to their fast convergence properties and covariant weight updates. However, computing natural gradients…
ARCADE: Adaptive Robot Control with Online Changepoint-Aware Bayesian Dynamics Learning
Rishabh Dev Yadav, Avirup Das, Hongyu Song +2
Real-world robots must operate under evolving dynamics caused by changing operating conditions, external disturbances, and unmodeled effects. These may appear as gradual drifts, tr…