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Paul Friedrich

4 accepted papers

2026

Beyond Uniformity: Regularizing Implicit Neural Representations through a Lipschitz Lens

ICLR 2026poster

Implicit Neural Representations (INRs) have shown great promise in solving inverse problems, but their lack of inherent regularization often leads to a trade-off between expressiveness and smoothness. While Lipschitz continuity presents a principled form of implicit regularization, it is often appli…

Cited by 0SourceScholar
2026

Fine-tuning Behavioral Cloning Policies with Preference‑Based Reinforcement Learning

ICLR 2026poster

Deploying reinforcement learning (RL) in robotics, industry, and health care is blocked by two obstacles: the difficulty of specifying accurate rewards and the risk of unsafe, data-hungry exploration. We address this by proposing a two-stage framework that first learns a safe initial policy from a r…

Cited by 0SourcecodeScholar
2026

Optimizing Rank for High-Fidelity Implicit Neural Representations

ICML 2026poster

Implicit Neural Representations (INRs) based on vanilla Multi-Layer Perceptrons (MLPs) are widely believed to be incapable of representing high-frequency content. This has directed research efforts towards architectural interventions, such as coordinate embeddings or specialized activation functions…

Cited by 0SourceScholar
2024

Scalable Mechanism Design for Multi-Agent Path Finding

IJCAI 2024poster

Multi-Agent Path Finding (MAPF) involves determining paths for multiple agents to travel simultaneously and collision-free through a shared area toward given goal locations. This problem is computationally complex, especially when dealing with large numbers of agents, as is common in realistic appli…