3 papers indexed
Anirudh jain, Sakshi Varshney, Samuel Kaski, Vikas Garg
Flow-based models have established state-of-the-art performance in generative modeling across domains, but are hard to interpret due to their complex latent embeddings. In particular, the entanglement of generative factors in the latent space hinders controlled generation. We cir…
Haripriya Harikumar, Sammie Katt, Yasir Zubayr Barlas, Samuel Kaski
Scientific experiments are often designed to maximize information gain, yet in many applications the primary objective is to support reliable downstream decision-making. Existing decision-aware experimental design and active learning methods typically assume well-specified outcom…
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 entirely or partially unavailable. We propose Generative Meta-Learning with Human Feedback (GMHF), a novel…