23 citations · 101 across the 39 of their papers we have counts for
7 papers · 1 filter
Diffusion Models are Secretly Exchangeable: Parallelizing DDPMs via Autospeculation
Hengyuan Hu, Aniket Das, Dorsa Sadigh +1
Denoising Diffusion Probabilistic Models (DDPMs) have emerged as powerful tools for generative modeling. However, their sequential computation requirements lead to significant infe…
Batch Active Learning of Reward Functions from Human Preferences
Erdem Bıyık, Nima Anari, Dorsa Sadigh
Data generation and labeling are often expensive in robot learning. Preference-based learning is a concept that enables reliable labeling by querying users with preference question…
Parallel Sampling of Diffusion Models
Andy Shih, Suneel Belkhale, Stefano Ermon +2
Diffusion models are powerful generative models but suffer from slow sampling, often taking 1000 sequential denoising steps for one sample. As a result, considerable efforts have b…
Learning Multimodal Rewards from Rankings
Vivek Myers, Erdem Bıyık, Nima Anari +1
Learning from human feedback has shown to be a useful approach in acquiring robot reward functions. However, expert feedback is often assumed to be drawn from an underlying unimoda…
From Sampling to Optimization on Discrete Domains with Applications to Determinant Maximization
Nima Anari, Thuy-Duong Vuong
We show a connection between sampling and optimization on discrete domains. For a family of distributions defined on size subsets of a ground set of elements that is closed…
Batch Active Learning Using Determinantal Point Processes
Erdem Bıyık, Kenneth Wang, Nima Anari +1
Data collection and labeling is one of the main challenges in employing machine learning algorithms in a variety of real-world applications with limited data. While active learning…