10 papers
Parameter-Free and Group Conditional Online Conformal Prediction
Beepul Bharti, Ambar Pal, Jacopo Teneggi +1
Uncertainty quantification (UQ) is critical for the deployment of machine learning predictors in real-world scenarios where the data distribution may shift over time (i.e., data ma…
Learning Affine-Equivariant Proximal Operators
Oriel Savir, Zhenghan Fang, Jeremias Sulam
Proximal operators are fundamental across many applications in signal processing and machine learning, including solving ill-posed inverse problems. Recent work has introduced Lear…
Global Sequential Testing for Multi-Stream Auditing
Beepul Bharti, Ambar Pal, Jeremias Sulam
Across many risk-sensitive areas, it is critical to continuously audit machine learning systems as we receive more data to quickly determine if they are performing as designed. Thi…
ProxT2I: Efficient Reward-Guided Text-to-Image Generation via Proximal Diffusion
Zhenghan Fang, Jian Zheng, Qiaozi Gao +2
Diffusion models have emerged as a dominant paradigm for generative modeling across a wide range of domains, including prompt-conditional generation. The vast majority of samplers,…
Learning Regularization Functionals for Inverse Problems: A Comparative Study
Johannes Hertrich, Hok Shing Wong, Alexander Denker +16
In recent years, a variety of learned regularization frameworks for solving inverse problems in imaging have emerged. These offer flexible modeling together with mathematical insig…
Beyond Scores: Proximal Diffusion Models
Zhenghan Fang, Mateo Díaz, Sam Buchanan +1
Diffusion models have quickly become some of the most popular and powerful generative models for high-dimensional data. The key insight that enabled their development was the reali…