357 citations · 992 across the 28 of their papers we have counts for
14 papers · 1 filter
Modular Conformal Calibration
Charles Marx, Shengjia Zhao, Willie Neiswanger +1
Uncertainty estimates must be calibrated (i.e., accurate) and sharp (i.e., informative) in order to be useful. This has motivated a variety of methods for recalibration, which use…
Low-Degree Multicalibration
Parikshit Gopalan, Michael P. Kim, Mihir Singhal +1
Introduced as a notion of algorithmic fairness, multicalibration has proved to be a powerful and versatile concept with implications far beyond its original intent. This stringent…
Improved Autoregressive Modeling with Distribution Smoothing
Chenlin Meng, Jiaming Song, Yang Song +2
While autoregressive models excel at image compression, their sample quality is often lacking. Although not realistic, generated images often have high likelihood according to the…
Local Calibration: Metrics and Recalibration
Rachel Luo, Aadyot Bhatnagar, Yu Bai +7
Probabilistic classifiers output confidence scores along with their predictions, and these confidence scores should be calibrated, i.e., they should reflect the reliability of the…
Privacy Preserving Recalibration under Domain Shift
Rachel Luo, Shengjia Zhao, Jiaming Song +3
Classifiers deployed in high-stakes real-world applications must output calibrated confidence scores, i.e. their predicted probabilities should reflect empirical frequencies. Recal…
Permutation Invariant Graph Generation via Score-Based Generative Modeling
Chenhao Niu, Yang Song, Jiaming Song +3
Learning generative models for graph-structured data is challenging because graphs are discrete, combinatorial, and the underlying data distribution is invariant to the ordering of…
A Theory of Usable Information Under Computational Constraints
Yilun Xu, Shengjia Zhao, Jiaming Song +2
We propose a new framework for reasoning about information in complex systems. Our foundation is based on a variational extension of Shannon's information theory that takes into ac…
Domain Adaptive Imitation Learning
Kuno Kim, Yihong Gu, Jiaming Song +2
We study the question of how to imitate tasks across domains with discrepancies such as embodiment, viewpoint, and dynamics mismatch. Many prior works require paired, aligned demon…
Learning Controllable Fair Representations
Jiaming Song, Pratyusha Kalluri, Aditya Grover +2
Learning data representations that are transferable and are fair with respect to certain protected attributes is crucial to reducing unfair decisions while preserving the utility o…
Bias and Generalization in Deep Generative Models: An Empirical Study
Shengjia Zhao, Hongyu Ren, Arianna Yuan +3
In high dimensional settings, density estimation algorithms rely crucially on their inductive bias. Despite recent empirical success, the inductive bias of deep generative models i…
InfoVAE: Information Maximizing Variational Autoencoders
Shengjia Zhao, Jiaming Song, Stefano Ermon
A key advance in learning generative models is the use of amortized inference distributions that are jointly trained with the models. We find that existing training objectives for…
On the Limits of Learning Representations with Label-Based Supervision
Jiaming Song, Russell Stewart, Shengjia Zhao +1
Advances in neural network based classifiers have transformed automatic feature learning from a pipe dream of stronger AI to a routine and expected property of practical systems. S…
Towards Deeper Understanding of Variational Autoencoding Models
Shengjia Zhao, Jiaming Song, Stefano Ermon
We propose a new family of optimization criteria for variational auto-encoding models, generalizing the standard evidence lower bound. We provide conditions under which they recove…
Learning Hierarchical Features from Generative Models
Shengjia Zhao, Jiaming Song, Stefano Ermon
Deep neural networks have been shown to be very successful at learning feature hierarchies in supervised learning tasks. Generative models, on the other hand, have benefited less f…