Shengjia Zhao | DeepAI

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Featured Co-authors

Caiming Xiong
210 publications
Stefano Ermon
168 publications
Marco Pavone
151 publications
Mykel J. Kochenderfer
144 publications
Silvio Savarese
130 publications
Tengyu Ma
97 publications
Huan Wang
82 publications
Yu Bai
51 publications
Jiaming Song
50 publications
Yang Song
49 publications
Noah Goodman
48 publications

∙ 11/17/2022

Online Distribution Shift Detection via Recency Prediction

When deploying modern machine learning-enabled robotic systems in high-s...

0 Rachel Luo, et al. ∙

∙ 03/02/2022

Low-Degree Multicalibration

Introduced as a notion of algorithmic fairness, multicalibration has pro...

0 Parikshit Gopalan, et al. ∙

∙ 09/28/2021

Sample-Efficient Safety Assurances using Conformal Prediction

When deploying machine learning models in high-stakes robotics applicati...

0 Rachel Luo, et al. ∙

∙ 07/12/2021

Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration

When facing uncertainty, decision-makers want predictions they can trust...

0 Shengjia Zhao, et al. ∙

∙ 02/22/2021

Localized Calibration: Metrics and Recalibration

Probabilistic classifiers output confidence scores along with their pred...

1 Rachel Luo, et al. ∙

∙ 11/15/2020

Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration

Decision makers often need to rely on imperfect probabilistic forecasts....

0 Shengjia Zhao, et al. ∙

∙ 08/21/2020

Privacy Preserving Recalibration under Domain Shift

Classifiers deployed in high-stakes real-world applications must output ...

4 Rachel Luo, et al. ∙

∙ 06/18/2020

Individual Calibration with Randomized Forecasting

Machine learning applications often require calibrated predictions, e.g....

0 Shengjia Zhao, et al. ∙

∙ 06/18/2020

A Framework for Sample Efficient Interval Estimation with Control Variates

We consider the problem of estimating confidence intervals for the mean ...

5 Shengjia Zhao, et al. ∙

∙ 03/02/2020

Permutation Invariant Graph Generation via Score-Based Generative Modeling

Learning generative models for graph-structured data is challenging beca...

18 Chenhao Niu, et al. ∙

∙ 02/25/2020

A Theory of Usable Information Under Computational Constraints

We propose a new framework for reasoning about information in complex sy...

8 Yilun Xu, et al. ∙

∙ 09/30/2019

Cross Domain Imitation Learning

We study the question of how to imitate tasks across domains with discre...

41 Kun ho Kim, et al. ∙

∙ 06/04/2019

Learning Neural PDE Solvers with Convergence Guarantees

Partial differential equations (PDEs) are widely used across the physica...

13 Jun-Ting Hsieh, et al. ∙

∙ 12/11/2018

Learning Controllable Fair Representations

Learning data representations that are transferable and fair with respec...

12 Jiaming Song, et al. ∙

∙ 11/08/2018

Bias and Generalization in Deep Generative Models: An Empirical Study

In high dimensional settings, density estimation algorithms rely crucial...

6 Shengjia Zhao, et al. ∙

∙ 06/18/2018

The Information Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Models

A variety of learning objectives have been proposed for training latent ...

2 Shengjia Zhao, et al. ∙

∙ 05/23/2018

Amortized Inference Regularization

The variational autoencoder (VAE) is a popular model for density estimat...

12 Rui Shu, et al. ∙

∙ 06/23/2017

A-NICE-MC: Adversarial Training for MCMC

Existing Markov Chain Monte Carlo (MCMC) methods are either based on gen...

0 Jiaming Song, et al. ∙

∙ 06/07/2017

InfoVAE: Information Maximizing Variational Autoencoders

It has been previously observed that variational autoencoders tend to ig...

0 Shengjia Zhao, et al. ∙

∙ 03/07/2017

On the Limits of Learning Representations with Label-Based Supervision

Advances in neural network based classifiers have transformed automatic ...

0 Jiaming Song, et al. ∙

∙ 02/28/2017

Towards Deeper Understanding of Variational Autoencoding Models

We propose a new family of optimization criteria for variational auto-en...

0 Shengjia Zhao, et al. ∙

∙ 02/27/2017

Learning Hierarchical Features from Generative Models

Deep neural networks have been shown to be very successful at learning f...

0 Shengjia Zhao, et al. ∙

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