Search Results for author: Shengjia Zhao

Found 37 papers, 12 papers with code

GPT-4 Technical Report

9 code implementations Preprint 2023 OpenAI, :, Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, Red Avila, Igor Babuschkin, Suchir Balaji, Valerie Balcom, Paul Baltescu, Haiming Bao, Mohammad Bavarian, Jeff Belgum, Irwan Bello, Jake Berdine, Gabriel Bernadett-Shapiro, Christopher Berner, Lenny Bogdonoff, Oleg Boiko, Madelaine Boyd, Anna-Luisa Brakman, Greg Brockman, Tim Brooks, Miles Brundage, Kevin Button, Trevor Cai, Rosie Campbell, Andrew Cann, Brittany Carey, Chelsea Carlson, Rory Carmichael, Brooke Chan, Che Chang, Fotis Chantzis, Derek Chen, Sully Chen, Ruby Chen, Jason Chen, Mark Chen, Ben Chess, Chester Cho, Casey Chu, Hyung Won Chung, Dave Cummings, Jeremiah Currier, Yunxing Dai, Cory Decareaux, Thomas Degry, Noah Deutsch, Damien Deville, Arka Dhar, David Dohan, Steve Dowling, Sheila Dunning, Adrien Ecoffet, Atty Eleti, Tyna Eloundou, David Farhi, Liam Fedus, Niko Felix, Simón Posada Fishman, Juston Forte, Isabella Fulford, Leo Gao, Elie Georges, Christian Gibson, Vik Goel, Tarun Gogineni, Gabriel Goh, Rapha Gontijo-Lopes, Jonathan Gordon, Morgan Grafstein, Scott Gray, Ryan Greene, Joshua Gross, Shixiang Shane Gu, Yufei Guo, Chris Hallacy, Jesse Han, Jeff Harris, Yuchen He, Mike Heaton, Johannes Heidecke, Chris Hesse, Alan Hickey, Wade Hickey, Peter Hoeschele, Brandon Houghton, Kenny Hsu, Shengli Hu, Xin Hu, Joost Huizinga, Shantanu Jain, Shawn Jain, Joanne Jang, Angela Jiang, Roger Jiang, Haozhun Jin, Denny Jin, Shino Jomoto, Billie Jonn, Heewoo Jun, Tomer Kaftan, Łukasz Kaiser, Ali Kamali, Ingmar Kanitscheider, Nitish Shirish Keskar, Tabarak Khan, Logan Kilpatrick, Jong Wook Kim, Christina Kim, Yongjik Kim, Jan Hendrik Kirchner, Jamie Kiros, Matt Knight, Daniel Kokotajlo, Łukasz Kondraciuk, Andrew Kondrich, Aris Konstantinidis, Kyle Kosic, Gretchen Krueger, Vishal Kuo, Michael Lampe, Ikai Lan, Teddy Lee, Jan Leike, Jade Leung, Daniel Levy, Chak Ming Li, Rachel Lim, Molly Lin, Stephanie Lin, Mateusz Litwin, Theresa Lopez, Ryan Lowe, Patricia Lue, Anna Makanju, Kim Malfacini, Sam Manning, Todor Markov, Yaniv Markovski, Bianca Martin, Katie Mayer, Andrew Mayne, Bob McGrew, Scott Mayer McKinney, Christine McLeavey, Paul McMillan, Jake McNeil, David Medina, Aalok Mehta, Jacob Menick, Luke Metz, Andrey Mishchenko, Pamela Mishkin, Vinnie Monaco, Evan Morikawa, Daniel Mossing, Tong Mu, Mira Murati, Oleg Murk, David Mély, Ashvin Nair, Reiichiro Nakano, Rajeev Nayak, Arvind Neelakantan, Richard Ngo, Hyeonwoo Noh, Long Ouyang, Cullen O'Keefe, Jakub Pachocki, Alex Paino, Joe Palermo, Ashley Pantuliano, Giambattista Parascandolo, Joel Parish, Emy Parparita, Alex Passos, Mikhail Pavlov, Andrew Peng, Adam Perelman, Filipe de Avila Belbute Peres, Michael Petrov, Henrique Ponde de Oliveira Pinto, Michael, Pokorny, Michelle Pokrass, Vitchyr H. Pong, Tolly Powell, Alethea Power, Boris Power, Elizabeth Proehl, Raul Puri, Alec Radford, Jack Rae, Aditya Ramesh, Cameron Raymond, Francis Real, Kendra Rimbach, Carl Ross, Bob Rotsted, Henri Roussez, Nick Ryder, Mario Saltarelli, Ted Sanders, Shibani Santurkar, Girish Sastry, Heather Schmidt, David Schnurr, John Schulman, Daniel Selsam, Kyla Sheppard, Toki Sherbakov, Jessica Shieh, Sarah Shoker, Pranav Shyam, Szymon Sidor, Eric Sigler, Maddie Simens, Jordan Sitkin, Katarina Slama, Ian Sohl, Benjamin Sokolowsky, Yang song, Natalie Staudacher, Felipe Petroski Such, Natalie Summers, Ilya Sutskever, Jie Tang, Nikolas Tezak, Madeleine B. Thompson, Phil Tillet, Amin Tootoonchian, Elizabeth Tseng, Preston Tuggle, Nick Turley, Jerry Tworek, Juan Felipe Cerón Uribe, Andrea Vallone, Arun Vijayvergiya, Chelsea Voss, Carroll Wainwright, Justin Jay Wang, Alvin Wang, Ben Wang, Jonathan Ward, Jason Wei, CJ Weinmann, Akila Welihinda, Peter Welinder, Jiayi Weng, Lilian Weng, Matt Wiethoff, Dave Willner, Clemens Winter, Samuel Wolrich, Hannah Wong, Lauren Workman, Sherwin Wu, Jeff Wu, Michael Wu, Kai Xiao, Tao Xu, Sarah Yoo, Kevin Yu, Qiming Yuan, Wojciech Zaremba, Rowan Zellers, Chong Zhang, Marvin Zhang, Shengjia Zhao, Tianhao Zheng, Juntang Zhuang, William Zhuk, Barret Zoph

We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs.

 Ranked #1 on Only Connect Walls Dataset Task 1 (Grouping) on OCW (using extra training data)

Arithmetic Reasoning Bug fixing +9

Online Distribution Shift Detection via Recency Prediction

no code implementations17 Nov 2022 Rachel Luo, Rohan Sinha, Yixiao Sun, Ali Hindy, Shengjia Zhao, Silvio Savarese, Edward Schmerling, Marco Pavone

When deploying modern machine learning-enabled robotic systems in high-stakes applications, detecting distribution shift is critical.

Generalizing Bayesian Optimization with Decision-theoretic Entropies

no code implementations4 Oct 2022 Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon

Bayesian optimization (BO) is a popular method for efficiently inferring optima of an expensive black-box function via a sequence of queries.

Bayesian Optimization Decision Making

Modular Conformal Calibration

no code implementations23 Jun 2022 Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon

We introduce a versatile class of algorithms for recalibration in regression that we call Modular Conformal Calibration (MCC).

regression

Low-Degree Multicalibration

no code implementations2 Mar 2022 Parikshit Gopalan, Michael P. Kim, Mihir Singhal, Shengjia Zhao

This stringent notion -- that predictions be well-calibrated across a rich class of intersecting subpopulations -- provides its strong guarantees at a cost: the computational and sample complexity of learning multicalibrated predictors are high, and grow exponentially with the number of class labels.

Fairness

Reliable Decisions with Threshold Calibration

no code implementations NeurIPS 2021 Roshni Sahoo, Shengjia Zhao, Alyssa Chen, Stefano Ermon

We propose a stronger notion of calibration called threshold calibration, which is exactly the condition required to ensure that decision loss is predicted accurately for threshold decisions.

Scheduling

Provably Calibrated Regression Under Distribution Drift

no code implementations29 Sep 2021 Shengjia Zhao, Yusuke Tashiro, Danny Tse, Stefano Ermon

Accurate uncertainty quantification is a key building block of trustworthy machine learning systems.

regression Time Series +2

H-Entropy Search: Generalizing Bayesian Optimization with a Decision-theoretic Uncertainty Measure

no code implementations29 Sep 2021 Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon

For special cases of the loss and design space, we develop gradient-based methods to efficiently optimize our proposed family of acquisition functions, and demonstrate that the resulting BO procedure shows strong empirical performance on a diverse set of optimization tasks.

Bayesian Optimization

Sample-Efficient Safety Assurances using Conformal Prediction

no code implementations28 Sep 2021 Rachel Luo, Shengjia Zhao, Jonathan Kuck, Boris Ivanovic, Silvio Savarese, Edward Schmerling, Marco Pavone

When deploying machine learning models in high-stakes robotics applications, the ability to detect unsafe situations is crucial.

Conformal Prediction Robotic Grasping

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

no code implementations NeurIPS 2021 Shengjia Zhao, Michael P. Kim, Roshni Sahoo, Tengyu Ma, Stefano Ermon

In this work, we introduce a new notion -- \emph{decision calibration} -- that requires the predicted distribution and true distribution to be ``indistinguishable'' to a set of downstream decision-makers.

Decision Making

Local Calibration: Metrics and Recalibration

no code implementations22 Feb 2021 Rachel Luo, Aadyot Bhatnagar, Yu Bai, Shengjia Zhao, Huan Wang, Caiming Xiong, Silvio Savarese, Stefano Ermon, Edward Schmerling, Marco Pavone

In this work, we propose the local calibration error (LCE) to span the gap between average and individual reliability.

Decision Making Fairness

H-divergence: A Decision-Theoretic Discrepancy Measure for Two Sample Tests

no code implementations1 Jan 2021 Shengjia Zhao, Abhishek Sinha, Yutong He, Aidan Perreault, Jiaming Song, Stefano Ermon

Based on ideas from decision theory, we investigate a new class of discrepancies that are based on the optimal decision loss.

Vocal Bursts Valence Prediction

Privacy Preserving Recalibration under Domain Shift

no code implementations21 Aug 2020 Rachel Luo, Shengjia Zhao, Jiaming Song, Jonathan Kuck, Stefano Ermon, Silvio Savarese

In an extensive empirical study, we find that our algorithm improves calibration on domain-shift benchmarks under the constraints of differential privacy.

Privacy Preserving

Individual Calibration with Randomized Forecasting

no code implementations ICML 2020 Shengjia Zhao, Tengyu Ma, Stefano Ermon

We show that calibration for individual samples is possible in the regression setup if the predictions are randomized, i. e. outputting randomized credible intervals.

Decision Making Fairness +1

A Framework for Sample Efficient Interval Estimation with Control Variates

1 code implementation18 Jun 2020 Shengjia Zhao, Christopher Yeh, Stefano Ermon

We consider the problem of estimating confidence intervals for the mean of a random variable, where the goal is to produce the smallest possible interval for a given number of samples.

regression

Permutation Invariant Graph Generation via Score-Based Generative Modeling

1 code implementation2 Mar 2020 Chenhao Niu, Yang song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon

In particular, we design a permutation equivariant, multi-channel graph neural network to model the gradient of the data distribution at the input graph (a. k. a., the score function).

Graph Generation

Approximating Human Judgment of Generated Image Quality

no code implementations30 Nov 2019 Y. Alex Kolchinski, Sharon Zhou, Shengjia Zhao, Mitchell Gordon, Stefano Ermon

Generative models have made immense progress in recent years, particularly in their ability to generate high quality images.

Domain Adaptive Imitation Learning

1 code implementation ICML 2020 Kuno Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon

We formalize the Domain Adaptive Imitation Learning (DAIL) problem, which is a unified framework for imitation learning in the presence of viewpoint, embodiment, and dynamics mismatch.

Imitation Learning

Towards Certified Defense for Unrestricted Adversarial Attacks

no code implementations25 Sep 2019 Shengjia Zhao, Yang song, Stefano Ermon

Our defense draws inspiration from differential privacy, and is based on intentionally adding noise to the classifier's outputs to limit the attacker's knowledge about the parameters.

Adversarial Attack

Cross Domain Imitation Learning

no code implementations25 Sep 2019 Kun Ho Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon

Informally, CDIL is the process of learning how to perform a task optimally, given demonstrations of the task in a distinct domain.

Imitation Learning

Learning Neural PDE Solvers with Convergence Guarantees

no code implementations ICLR 2019 Jun-Ting Hsieh, Shengjia Zhao, Stephan Eismann, Lucia Mirabella, Stefano Ermon

Partial differential equations (PDEs) are widely used across the physical and computational sciences.

Learning Controllable Fair Representations

3 code implementations11 Dec 2018 Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon

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 of the data.

Fairness

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

2 code implementations18 Jun 2018 Shengjia Zhao, Jiaming Song, Stefano Ermon

A large number of objectives have been proposed to train latent variable generative models.

Amortized Inference Regularization

no code implementations NeurIPS 2018 Rui Shu, Hung H. Bui, Shengjia Zhao, Mykel J. Kochenderfer, Stefano Ermon

In this paper, we leverage the fact that VAEs rely on amortized inference and propose techniques for amortized inference regularization (AIR) that control the smoothness of the inference model.

Density Estimation Representation Learning

Learning Hierarchical Features from Deep Generative Models

no code implementations ICML 2017 Shengjia Zhao, Jiaming Song, Stefano Ermon

In this paper, we prove that hierarchical latent variable models do not take advantage of the hierarchical structure when trained with existing variational methods, and provide some limitations on the kind of features existing models can learn.

A-NICE-MC: Adversarial Training for MCMC

3 code implementations NeurIPS 2017 Jiaming Song, Shengjia Zhao, Stefano Ermon

We propose A-NICE-MC, a novel method to train flexible parametric Markov chain kernels to produce samples with desired properties.

InfoVAE: Information Maximizing Variational Autoencoders

6 code implementations7 Jun 2017 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.

On the Limits of Learning Representations with Label-Based Supervision

no code implementations7 Mar 2017 Jiaming Song, Russell Stewart, Shengjia Zhao, Stefano Ermon

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.

Representation Learning Transfer Learning

Towards Deeper Understanding of Variational Autoencoding Models

2 code implementations28 Feb 2017 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.

Learning Hierarchical Features from Generative Models

3 code implementations27 Feb 2017 Shengjia Zhao, Jiaming Song, Stefano Ermon

In this paper, we prove that hierarchical latent variable models do not take advantage of the hierarchical structure when trained with existing variational methods, and provide some limitations on the kind of features existing models can learn.

Adaptive Concentration Inequalities for Sequential Decision Problems

no code implementations NeurIPS 2016 Shengjia Zhao, Enze Zhou, Ashish Sabharwal, Stefano Ermon

A key challenge in sequential decision problems is to determine how many samples are needed for an agent to make reliable decisions with good probabilistic guarantees.

Two-sample testing

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