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 Long-Context Understanding on Ada-LEval (BestAnswer)
no code implementations • 17 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.
no code implementations • 4 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.
no code implementations • 23 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).
no code implementations • 2 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.
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.
no code implementations • 29 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.
no code implementations • 29 Sep 2021 • Shengjia Zhao, Yusuke Tashiro, Danny Tse, Stefano Ermon
Accurate uncertainty quantification is a key building block of trustworthy machine learning systems.
no code implementations • ICLR 2022 • Shengjia Zhao, Abhishek Sinha, Yutong He, Aidan Perreault, Jiaming Song, Stefano Ermon
Measuring the discrepancy between two probability distributions is a fundamental problem in machine learning and statistics.
no code implementations • 28 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.
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.
no code implementations • ICLR 2021 • Chenlin Meng, Jiaming Song, Yang song, Shengjia Zhao, Stefano Ermon
While autoregressive models excel at image compression, their sample quality is often lacking.
no code implementations • 22 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.
no code implementations • 1 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.
no code implementations • 15 Nov 2020 • Shengjia Zhao, Stefano Ermon
Decision makers often need to rely on imperfect probabilistic forecasts.
no code implementations • 21 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.
1 code implementation • 18 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.
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.
1 code implementation • 2 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).
1 code implementation • ICLR 2020 • Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, Stefano Ermon
We propose a new framework for reasoning about information in complex systems.
no code implementations • 30 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.
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.
no code implementations • 25 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.
no code implementations • 25 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.
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.
3 code implementations • 11 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.
2 code implementations • NeurIPS 2018 • Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah Goodman, Stefano Ermon
In high dimensional settings, density estimation algorithms rely crucially on their inductive bias.
2 code implementations • 18 Jun 2018 • Shengjia Zhao, Jiaming Song, Stefano Ermon
A large number of objectives have been proposed to train latent variable generative models.
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.
no code implementations • ICLR 2018 • Shengjia Zhao, Jiaming Song, Stefano Ermon
A variety of learning objectives have been recently proposed for training 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.
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.
6 code implementations • 7 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.
no code implementations • 7 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.
2 code implementations • 28 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.
3 code implementations • 27 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.
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.