1 code implementation • 21 Feb 2024 • Edwin Zhang, Sadie Zhao, Tonghan Wang, Safwan Hossain, Henry Gasztowtt, Stephan Zheng, David C. Parkes, Milind Tambe, YiLing Chen
Artificial Intelligence (AI) holds promise as a technology that can be used to improve government and economic policy-making.
no code implementations • 10 Jul 2023 • Yoshua Bengio, Prateek Gupta, Lu Li, Soham Phade, Sunil Srinivasa, Andrew Williams, Tianyu Zhang, Yang Zhang, Stephan Zheng
On the other hand, an interdisciplinary panel of human experts in law, policy, sociology, economics and environmental science, evaluated the solutions qualitatively.
no code implementations • 10 Apr 2023 • Arundhati Banerjee, Soham Phade, Stefano Ermon, Stephan Zheng
We then show that our model-based meta-learning approach is cost-effective in intervening on bandit agents with unseen explore-exploit strategies.
2 code implementations • 15 Aug 2022 • Tianyu Zhang, Andrew Williams, Soham Phade, Sunil Srinivasa, Yang Zhang, Prateek Gupta, Yoshua Bengio, Stephan Zheng
To facilitate this research, here we introduce RICE-N, a multi-region integrated assessment model that simulates the global climate and economy, and which can be used to design and evaluate the strategic outcomes for different negotiation and agreement frameworks.
1 code implementation • 18 Jan 2022 • Tong Mu, Stephan Zheng, Alexander Trott
In a sequential setting with multiple Agents, RIRL shows opposing consequences of the Principal's inattention to different information channels: 1) inattention to Agents' outputs closes wage gaps based on ability differences; and 2) inattention to Agents' efforts induces a social dilemma dynamic in which Agents work harder, but essentially for free.
no code implementations • 3 Jan 2022 • Michael Curry, Alexander Trott, Soham Phade, Yu Bai, Stephan Zheng
We validate the learned solutions are $\epsilon$-meta-equilibria through best-response analyses, show that they align with economic intuitions, and show our approach can learn a spectrum of qualitatively distinct $\epsilon$-meta-equilibria in open RBC models.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 6 Dec 2021 • Alexander Lavin, David Krakauer, Hector Zenil, Justin Gottschlich, Tim Mattson, Johann Brehmer, Anima Anandkumar, Sanjay Choudry, Kamil Rocki, Atılım Güneş Baydin, Carina Prunkl, Brooks Paige, Olexandr Isayev, Erik Peterson, Peter L. McMahon, Jakob Macke, Kyle Cranmer, Jiaxin Zhang, Haruko Wainwright, Adi Hanuka, Manuela Veloso, Samuel Assefa, Stephan Zheng, Avi Pfeffer
We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence.
3 code implementations • 31 Aug 2021 • Tian Lan, Sunil Srinivasa, Huan Wang, Stephan Zheng
We present WarpDrive, a flexible, lightweight, and easy-to-use open-source RL framework that implements end-to-end deep multi-agent RL on a single GPU (Graphics Processing Unit), built on PyCUDA and PyTorch.
1 code implementation • 6 Aug 2021 • Alexander Trott, Sunil Srinivasa, Douwe van der Wal, Sebastien Haneuse, Stephan Zheng
Here we show that the AI Economist framework enables effective, flexible, and interpretable policy design using two-level reinforcement learning (RL) and data-driven simulations.
1 code implementation • 5 Aug 2021 • Stephan Zheng, Alexander Trott, Sunil Srinivasa, David C. Parkes, Richard Socher
Here we show that machine-learning-based economic simulation is a powerful policy and mechanism design framework to overcome these limitations.
1 code implementation • 10 Jun 2021 • Eric Zhao, Alexander R. Trott, Caiming Xiong, Stephan Zheng
We study the problem of training a principal in a multi-agent general-sum game using reinforcement learning (RL).
2 code implementations • NAACL 2021 • Karan Goel, Nazneen Rajani, Jesse Vig, Samson Tan, Jason Wu, Stephan Zheng, Caiming Xiong, Mohit Bansal, Christopher Ré
Despite impressive performance on standard benchmarks, deep neural networks are often brittle when deployed in real-world systems.
1 code implementation • 11 Jan 2021 • Alexander Lavin, Ciarán M. Gilligan-Lee, Alessya Visnjic, Siddha Ganju, Dava Newman, Atılım Güneş Baydin, Sujoy Ganguly, Danny Lange, Amit Sharma, Stephan Zheng, Eric P. Xing, Adam Gibson, James Parr, Chris Mattmann, Yarin Gal
The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end.
no code implementations • 1 Jan 2021 • Eric Zhao, Alexander R Trott, Caiming Xiong, Stephan Zheng
Policies for real-world multi-agent problems, such as optimal taxation, can be learned in multi-agent simulations with AI agents that emulate humans.
no code implementations • 11 Dec 2020 • Ian Foster, David Parkes, Stephan Zheng
These advances may lead to a new era in computational simulation, in which sensors of many kinds are used to produce vast quantities of data, AI methods identify patterns in those data, and new AI-driven simulators combine machine-learned and mathematical rules to make accurate and actionable predictions.
2 code implementations • ACL 2020 • Nazneen Fatema Rajani, Rui Zhang, Yi Chern Tan, Stephan Zheng, Jeremy Weiss, Aadit Vyas, Abhijit Gupta, Caiming Xiong, Richard Socher, Dragomir Radev
Our framework learns to generate explanations of how the physical simulation will causally evolve so that an agent or a human can easily reason about a solution using those interpretable descriptions.
2 code implementations • 28 Apr 2020 • Stephan Zheng, Alexander Trott, Sunil Srinivasa, Nikhil Naik, Melvin Gruesbeck, David C. Parkes, Richard Socher
In experiments conducted on MTurk, an AI tax policy provides an equality-productivity trade-off that is similar to that provided by the Saez framework along with higher inverse-income weighted social welfare.
1 code implementation • ICML 2020 • Jung Yeon Park, Kenneth Theo Carr, Stephan Zheng, Yisong Yue, Rose Yu
Efficient and interpretable spatial analysis is crucial in many fields such as geology, sports, and climate science.
1 code implementation • NeurIPS 2019 • Alexander Trott, Stephan Zheng, Caiming Xiong, Richard Socher
For instance, in tasks where the agent must achieve some goal state, simple distance-to-goal reward shaping often fails, as it renders learning vulnerable to local optima.
no code implementations • WS 2020 • Michael Shum, Stephan Zheng, Wojciech Kryściński, Caiming Xiong, Richard Socher
Human-like chit-chat conversation requires agents to generate responses that are fluent, engaging and consistent.
no code implementations • 25 Sep 2019 • Wenling Shang, Alex Trott, Stephan Zheng, Caiming Xiong, Richard Socher
Efficiently learning to solve tasks in complex environments is a key challenge for reinforcement learning (RL) agents.
no code implementations • 1 Jul 2019 • Wenling Shang, Alex Trott, Stephan Zheng, Caiming Xiong, Richard Socher
We perform a thorough ablation study to evaluate our approach on a suite of challenging maze tasks, demonstrating significant advantages from the proposed framework over baselines that lack world graph knowledge in terms of performance and efficiency.
Hierarchical Reinforcement Learning reinforcement-learning +1
no code implementations • 29 May 2019 • Huan Wang, Stephan Zheng, Caiming Xiong, Richard Socher
For this problem class, estimating the expected return is efficient and the trajectory can be computed deterministically given peripheral random variables, which enables us to study reparametrizable RL using supervised learning and transfer learning theory.
1 code implementation • NeurIPS 2019 • Yukai Liu, Rose Yu, Stephan Zheng, Eric Zhan, Yisong Yue
Missing value imputation is a fundamental problem in spatiotemporal modeling, from motion tracking to the dynamics of physical systems.
Ranked #1 on Multivariate Time Series Imputation on PEMS-SF
3 code implementations • 14 Oct 2018 • Steven Farrell, Paolo Calafiura, Mayur Mudigonda, Prabhat, Dustin Anderson, Jean-Roch Vlimant, Stephan Zheng, Josh Bendavid, Maria Spiropulu, Giuseppe Cerati, Lindsey Gray, Jim Kowalkowski, Panagiotis Spentzouris, Aristeidis Tsaris
The second set of models use Graph Neural Networks (GNNs) for the tasks of hit classification and segment classification.
High Energy Physics - Experiment Data Analysis, Statistics and Probability
2 code implementations • ICLR 2019 • Eric Zhan, Stephan Zheng, Yisong Yue, Long Sha, Patrick Lucey
We study the problem of training sequential generative models for capturing coordinated multi-agent trajectory behavior, such as offensive basketball gameplay.
1 code implementation • 11 Mar 2018 • Sumanth Dathathri, Stephan Zheng, Tianwei Yin, Richard M. Murray, Yisong Yue
Deep neural networks are vulnerable to adversarial examples, which dramatically alter model output using small input changes.
no code implementations • 19 Feb 2018 • Stephan Zheng, Rose Yu, Yisong Yue
High-dimensional tensor models are notoriously computationally expensive to train.
no code implementations • ICLR 2018 • Rose Yu, Stephan Zheng, Anima Anandkumar, Yisong Yue
We present Tensor-Train RNN (TT-RNN), a novel family of neural sequence architectures for multivariate forecasting in environments with nonlinear dynamics.
no code implementations • ICLR 2018 • Stephan Zheng, Yisong Yue
Reinforcement learning in environments with large state-action spaces is challenging, as exploration can be highly inefficient.
1 code implementation • ICLR 2018 • Rose Yu, Stephan Zheng, Anima Anandkumar, Yisong Yue
We present Higher-Order Tensor RNN (HOT-RNN), a novel family of neural sequence architectures for multivariate forecasting in environments with nonlinear dynamics.
no code implementations • NeurIPS 2016 • Stephan Zheng, Yisong Yue, Patrick Lucey
We study the problem of modeling spatiotemporal trajectories over long time horizons using expert demonstrations.
no code implementations • CVPR 2016 • Stephan Zheng, Yang song, Thomas Leung, Ian Goodfellow
In this paper we address the issue of output instability of deep neural networks: small perturbations in the visual input can significantly distort the feature embeddings and output of a neural network.