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Neural Operators Can Play Dynamic Stackelberg Games

Neural Operators Can Play Dynamic Stackelberg Games

14 November 2024
Guillermo Alvarez
Ibrahim Ekren
Anastasis Kratsios
Xuwei Yang
ArXiv (abs)PDFHTML

Papers citing "Neural Operators Can Play Dynamic Stackelberg Games"

14 / 14 papers shown
Title
Bridging the Gap Between Approximation and Learning via Optimal Approximation by ReLU MLPs of Maximal Regularity
Bridging the Gap Between Approximation and Learning via Optimal Approximation by ReLU MLPs of Maximal Regularity
Ruiyang Hong
Anastasis Kratsios
29
1
0
18 Sep 2024
Calibrated Stackelberg Games: Learning Optimal Commitments Against
  Calibrated Agents
Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents
Nika Haghtalab
Chara Podimata
Kunhe Yang
79
26
0
05 Jun 2023
Operator learning with PCA-Net: upper and lower complexity bounds
Operator learning with PCA-Net: upper and lower complexity bounds
S. Lanthaler
72
26
0
28 Mar 2023
GNOT: A General Neural Operator Transformer for Operator Learning
GNOT: A General Neural Operator Transformer for Operator Learning
Zhongkai Hao
Zhengyi Wang
Hang Su
Chengyang Ying
Yinpeng Dong
Songming Liu
Ze Cheng
Jian Song
Jun Zhu
AI4CE
86
193
0
28 Feb 2023
Designing Universal Causal Deep Learning Models: The Case of Infinite-Dimensional Dynamical Systems from Stochastic Analysis
Designing Universal Causal Deep Learning Models: The Case of Infinite-Dimensional Dynamical Systems from Stochastic Analysis
Luca Galimberti
Anastasis Kratsios
Giulia Livieri
OOD
108
14
0
24 Oct 2022
Designing Universal Causal Deep Learning Models: The Geometric
  (Hyper)Transformer
Designing Universal Causal Deep Learning Models: The Geometric (Hyper)Transformer
Beatrice Acciaio
Anastasis Kratsios
G. Pammer
OOD
123
23
0
31 Jan 2022
Optimal Approximation Rate of ReLU Networks in terms of Width and Depth
Optimal Approximation Rate of ReLU Networks in terms of Width and Depth
Zuowei Shen
Haizhao Yang
Shijun Zhang
200
118
0
28 Feb 2021
Fourier Neural Operator for Parametric Partial Differential Equations
Fourier Neural Operator for Parametric Partial Differential Equations
Zong-Yi Li
Nikola B. Kovachki
Kamyar Azizzadenesheli
Burigede Liu
K. Bhattacharya
Andrew M. Stuart
Anima Anandkumar
AI4CE
522
2,456
0
18 Oct 2020
Neural Operator: Graph Kernel Network for Partial Differential Equations
Neural Operator: Graph Kernel Network for Partial Differential Equations
Zong-Yi Li
Nikola B. Kovachki
Kamyar Azizzadenesheli
Burigede Liu
K. Bhattacharya
Andrew M. Stuart
Anima Anandkumar
208
749
0
07 Mar 2020
Deep learning architectures for nonlinear operator functions and
  nonlinear inverse problems
Deep learning architectures for nonlinear operator functions and nonlinear inverse problems
Maarten V. de Hoop
Matti Lassas
C. Wong
78
26
0
23 Dec 2019
DeepONet: Learning nonlinear operators for identifying differential
  equations based on the universal approximation theorem of operators
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators
Lu Lu
Pengzhan Jin
George Karniadakis
248
2,162
0
08 Oct 2019
Rectified deep neural networks overcome the curse of dimensionality for
  nonsmooth value functions in zero-sum games of nonlinear stiff systems
Rectified deep neural networks overcome the curse of dimensionality for nonsmooth value functions in zero-sum games of nonlinear stiff systems
C. Reisinger
Yufei Zhang
53
70
0
15 Mar 2019
Nearly-tight VC-dimension and pseudodimension bounds for piecewise
  linear neural networks
Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks
Peter L. Bartlett
Nick Harvey
Christopher Liaw
Abbas Mehrabian
226
434
0
08 Mar 2017
Neural Machine Translation by Jointly Learning to Align and Translate
Neural Machine Translation by Jointly Learning to Align and Translate
Dzmitry Bahdanau
Kyunghyun Cho
Yoshua Bengio
AIMat
589
27,345
0
01 Sep 2014
1