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ORCIDSamuel N. Cohen
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2020 – today
- 2024
[c3]Gholamali Aminian, Yixuan He, Gesine Reinert, Lukasz Szpruch, Samuel N. Cohen:
Generalization Error of Graph Neural Networks in the Mean-field Regime. ICML 2024- [i9]Gholamali Aminian, Yixuan He, Gesine Reinert, Lukasz Szpruch, Samuel N. Cohen:
Generalization Error of Graph Neural Networks in the Mean-field Regime. CoRR abs/2402.07025 (2024) - [i8]Gholamali Aminian, Amir R. Asadi, Tian Li, Ahmad Beirami, Gesine Reinert, Samuel N. Cohen:
Generalization Error of the Tilted Empirical Risk. CoRR abs/2409.19431 (2024) - 2023
- [j7]Samuel N. Cohen, Deqing Jiang, Justin A. Sirignano:
Neural Q-learning for solving PDEs. J. Mach. Learn. Res. 24: 236:1-236:49 (2023) - [c2]Samuel N. Cohen, Marc Sabate Vidales, David Siska, Lukasz Szpruch:
Inefficiency of CFMs: Hedging Perspective and Agent-Based Simulations. FC Workshops 2023: 303-319 - [i7]Deqing Jiang, Justin A. Sirignano, Samuel N. Cohen:
Global Convergence of Deep Galerkin and PINNs Methods for Solving Partial Differential Equations. CoRR abs/2305.06000 (2023) - [i6]Gholamali Aminian, Samuel N. Cohen, Lukasz Szpruch:
Mean-field Analysis of Generalization Errors. CoRR abs/2306.11623 (2023) - 2022
- [i5]Samuel N. Cohen, Deqing Jiang, Justin A. Sirignano:
Neural Q-learning for solving elliptic PDEs. CoRR abs/2203.17128 (2022) - [i4]James Jordon, Lukasz Szpruch, Florimond Houssiau, Mirko Bottarelli, Giovanni Cherubin, Carsten Maple, Samuel N. Cohen, Adrian Weller:
Synthetic Data - what, why and how? CoRR abs/2205.03257 (2022) - [i3]Florimond Houssiau, James Jordon, Samuel N. Cohen, Owen Daniel, Andrew Elliott
, James Geddes, Callum D. Mole, Camila Rangel Smith, Lukasz Szpruch:
TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data. CoRR abs/2211.06550 (2022) - [i2]Florimond Houssiau, Samuel N. Cohen, Lukasz Szpruch, Owen Daniel, Michaela G. Lawrence, Robin Mitra, Henry Wilde, Callum D. Mole:
A Framework for Auditable Synthetic Data Generation. CoRR abs/2211.11540 (2022) - 2021
- [c1]Haoyang Cao, Samuel N. Cohen, Lukasz Szpruch:
Identifiability in inverse reinforcement learning. NeurIPS 2021: 12362-12373 - [i1]Haoyang Cao, Samuel N. Cohen, Lukasz Szpruch:
Identifiability in inverse reinforcement learning. CoRR abs/2106.03498 (2021)
2010 – 2019
- 2019
- [j6]Andrew L. Allan, Samuel N. Cohen:
Parameter Uncertainty in the Kalman-Bucy Filter. SIAM J. Control. Optim. 57(3): 1646-1671 (2019) - 2017
- [j5]Samuel N. Cohen, Victor Fedyashov:
Nash equilibria for nonzero-sum ergodic stochastic differential games. J. Appl. Probab. 54(4): 977-994 (2017) - 2015
- [j4]Robert J. Elliott, Tak Kuen Siu, Samuel N. Cohen:
Backward stochastic difference equations for dynamic convex risk measures on a binomial tree. J. Appl. Probab. 52(3): 771-785 (2015) - 2014
- [j3]Samuel N. Cohen:
Undiscounted Markov Chain BSDEs to Stopping Times. J. Appl. Probab. 51(1): 262-281 (2014) - 2013
- [j2]Samuel N. Cohen, Ying Hu:
Ergodic BSDEs Driven by Markov Chains. SIAM J. Control. Optim. 51(5): 4138-4168 (2013) - 2011
- [j1]Samuel N. Cohen, Robert J. Elliott:
Backward Stochastic Difference Equations and Nearly Time-Consistent Nonlinear Expectations. SIAM J. Control. Optim. 49(1): 125-139 (2011)
Coauthor Index
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