Fetching the paper…
Reading the bibliography…
We develop a fitted value iteration (FVI) method to compute bicausal optimal transport (OT) where couplings have an adapted structure.
A class of Wasserstein metrics for probability distributions
Givens, C. R. and Shortt, R. M. (1984) · 1984
Earlier work this paper cites.
Neural Network Learning: Theoretical Foundations
Anthony, M. and Bartlett, P. L. (1999) · 1999
Earlier work this paper cites.
Approximate solutions to Markov decision processes
Gordon, G. J. (1999) · 1999
Earlier work this paper cites.
A few notes on statistical learning theory
Mendelson, S. (2003) · 2002
Earlier work this paper cites.
Error bounds for approximate policy iteration
Munos, R. (2003) · 2003
Earlier work this paper cites.
Local Rademacher complexities
Bartlett, P., Bousquet, O., and Mendelson, S. (2005) · 2005
Earlier work this paper cites.
Measure Theory
Bogachev, V. I. (2007) · 2007
Earlier work this paper cites.
The tradeoffs of large scale learning
Bottou, L. and Bousquet, O. (2007) · 2007
Earlier work this paper cites.
Approximate Dynamic Programming: Solving the Curses of Dimensionality
Powell, W. B. (2007) · 2007
Earlier work this paper cites.
Finite-time bounds for fitted value iteration
Munos, R. and Szepesvári, C. (2008) · 2008
Earlier work this paper cites.
Optimal Transport: Old and New
Villani, C. (2009) · 2009
Earlier work this paper cites.
A distance for multistage stochastic optimization models
Pflug, G. C. and Pichler, A. (2012) · 2012
Earlier work this paper cites.
Sinkhorn distances: Lightspeed computation of optimal transport
Cuturi, M. (2013) · 2013
Earlier work this paper cites.
Causal transference plans and their Monge-Kantorovich problems
Lassalle, R. (2013) · 2013
Earlier work this paper cites.
Understanding Machine Learning: From Theory to Algorithms
Shalev-Shwartz, S. and Ben-David, S. (2014) · 2014
Earlier work this paper cites.
On the rate of convergence in Wasserstein distance of the empirical measure
Fournier, N. and Guillin, A. (2015) · 2015
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · 2015
Cited alongside, same era.
Optimal Transport for Applied Mathematicians
Santambrogio, F. (2015) · 2015
Cited alongside, same era.
Stochastic optimization for large-scale optimal transport
Genevay, A., Cuturi, M., Peyré, G., and Bach, F. (2016) · 2016
Cited alongside, same era.
Local Rademacher complexity bounds based on covering numbers
Lei, Y., Ding, L., and Bi, Y. (2016) · 2016
Cited alongside, same era.
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
Altschuler, J., Niles-Weed, J., and Rigollet, P. (2017) · 2017
Cited alongside, same era.
Causal transport in discrete time and applications
Backhoff-Veraguas, J., Beiglbock, M., Lin, Y., and Zalashko, A. (2017) · 2017
COT-GAN: Generating sequential data via causal optimal transport
Xu, T., Li, W. K., Munn, M., and Acciaio, B. (2020) · 2020
Later among the works it cites.
Cournot–Nash equilibrium and optimal transport in a dynamic setting
Acciaio, B., Backhoff-Veraguas, J., and Jia, J. (2021) · 2021
Later among the works it cites.
Transport plans with domain constraints
Bayraktar, E., Zhang, X., and Zhou, Z. (2021) · 2021
Later among the works it cites.
Risk bounds and Rademacher complexity in batch reinforcement learning
Duan, Y., Jin, C., and Li, Z. (2021) · 2021
Later among the works it cites.
Computation of optimal transport and related hedging problems via penalization and neural networks
Eckstein, S. and Kupper, M. (2021) · 2021
Later among the works it cites.
Approximating smooth functions by deep neural networks with sigmoid activation function
Langer, S. (2021) · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Martingale optimal transport with stopping
Bayraktar, E., Cox, A. M., and Stoev, Y. (2018) · 2018
Cited alongside, same era.
Foundations of Machine Learning
Mohri, M., Rostamizadeh, A., and Talwalkar, A. (2018) · 2018
Cited alongside, same era.
Large-scale optimal transport and mapping estimation
Seguy, V., Damodaran, B. B., Flamary, R., Courty, N., Rolet, A., and Blondel, M. (2018) · 2018
Cited alongside, same era.
Information-theoretic considerations in batch reinforcement learning
Chen, J. and Jiang, N. (2019) · 2019
Cited alongside, same era.
Sample complexity of Sinkhorn divergences
Genevay, A., Chizat, L., Bach, F., Cuturi, M., and Peyré, G. (2019) · 2019
Cited alongside, same era.
Computational optimal transport: With applications to data science
Peyré, G. and Cuturi, M. (2019) · 2019
Cited alongside, same era.
Later among the works it cites.
Estimating processes in adapted Wasserstein distance
Backhoff-Veraguas, J., Bartl, D., Beiglböck, M., and Wiesel, J. (2022) · 2022
Later among the works it cites.
Stability and sample complexity of divergence regularized optimal transport
Bayraktar, E., Eckstein, S., and Zhang, X. (2022) · 2022
Later among the works it cites.
Quantitative stability of regularized optimal transport and convergence of Sinkhorn’s algorithm
Eckstein, S. and Nutz, M. (2022) · 2022
Later among the works it cites.
On the efficiency of entropic regularized algorithms for optimal transport
Lin, T., Ho, N., and Jordan, M. I. (2022) · 2022
Later among the works it cites.
The nested Sinkhorn divergence to learn the nested distance
Pichler, A. and Weinhardt, M. (2022) · 2022
Later among the works it cites.
Computational methods for adapted optimal transport
Eckstein, S. and Pammer, G. (2023) · 2023
Closest in time.
Distributionally robust Kalman filtering with volatility uncertainty
Han, B. (2023) · 2023
Closest in time.
Convergence rates for regularized optimal transport via quantization
Eckstein, S. and Nutz, M. (2024) · 2024
Closest in time.
Quantitative convergence of quadratically regularized linear programs
González-Sanz, A. and Nutz, M. (2024) · 2024
Closest in time.