2022–2023 Publications
Dropout Training is Distributionally Robust Optimal
Blanchet, J., Kang, Y., Montiel Olea, J. L., Nguyen, V. A., & Zhang, X. (2023)
Journal of Machine Learning Research, 24: 1–60.
Optimal Sample Complexity of Reinforcement Learning for Uniformly Ergodic Discounted Markov Decision Processes
Wang, S., Blanchet, J., & Glynn, P. (2023)
arXiv:2302.07477.
Wasserstein Distributionally Robust Linear-Quadratic Estimation under Martingale Constraints
Lotidis, K., Bambos, N., Blanchet, J., & Li, J. (2023)
International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR, 8629–8644.
Statistical Limit Theorems in Distributionally Robust Optimization
Blanchet, J., & Shapiro, A. (2023)
arXiv:2303.14867.
A Finite Sample Complexity Bound for Distributionally Robust Q-Learning
Wang, S., Si, N., Blanchet, J., & Zhou, Z. (2023)
AISTATS, PMLR, 3370–3398.
Detection and Reduction of Systematic Bias in High-throughput Rupture Experiments
Wu, H., Zhang, X., Zhou, Y., Blanchet, J., Suo, Z., & Lu, T. (2023)
Journal of the Mechanics and Physics of Solids, 174: 105249.
When can Regression-Adjusted Control Variates Help? Rare Events, Sobolev Embedding and Minimax Optimality
Blanchet, J., Chen, H., Lu, Y., & Ying, L. (2023)
arXiv:2305.16527.
Neural Network Accelerated Process Design of Polycrystalline Microstructures
Lin, J., Hasan, M., Acar, P., Blanchet, J., & Tarokh, V. (2023)
Materials Today Communications, 36: 106884.
Unifying Distributionally Robust Optimization via Optimal Transport Theory
Blanchet, J., Kuhn, D., Li, J., & Taskesen, B. (2023)
arXiv:2308.05414.
Efficient Scenario Generation for Heavy-Tailed Chance Constrained Optimization
Blanchet, J., Zhang, F., & Zwart, B. (2023)
Stochastic Systems.
Optimal Sample Complexity for Average Reward Markov Decision Processes
Wang, S., Blanchet, J., & Glynn, P. (2023)
arXiv:2310.08833.
Modeling Shortest Paths in Polymeric Networks using Spatial Branching Processes
Zhang, Z., Mohanty, S., Blanchet, J., & Cai, W. (2023)
arXiv:2310.18551.
Universal Gradient Descent Ascent Method for Nonconvex-Nonconcave Minimax Optimization
Zheng, T., Zhu, L., So, A. M.-C., Blanchet, J., & Li, J. (2023)
NeurIPS 2023.
Accelerated Sampling of Rare Events using a Neural Network Bias Potential
Hua, X., Ahmad, R., Blanchet, J., & Cai, W. (2023)
NeurIPS Workshop: AI for Accelerated Materials Design.
Payoff-based Learning with Matrix Multiplicative Weights in Quantum Games
Lotidis, K., Mertikopoulos, P., Bambos, N., & Blanchet, J. (2023)
NeurIPS 2023.
Representation Learning for Extremes
Hasan, A., Ng, Y., Blanchet, J., & Tarokh, V. (2023)
NeurIPS Workshop on Heavy Tails in Machine Learning.
On the Foundation of Distributionally Robust Reinforcement Learning
Wang, S., Si, N., Blanchet, J., & Zhou, Z. (2023)
arXiv:2311.09018.
Unbiased Optimal Stopping via the MUSE
Zhou, Z., Wang, G., Blanchet, J., & Glynn, P. (2023)
Stochastic Processes and their Applications, 166: 104088.
Is a High-throughput Experimental Dataset Large Enough to Accurately Estimate a Statistic?
Zhou, Y., Lin, S., Zhang, X., Wu, H., Blanchet, J., Suo, Z., & Lu, T. (2023)
Journal of the Mechanics and Physics of Solids, 105521.
Doubly Smoothed GDA: Global Convergent Algorithm for Constrained Nonconvex-Nonconcave Minimax Optimization
Zheng, T., Zhu, L., So, A. M.-C., Blanchet, J., & Li, J. (2022)
arXiv:2212.12978.
Network Evolution Controlling Strain-induced Damage and Self-healing of Elastomers with Dynamic Bonds
Yin, Y., Mohanty, S., Cooper, C. B., Bao, Z., & Cai, W. (2024)
arXiv:submit/5358548.
Sharp Error Estimates for Target Measure Diffusion Maps with Applications to the Committor Problem
Sule, S., Evans, L., & Cameron, M. (2023)
arXiv:2312.14418. Submitted to Applied and Computational Harmonic Analysis.
A Finite Expression Method for Solving High-Dimensional Committor Problems
Song, Z., Cameron, M. K., & Yang, H. (2023)
arXiv:2306.12268. Submitted to SIAM Journal on Scientific Computing.
Influence of Noise on a Rotating, Softening Cantilever Beam
Cilenti, L., Cameron, M., & Balachandran, B. (2024)
International Journal of Non-Linear Mechanics, 159: 104582. doi:10.1016/j.ijnonlinmec.2023.104582.
Optimal Control for Sampling the Transition Path Process and Estimating Rates
Yuan, J., Shah, A., Bentz, C., & Cameron, M. (2024)
Communications of Nonlinear Science and Numerical Simulation, 129: 107701. doi:10.1016/j.cnsns.2023.107701.
The Effect of Scatter of Polymer Chain Length on Strength
Tao, M., Lavoie, S., Suo, Z., & Cameron, M. (2023)
Extreme Mechanics Letters, 61: 102024. doi:10.1016/j.eml.2023.102024.
Predicting Molecule Size Distribution in Hydrocarbon Pyrolysis using Random Graph Theory
Dufour-Decieux, V., Moakler, C., Reed, E. J., & Cameron, M. (2023)
Journal of Chemical Physics, 158: 024101. doi:10.1063/5.0133641.
Computing Committors in Collective Variables via Mahalanobis Diffusion Maps
Evans, L., Cameron, M. K., & Tiwary, P. (2023)
Applied and Computational Harmonic Analysis, 64: 62–101. doi:10.1016/j.acha.2023.01.001.
Inference and Sampling of Point Processes from Diffusion Excursions
Hasan, A., Chen, Y., Ng, Y., Abdelghani, M., Schneider, A., & Tarokh, V. (2023)
Uncertainty in Artificial Intelligence (UAI).
Polyacrylamide Hydrogels VI: Synthesis-Property Relation
Wang, Y., Nian, G., Kim, J., & Suo, Z. (2022)
Journal of the Mechanics and Physics of Solids, 170.
Fracture Initiated from Corners in Brittle Soft Materials
Steck, J., Hassan, S., & Suo, Z. (2022)
Journal of the Mechanics and Physics of Solids, 170.
Self-assembled Nanocomposites of High Water Content and Load-bearing Capacity
Zhang, G., Kim, J., Hassan, S., & Suo, Z. (2022)
Proceedings of the National Academy of Sciences, 119(32): e2203962119.
Strain-stiffening Seal
Chen, B., Chen, C., Lou, Y., & Suo, Z. (2022)
Soft Matter, 18: 2992–3003.
High-throughput Experiments for Rare-event Rupture of Materials
Zhou, Y., Zhang, X., Yang, M., Pan, Y., Du, Z., Blanchet, J., Suo, Z., & Lu, T. (2022)
Matter, 5: 1–12.
Distribution learning via neural differential equations: a nonparametric statistical perspective
Marzouk, Y., Ren, Z., Wang, S., & Zech, J. (2024)
Journal of Machine Learning Research, 25(232): 1–61.
Infinite-Dimensional Diffusion Models
Pidstrigach, J., Marzouk, Y., Reich, S., & Wang, S. (2024)
Journal of Machine Learning Research, 25(414): 1–52.
Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference
Wang, Z. O., Baptista, R., Marzouk, Y., Ruthotto, L., & Verma, D. (2023)
arXiv:2310.16975.
Distribution Learning via Neural Differential Equations: Minimal Energy Regularization and Approximation Theory
Marzouk, Y., Ren, Z., & Zech, J. (2025)
arXiv:2502.03795.
Score Operator Newton Transport
Chandramoorthy, N., Schaefer, F. T., & Marzouk, Y. (2024)
AISTATS, PMLR, 3349–3357.
Random Graph Models Predicting Giant Molecules in Hydrocarbon Pyrolysis
Ruth, P. E., Dufour-Decieux, V., Moakler, C., & Cameron, M. (2025)
In preparation for Physical Review E.
Modeling Fracture of Polymer Networks
Tao, M., Lavoie, S., Suo, Z., & Cameron, M. (2025)
In preparation.
