[LHL21] Qi Lei, Wei Hu, and Jason D. Lee. "Near-Optimal Linear Regression under Distribution Shift". In: ArXiv (June 23, 2021).
[MPZ21] John P Miller, Juan C Perdomo, and Tijana Zrnic. "Outside the echo chamber: Optimizing the performative risk". In: International Conference on Machine Learning. PMLR. 2021, pp. 7710-7720.
[Wil+21] Olivia Wiles et al. "A Fine-Grained Analysis on Distribution Shift". In: ArXiv (Oct. 21, 2021).
[Zrn+21] Tijana Zrnic et al. "Who Leads and Who Follows in Strategic Classification?" In: Advances in Neural Information Processing Systems. Vol. 34. Curran Associates, Inc., 2021, pp. 15257-15269.
[AZ22] Alekh Agarwal and Tong Zhang. "Minimax Regret Optimization for Robust Machine Learning under Distribution Shift". In: Proceedings of Thirty Fifth Conference on Learning Theory. Conference on Learning Theory. PMLR, June 28, 2022, pp. 2704-2729.
[Giu+22] Valeria Giunta et al. "Local and Global Existence for Nonlocal Multispecies Advection-Diffusion Models". In: SIAM Journal on Applied Dynamical Systems 21.3 (Sept. 2022), pp. 1686-1708. ISSN: 1536-0040. DOI: 10.1137/21M1425992.
[HP522] Georg Heinze, Jan-Frederik Pietschmann, and Markus Schmidtchen. Nonlocal cross-interaction systems on graphs: Nonquadratic Finslerian structure and nonlinear mobilities. Jan. 11, 2022. DOI: 10.48550/arXiv.2107.11289.arXiv: 2107.11289 [math].
[JPZ22] Ansgar Jungel, Stefan Portisch, and Antoine Zurek. "Nonlocal cross-diffusion systems for multi-species populations and networks". In: Nonlinear Analysis 219 (June 1, 2022), p. 112800. ISSN: 0362-546X. DOI: 10.1016/j.na.2022.112800.
[LY22] Guanlin Li and Yao Yao. “Two-species competition model with chemotaxis: well-posedness, stability and dynamics”. In: Nonlinearity 35.3 (Feb. 3, 2022), p. 1329. ISSN: 0951-7715. DOI: 10.1088/1361-6544/ac4a8d.
[LFG22] Fengming Lin, Xiaolei Fang, and Zheming Gao. "Distributionally Robust Optimization: A review on theory and applications". In: Numerical Algebra, Control and Optimization 12.1 (2022), pp. 159-212.
[Nar+22] Adhyyan Narang et al. "Learning in Stochastic Monotone Games with Decision-Dependent Data". In: International Conference on Artificial Intelligence and Statistics. PMLR. 2022, pp. 5891-5912.
[Ray+22] Mitas Ray et al. "Decision-dependent risk minimization in geometrically decaying dynamic environments". In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 36. 7. 2022, pp. 8081-8088.
[CFG23] J. Carrillo, Alejandro Fernandez-Jimenez, and D. G'omez-Castro. "Partial mass concentration for fast-diffusions with non-local aggregation terms". In: Apr. 10, 2023.
[Dou+23] Marie Doumic et al. Multispecies cross-diffusions: from a nonlocal mean-field to a porous medium system without self-diffusion. June 12, 2023. DOI: 10.48550/arXiv.2306.01777.arXiv:2306.01777 [math].
[GG23] Camilo Garcia Trillos and Nicolas Garcia Trillos. On adversarial robustness and the use of Wasserstein ascent-descent dynamics to enforce it. Jan. 9, 2023. DOI: 10.48550/arXiv.2301.03662.arXiv:2301.03662[cs,math].
[HP523] Georg Heinze, Jan-Frederik Pietschmann, and Markus Schmidtchen. "Nonlocal cross-interaction systems on graphs: Energy landscape and dynamics". In: *Kink and Related Models* (KRM) (2023).
[Lu23] Yulong Lu. Two-Scale Gradient Descent Ascent Dynamics Finds Mixed Nash Equilibria of Continuous Games: A Mean-Field Perspective. Jan. 8, 2023. DOI: 10.48550/arXiv.2212.08791.arXiv:2212.08791[cs,math,stat].
[WC23] Guillaume Wang and Lenaic Chizat. An Exponentially Converging Particle Method for the Mixed Nash Equilibrium of Continuous Games. Apr. 13, 2023. DOI: 10.48550/arXiv.2211.01280.arXiv:2211.01280[cs,math].
[Qui+] Joaquin Quinonero-Candela et al. Dataset Shift in Machine Learning. MIT Press.