X. Fan, C.W. Leung, P. Turrini, "Co-Learning of Strategy and Structure Achieves Full Cooperation in Complex Networks with Dynamical Linking", International Joint Conference on Artificial Intelligence (IJCAI), 2025.
A. Bhattacharyya, D. Choo, S. Gayen, and D. Myrisiotis, "Learnability of Parameter-bounded Bayes Nets", International Joint Conference on Artificial Intelligence (IJCAI), 2025.
Z. Zhu, F. Liu, V. Cevher, "How Gradient descent balances features: A dynamical analysis for two-layer neural networks," International Conference on Learning Representations (ICLR), 2025.
A. Bhattacharyya, S. Gayen, K.S. Meel, D. Myrisiotris, A. Pavan, and N.V. Vinodchandran. "Computational Explorations of Total Variation Distance", International Conference on Learning Representations (ICLR), 2025.
C.W. Leung, P. Turrini, Ann Nowé, "Curiosity Driven Partner Selection Accelerates Convention Emergence in Language Games", International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2025. Best Paper Award Finalist.
V. Pollatos, D. Mandal, G. Radanovic, "On Corruption-Robustness in Performative Reinforcement Learning", In the 39th AAAI Conference on Artificial Intelligence (AAAI), 2025.
T. Mildner, O. Hamelijnck, P. Giampouras, T. Damoulas,"Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework", International Conference on Machine Learning (accepted as spotlight paper), (ICML), 2025.
P. Giampouras, H. Cai, and R. Vidal, "Guarantees of a Preconditioned Subgradient Algorithm for Overparameterized Asymmetric Low-rank Matrix Recovery," International Conference on Machine Learning (ICML), 2025.
Y. Zhang, F. Liu, Y. Chen, "LoRA-One: One-Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently," (Oral Presentation), International Conference on Machine Learning (ICML), 2025.
C. Dellaporta, P. O'Hara, and T. Damoulas. "Decision making under the exponential family: Distributionally Robust Optimisation with Bayesian Ambiguity Sets.", International Conference on Machine Learning (accepted as spotlight paper), (ICML), 2025.
A. Bhattacharyya, D. Choo, P.G. John, and T. Gouleakis. "Learning multivariate Gaussians with imperfect advice", International Conference on Machine Learning (ICML), 2025.
Y. Wang, Y. Chen, L. Rosasco, F. Liu, "The Shape of Generalization through the Lens of Norm-based Capacity Control," in the 39th Conference on Neural Information Processing Systems (NeurIPS), 2025.
T. K. Buening, J. Gan, D. Mandal, M. Kwiatkowska. “Strategyproof Reinforcement Learning from Human Feedback", In the 39th Annual Conference on Neural Information Processing Systems (NeurIPS), 2025.
J. Gan, R. Majumdar, D. Mandal, G. Radanovic. “Stochastic Principal-Agent Problems: Efficient Computation and Learning”, In the 39th Annual Conference on Neural Information Processing Systems (NeurIPS), 2025.
A. Bhattacharyya, S. Gayen, P.G. John, S. Sen, and N.V. Vinodchandran, "Distribution Learning meets Graph Structure Sampling", Conference on Neural Information Processing Systems (NeurIPS), 2025.
A. Bhattacharyya, D. Choo, P.G. John, and T. Gouleakis, "Product Distribution Learning with Imperfect Advice", Conference on Neural Information Processing Systems (NeurIPS), accepted as spotlight paper, 2025.
D. Mandal, A. Nika, P. Kamalruban, A. Singla, G. Radanovic. “Corruption Robust Reinforcement Learning with Human Feedback”, In the 28th International Conference on Artificial Intelligence and Statistics (selected for oral presentation), (AISTATS), 2025.
D. Mandal, G. Radanovic, "Performative Reinforcement Learning with Linear Markov Decision Process", International Conference on Artificial Intelligence and Statistics (AISTATS), 2025.
A. Nika, J. Nöther, D. Mandal, P. Kamalaruban, A. Singal, G. Radanovic. "Policy Teaching via Data Poisoning in Learning from Human Preferences", In the 28th International Conference on Artificial Intelligence and Statistics (AISTATS), 2025.
R. Sahitaj, P. Sasnauskas, Y. Yalin, D. Mandal, G. Radanovic, "Independent Learning in Performative Markov Potential Games", In the 28th International Conference on Artificial Intelligence and Statistics (AISTATS), 2025.
A. Bhattacharyya, W. Feng and P. Srivastava, "Approximating the Total Variation Distance between Gaussians", International Conference on Artificial Intelligence and Statistics (AISTATS), 2025.
A. Bhattacharyya, C. Daskalakis, T. Gouleakis, and Y. Wang. "Learning High-dimensional Gaussians from Censored Data", International Conference on Artificial Intelligence and Statistics (AISTATS), 2025.
U. Grandi, G. Lisowksi, L. Kane's, R. Shridaran, P. Turrini, "A Complexity Theoretic Analysis of Majority Illusion in Social Networks", Journal of Artificial Intelligence Research (JAIR), (83). 2025
Matthias C. Caro, Tom Gur, Cambyse Rouzé, Daniel Stilck França, and Sathyawageeswar Subramanian. “Information-theoretic generalization bounds for learning from quantum data”. In: Proceedings of Thirty Seventh Conference on Learning Theory (COLT 2024). Ed. by Shipra Agrawal and Aaron Roth. Vol. 247. Proceedings of Machine Learning Research. PMLR, 30 Jun–03 Jul 2024, pp. 775–839. https://proceedings.mlr.press/v247/caro24a.html
Matthias C. Caro, Marcel Hinsche, Marios Ioannou, Alexander Nietner, and Ryan Sweke.“Classical Verification of Quantum Learning”. In: 15th Innovations in Theoretical ComputerScience Conference (ITCS 2024). Ed. by Venkatesan Guruswami. Vol. 287. Leibniz InternationalProceedings in Informatics (LIPIcs). Dagstuhl, Germany: Schloss Dagstuhl – Leibniz-Zentrumfür Informatik, 2024, 24:1–24:23. https://doi.org/10.4230/LIPIcs.ITCS.2024.24
Y. Chen, F. Liu, T. Suzuki, V. Cevher, "High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization." International Conference on Machine Learning (ICML), 2024.
Rocamora, Elias Abad, Yongtao Wu, Fanghui Liu, Grigorios Chrysos, and Volkan Cevher. "Revisiting Character-level Adversarial Attacks for Language Models." International Conference on Machine Learning (ICML), 2024.
Chen Y, Liu F, Lu Y, Chrysos G, Cevher V. Generalization of Scaled Deep ResNets in the Mean-Field Regime. InThe Twelfth International Conference on Learning Representations (ICLR), 2024. (Spotlight)
Dyer, Joel, Nicholas Bishop, Yorgos Felekis, Fabio Massimo Zennaro, Anisoara Calinescu, Theodoros Damoulas, and Michael Wooldridge. "Interventionally consistent surrogates for complex simulation models.", Advances in Neural Information Processing Systems, 37 (2024): 21814-21841 (NeurIPS), 2024.
Zachos, Ioannis, Mark Girolami, and Theodoros Damoulas. "Generating origin-destination matrices in neural spatial interaction models.", Advances in Neural Information Processing Systems, 37: 110436-110463, (NeurIPS), 2024.
Hamelijnck, Oliver, Arno Solin, and Theodoros Damoulas. "Physics-informed variational state-space Gaussian processes.", Advances in Neural Information Processing Systems, 37: 98505-98536, (NeurIPS), 2024.
Felekis, Yorgos, Fabio Massimo Zennaro, Nicola Branchini, and Theodoros Damoulas. "Causal optimal transport of abstractions." In Causal Learning and Reasoning, pp. 462-498. PMLR, (CLeaR) 2024.
Haimeng Zhao, Laura Lewis, Ishaan Kannan, Yihui Quek, Hsin-Yuan Huang, and Matthias C. Caro. “Learning Quantum States and Unitaries of Bounded Gate Complexity”. PRX Quantum 5 (4 Oct. 2024), p. 040306. https://doi.org/10.1103/PRXQuantum.5.040306
Matthias C. Caro. “Learning Quantum Processes and Hamiltonians via the Pauli Transfer Matrix”. ACM Transactions on Quantum Computing 5.2 (June 2024). https://doi.org/10.1145/3670418
Joe Gibbs, Zoë Holmes, Matthias C. Caro, Nicholas Ezzell, Hsin-Yuan Huang, Lukasz Cincio, Andrew T. Sornborger, and Patrick J. Coles. “Dynamical simulation via quantum machine learning with provable generalization”. Phys. Rev. Res. 6 (1 Mar. 2024), p. 013241. https://doi.org/10.1103/PhysRevResearch.6.013241
F. Liu, L. Dadi, V. Cevher. Learning with norm constrained, over-parameterized, two-layer neural networks. Journal of Machine Learning Research (JMLR). 2024
Branchini, Nicola, Virginia Aglietti, Neil Dhir, and Theodoros Damoulas. "Causal entropy optimization." In International Conference on Artificial Intelligence and Statistics, pp. 8586-8605. PMLR, (AISTATS) 2023.
Zennaro, F. M., Drávucz, M., Apachitei, G., Widanage, W. D., & Damoulas, T. (2023, August). Jointly learning consistent causal abstractions over multiple interventional distributions. In Conference on Causal Learning and Reasoning (pp. 88-121), PMLR, (CLeaR) 2023
Wu, Yongtao, Fanghui Liu, Grigorios Chrysos, and Volkan Cevher. "On the convergence of encoder-only shallow transformers." Advances in Neural Information Processing Systems (NeurIPS), 2023.
Matthias C. Caro. “From undecidability of non-triviality and finiteness to undecidability of learnability”. In: International Journal of Approximate Reasoning 163, 109057 (2023). https://doi.org/10.1016/j.ijar.2023.109057.
Matthias C. Caro, Hsin-Yuan Huang, Nicholas Ezzell, Joe Gibbs, Andrew T. Sornborger, LukaszCincio, Patrick J. Coles, and Zoë Holmes. “Out-of-distribution generalization for learningquantum dynamics”.
Markus Hasenöhrl and Matthias C. Caro. "On the Generators of Quantum Dynamical Semigroups with Invariant Subalgebras". Open Systems & Information Dynamics 30.01 (2023): 2350001. https://doi.org/10.1142/S1230161223500014
Perera, Shanaka, Virginia Aglietti, and Theodoros Damoulas. "On the competitive facility location problem with a Bayesian spatial interaction model.", Journal of the Royal Statistical Society Series C: Applied Statistics, 72.1 (2023): 165-187
