Symmetries In-Context: Universal Self-Supervised Learning through Contextual World Models
Gupta, S., C. Wang, Y. Wang, T. Jaakkola, and S. Jegelka, 2024. Symmetries In-Context: Universal Self-Supervised Learning through Contextual World Models. Neural Information Processing Systems (NeurIPS) 2024. https://arxiv.org/abs/2405.18193
On the Hardness of Learning Under Symmetries
Kiani, B., T. Le, H. Lawrence, S. Jegelka, and M. Weber, 2024. On the Hardness of Learning Under Symmetries. International Conference on Learning Representations (ICLR) 2024. https://arxiv.org/abs/2401.01869
A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs
Le, T., L. Ruiz, and S. Jegelka, 2024. A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs. International Conference on Learning Representations (ICLR) 2024. https://arxiv.org/abs/2311.10610
Consistent Validation for Predictive Methods in Spatial Settings
Burt, D. R., Shen, Y., & Broderick, T. (2024). Consistent Validation for Predictive Methods in Spatial Settings. ICML 2024 AI for Science Workshop. https://openreview.net/forum?id=dUGehG7cRf
Multi-marginal Schrödinger Bridges with Iterative Reference Refinement
Shen, Y., Berlinghieri, R., & Broderick, T. (2025). Multi-marginal Schrödinger Bridges with Iterative Reference Refinement. The 28th International Conference on Artificial Intelligence and Statistics. https://openreview.net/forum?id=VcwZ3gtYFY
On Leveraging Pretrained GANs for Generation with Limited Data
Zhao, M., Y. Cong, and L. Carin, 2020. On Leveraging Pretrained GANs for Generation with Limited Data. In: Proceedings of Machine Learning Research 119, 11340–11351.
Generalization and Representational Limits of Graph Neural Networks
Garg, V.K., S. Jegelka, and T. Jaakkola, 2020. Generalization and Representational Limits of Graph Neural Networks. In: Proceedings of Machine Learning Research 119, 3419–3430.
Measuring the Robustness of Gaussian Processes to Kernel Choice
Stephenson, W.T., S. Ghosh, T.D. Nguyen, M. Yurochkin, S.K. Deshpande, and T. Broderick, 2022. Measuring the Robustness of Gaussian Processes to Kernel Choice. Proceedings of Machine Learning Research 151, 3308–3331.
CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information
Cheng, P., W. Hao, S. Dai, J. Liu, Z. Gan, and L. Carin, 2020. CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information. In: Proceedings of Machine Learning Research 119, 1779–1788.
Measuring Generalization with Optimal Transport
Chuang, C.-Y., Y. Mroueh, K. Greenewald, A. Torralba, and S. Jegelka, 2021. Measuring Generalization with Optimal Transport. In: Neural Information Processing Systems (NeurIPS) 2021, December 6–14, 2021.
Gaussian Processes at the Helm(holtz): A More Fluid Model for Ocean Currents
Berlinghieri, R., B.L. Trippe, D.R. Burt, R. Giordano, K. Srinivasan, T. Özgökmen, J. Xia, and T. Broderick, 2023. Gaussian Processes at the Helm(holtz): A More Fluid Model for Ocean Currents. In: Proceedings of Machine Learning Research 202, 2113–2163.
The Exact Sample Complexity Gain from Invariances for Kernel Regression
Tahmasebi, B. and S. Jegelka, 2023. The Exact Sample Complexity Gain from Invariances for Kernel Regression. In: Neural Information Processing Systems (NeurIPS) 2023, New Orleans, December 10–16, 2023. doi:10.48550/arXiv.2303.14269
Evaluation of Deep Neural Operator Models toward Ocean Forecasting
Rajagopal, E., A.N.S. Babu, T. Ryu, P.J. Haley, Jr., C. Mirabito, and P.F.J. Lermusiaux, 2023. Evaluation of Deep Neural Operator Models toward Ocean Forecasting. In: OCEANS '23 IEEE/MTS Gulf Coast, 25–28 September 2023. doi:10.23919/OCEANS52994.2023.10337380
On the Generalization of Learning Algorithms That Do Not Converge
Chandramoorthy, N., A. Loukas, K. Gatmiry, S. Jegelka, 2022. On the Generalization of Learning Algorithms That Do Not Converge. In: Neural Information Processing Systems (NeurIPS) 2022, New Orleans, November 28–December 9, 2022.
Neural Closure Model for Dynamic Mode Decomposition Forecasts
Ryu, T., Suresh Babu, A., and Lermusiaux, P., 2022. Neural Closure Model for Dynamic Mode Decomposition Forecasts. In: Model Reduction and Surrogate Modelling (MORE) 2022, Berlin, September 19–23, 2022.
Sparse Regression and Adaptive Feature Generation for the Discovery of Dynamical Systems
Kulkarni, C.S., A. Gupta, and P.F.J. Lermusiaux, 2020. Sparse Regression and Adaptive Feature Generation for the Discovery of Dynamical Systems. In: Darema, F., E. Blasch, S. Ravela, and A. Aved (eds.), Dynamic Data Driven Application Systems. DDDAS 2020. Lecture Notes in Computer Science 12312, 208–216. doi:10.1007/978-3-030-61725-7_25
