On the Emergence of Position Bias in Transformers
Wu, X., Wang, Y., Jegelka, S. and Jadbabaie, A., On the Emergence of Position Bias in Transformers. In Forty-second International Conference on Machine Learning. https://doi.org/10.48550/arXiv.2502.01951
An Information Criterion for Controlled Disentanglement of Multimodal Data
Wang, C., Gupta, S., Zhang, X., Tonekaboni, S., Jegelka, S., Jaakkola, T. and Uhler, C., An Information Criterion for Controlled Disentanglement of Multimodal Data. In The Thirteenth International Conference on Learning Representations. https://doi.org/10.48550/arXiv.2410.23996
A Robust Kernel Statistical Test of Invariance: Detecting Subtle Asymmetries
Soleymani, A., Tahmasebi, B., Jegelka, S., & Jaillet, P. (2025). A Robust Kernel Statistical Test of Invariance: Detecting Subtle Asymmetries. The 28th International Conference on Artificial Intelligence and Statistics. https://openreview.net/forum?id=uPgGW67dSX
Learning with Exact Invariances in Polynomial Time
Soleymani, A., Tahmasebi, B., Jegelka, S., & Jaillet, P. (2025). Learning with Exact Invariances in Polynomial Time. Forty-Second International Conference on Machine Learning. https://openreview.net/forum?id=e46xNZhwl8
Use of Vision Transformer to Classify Sea Surface Phenomena in SAR Imagery
Xia, J., Romeiser, R., Zhang, W., & Özgökmen, T. (2025). Use of Vision Transformer to Classify Sea Surface Phenomena in SAR Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 18, 10937–10956. https://doi.org/10.1109/JSTARS.2025.3558673
Evaluation of Analytical Turbulence Closures for Quasi-Geostrophic Ocean Flows with Coastal Boundaries
Suresh Babu, A. N., Sadam, A., & Lermusiaux, P. F. J. (2025). Evaluation of Analytical Turbulence Closures for Quasi-Geostrophic Ocean Flows with Coastal Boundaries. OCEANS ’25 IEEE/MTS Great Lakes, 1–10. https://doi.org/10.23919/OCEANS59106.2025.11245082
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.
