| 2026 | COLT | Efficient Learning and Symmetry Discovery under Exact Invariances. | Ashkan Soleymani, Behrooz Tahmasebi, Patrick Jaillet, Stefanie Jegelka |
| 2026 | COLT | Data Augmentation: A Fourier Analysis Perspective. | Behrooz Tahmasebi, Melanie Weber, Stefanie Jegelka |
| 2025 | AISTATS | A Robust Kernel Statistical Test of Invariance: Detecting Subtle Asymmetries. | Ashkan Soleymani, Behrooz Tahmasebi, Stefanie Jegelka, Patrick Jaillet |
| 2025 | AISTATS | Regularity in Canonicalized Models: A Theoretical Perspective. | Behrooz Tahmasebi, Stefanie Jegelka |
| 2025 | COLT | Computing Optimal Regularizers for Online Linear Optimization. | Khashayar Gatmiry, Jon Schneider, Stefanie Jegelka |
| 2025 | ICLR | What is Wrong with Perplexity for Long-context Language Modeling? | Lizhe Fang, Yifei Wang, Zhaoyang Liu, Chenheng Zhang, Stefanie Jegelka, Jinyang Gao, Bolin Ding, Yisen Wang |
| 2025 | ICLR | Higher-Order Graphon Neural Networks: Approximation and Cut Distance. | Daniel Herbst, Stefanie Jegelka |
| 2025 | ICLR | Learning Efficient Positional Encodings with Graph Neural Networks. | Charilaos I. Kanatsoulis, Evelyn Choi, Stefanie Jegelka, Jure Leskovec, Alejandro Ribeiro |
| 2025 | ICLR | Generalization, Expressivity, and Universality of Graph Neural Networks on Attributed Graphs. | Levi Rauchwerger, Stefanie Jegelka, Ron Levie |
| 2025 | ICLR | Generalization Bounds for Canonicalization: A Comparative Study with Group Averaging. | Behrooz Tahmasebi, Stefanie Jegelka |
| 2025 | ICLR | An Information Criterion for Controlled Disentanglement of Multimodal Data. | Chenyu Wang, Sharut Gupta, Xinyi Zhang, Sana Tonekaboni, Stefanie Jegelka, Tommi S. Jaakkola, Caroline Uhler |
| 2025 | ICLR | Beyond Interpretability: The Gains of Feature Monosemanticity on Model Robustness. | Qi Zhang, Yifei Wang, Jingyi Cui, Xiang Pan, Qi Lei, Stefanie Jegelka, Yisen Wang |
| 2025 | ICML | On the Emergence of Position Bias in Transformers. | Xinyi Wu, Yifei Wang, Stefanie Jegelka, Ali Jadbabaie |
| 2025 | ICML | Learning with Exact Invariances in Polynomial Time. | Ashkan Soleymani, Behrooz Tahmasebi, Stefanie Jegelka, Patrick Jaillet |
| 2025 | ICML | Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation. | Tiansheng Wen, Yifei Wang, Zequn Zeng, Zhong Peng, Yudi Su, Xinyang Liu, Bo Chen, Hongwei Liu, Stefanie Jegelka, Chenyu You |
| 2024 | ICLR | Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning. | Sharut Gupta, Joshua Robinson, Derek Lim, Soledad Villar, Stefanie Jegelka |
| 2024 | ICLR | Context is Environment. | Sharut Gupta, Stefanie Jegelka, David Lopez-Paz, Kartik Ahuja |
| 2024 | ICLR | On the Stability of Expressive Positional Encodings for Graphs. | Yinan Huang, William Lu, Joshua Robinson, Yu Yang, Muhan Zhang, Stefanie Jegelka, Pan Li |
| 2024 | ICLR | On the hardness of learning under symmetries. | Bobak T. Kiani, Thien Le, Hannah Lawrence, Stefanie Jegelka, Melanie Weber |
| 2024 | ICLR | A Poincar Inequality and Consistency Results for Signal Sampling on Large Graphs. | Thien Le, Luana Ruiz, Stefanie Jegelka |
| 2024 | ICML | Position: Future Directions in the Theory of Graph Machine Learning. | Christopher Morris, Fabrizio Frasca, Nadav Dym, Haggai Maron, Ismail Ilkan Ceylan, Ron Levie, Derek Lim, Michael M. Bronstein, Martin Grohe, Stefanie Jegelka |
| 2024 | ICML | Simplicity Bias via Global Convergence of Sharpness Minimization. | Khashayar Gatmiry, Zhiyuan Li, Sashank J. Reddi, Stefanie Jegelka |
| 2024 | ICML | Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning? | Khashayar Gatmiry, Nikunj Saunshi, Sashank J. Reddi, Stefanie Jegelka, Sanjiv Kumar |
| 2024 | ICML | Sample Complexity Bounds for Estimating Probability Divergences under Invariances. | Behrooz Tahmasebi, Stefanie Jegelka |
| 2024 | ICML | A Universal Class of Sharpness-Aware Minimization Algorithms. | Behrooz Tahmasebi, Ashkan Soleymani, Dara Bahri, Stefanie Jegelka, Patrick Jaillet |
| 2023 | AISTATS | The Power of Recursion in Graph Neural Networks for Counting Substructures. | Behrooz Tahmasebi, Derek Lim, Stefanie Jegelka |
| 2023 | ICLR | Sign and Basis Invariant Networks for Spectral Graph Representation Learning. | Derek Lim, Joshua David Robinson, Lingxiao Zhao, Tess E. Smidt, Suvrit Sra, Haggai Maron, Stefanie Jegelka |
| 2023 | ICML | InfoOT: Information Maximizing Optimal Transport. | Ching-Yao Chuang, Stefanie Jegelka, David Alvarez-Melis |
| 2023 | ICML | Efficiently predicting high resolution mass spectra with graph neural networks. | Michael Murphy, Stefanie Jegelka, Ernest Fraenkel, Tobias Kind, David Healey, Thomas Butler |
| 2022 | CVPR | Robust Contrastive Learning against Noisy Views. | Ching-Yao Chuang, R. Devon Hjelm, Xin Wang, Vibhav Vineet, Neel Joshi, Antonio Torralba, Stefanie Jegelka, Yale Song |
| 2022 | ICLR | Optimization and Adaptive Generalization of Three layer Neural Networks. | Khashayar Gatmiry, Stefanie Jegelka, Jonathan A. Kelner |
| 2022 | ICLR | Training invariances and the low-rank phenomenon: beyond linear networks. | Thien Le, Stefanie Jegelka |
| 2021 | ICLR | Contrastive Learning with Hard Negative Samples. | Joshua David Robinson, Ching-Yao Chuang, Suvrit Sra, Stefanie Jegelka |
| 2021 | ICLR | How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks. | Keyulu Xu, Mozhi Zhang, Jingling Li, Simon Shaolei Du, Ken-ichi Kawarabayashi, Stefanie Jegelka |
| 2021 | ICML | Information Obfuscation of Graph Neural Networks. | Peiyuan Liao, Han Zhao, Keyulu Xu, Tommi S. Jaakkola, Geoffrey J. Gordon, Stefanie Jegelka, Ruslan Salakhutdinov |
| 2021 | ICML | Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth. | Keyulu Xu, Mozhi Zhang, Stefanie Jegelka, Kenji Kawaguchi |
| 2020 | AISTATS | Distributionally Robust Bayesian Optimization. | Johannes Kirschner, Ilija Bogunovic, Stefanie Jegelka, Andreas Krause |
| 2020 | ICLR | What Can Neural Networks Reason About? | Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S. Du, Ken-ichi Kawarabayashi, Stefanie Jegelka |
| 2020 | ICML | Estimating Generalization under Distribution Shifts via Domain-Invariant Representations. | Ching-Yao Chuang, Antonio Torralba, Stefanie Jegelka |
| 2020 | ICML | Generalization and Representational Limits of Graph Neural Networks. | Vikas K. Garg, Stefanie Jegelka, Tommi S. Jaakkola |
| 2020 | ICML | Optimal approximation for unconstrained non-submodular minimization. | Marwa El Halabi, Stefanie Jegelka |
| 2020 | ICML | Strength from Weakness: Fast Learning Using Weak Supervision. | Joshua Robinson, Stefanie Jegelka, Suvrit Sra |
| 2020 | ICML | Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions. | Jingzhao Zhang, Hongzhou Lin, Stefanie Jegelka, Suvrit Sra, Ali Jadbabaie |
| 2019 | ACL | Are Girls Neko or Shōjo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization. | Mozhi Zhang, Keyulu Xu, Ken-ichi Kawarabayashi, Stefanie Jegelka, Jordan L. Boyd-Graber |
| 2019 | AISTATS | Towards Optimal Transport with Global Invariances. | David Alvarez-Melis, Stefanie Jegelka, Tommi S. Jaakkola |
| 2019 | AISTATS | Distributionally Robust Submodular Maximization. | Matthew Staib, Bryan Wilder, Stefanie Jegelka |
| 2019 | ICLR | How Powerful are Graph Neural Networks? | Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka |
| 2019 | ICML | Learning Generative Models across Incomparable Spaces. | Charlotte Bunne, David Alvarez-Melis, Andreas Krause, Stefanie Jegelka |
| 2018 | AAAI | Streaming Non-Monotone Submodular Maximization: Personalized Video Summarization on the Fly. | Baharan Mirzasoleiman, Stefanie Jegelka, Andreas Krause |
| 2018 | AISTATS | Structured Optimal Transport. | David Alvarez-Melis, Tommi S. Jaakkola, Stefanie Jegelka |
| 2018 | AISTATS | Batched Large-scale Bayesian Optimization in High-dimensional Spaces. | Zi Wang, Clement Gehring, Pushmeet Kohli, Stefanie Jegelka |
| 2018 | ICLR | Distributional Adversarial Networks. | Chengtao Li, David Alvarez-Melis, Keyulu Xu, Stefanie Jegelka, Suvrit Sra |
| 2018 | ICML | Representation Learning on Graphs with Jumping Knowledge Networks. | Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka |
| 2018 | UAI | Discrete Sampling using Semigradient-based Product Mixtures. | Alkis Gotovos, S. Hamed Hassani, Andreas Krause, Stefanie Jegelka |
| 2017 | CVPR | Deep Metric Learning via Facility Location. | Hyun Oh Song, Stefanie Jegelka, Vivek Rathod, Kevin Murphy |
| 2017 | ICASSP | Multiple wavelength sensing array design. | Gal Shulkind, Stefanie Jegelka, Gregory W. Wornell |
| 2017 | ICML | Robust Budget Allocation via Continuous Submodular Functions. | Matthew Staib, Stefanie Jegelka |
| 2017 | ICML | Max-value Entropy Search for Efficient Bayesian Optimization. | Zi Wang, Stefanie Jegelka |
| 2017 | ICML | Batched High-dimensional Bayesian Optimization via Structural Kernel Learning. | Zi Wang, Chengtao Li, Stefanie Jegelka, Pushmeet Kohli |
| 2017 | ICRA | Focused model-learning and planning for non-Gaussian continuous state-action systems. | Zi Wang, Stefanie Jegelka, Leslie Pack Kaelbling, Toms Lozano-Prez |
| 2016 | AISTATS | Efficient Sampling for k-Determinantal Point Processes. | Chengtao Li, Stefanie Jegelka, Suvrit Sra |
| 2016 | AISTATS | Optimization as Estimation with Gaussian Processes in Bandit Settings. | Zi Wang, Bolei Zhou, Stefanie Jegelka |
| 2016 | CVPR | Deep Metric Learning via Lifted Structured Feature Embedding. | Hyun Oh Song, Yu Xiang, Stefanie Jegelka, Silvio Savarese |
| 2016 | ICML | Fast DPP Sampling for Nystrom with Application to Kernel Methods. | Chengtao Li, Stefanie Jegelka, Suvrit Sra |
| 2016 | ICML | Gaussian quadrature for matrix inverse forms with applications. | Chengtao Li, Suvrit Sra, Stefanie Jegelka |
| 2014 | CVPR | Learning Scalable Discriminative Dictionary with Sample Relatedness. | Jiashi Feng, Stefanie Jegelka, Shuicheng Yan, Trevor Darrell |
| 2014 | ICML | On learning to localize objects with minimal supervision. | Hyun Oh Song, Ross B. Girshick, Stefanie Jegelka, Julien Mairal, Zad Harchaoui, Trevor Darrell |
| 2014 | UAI | Monotone Closure of Relaxed Constraints in Submodular Optimization: Connections Between Minimization and Maximization. | Rishabh K. Iyer, Stefanie Jegelka, Jeff A. Bilmes |
| 2013 | CVPR | A Principled Deep Random Field Model for Image Segmentation. | Pushmeet Kohli, Anton Osokin, Stefanie Jegelka |
| 2013 | ICML | Fast Semidifferential-based Submodular Function Optimization. | Rishabh K. Iyer, Stefanie Jegelka, Jeff A. Bilmes |
| 2011 | CVPR | Submodularity beyond submodular energies: Coupling edges in graph cuts. | Stefanie Jegelka, Jeff A. Bilmes |
| 2011 | ICML | Online Submodular Minimization for Combinatorial Structures. | Stefanie Jegelka, Jeff A. Bilmes |
| 2011 | ICML | Approximation Bounds for Inference using Cooperative Cuts. | Stefanie Jegelka, Jeff A. Bilmes |
| 2009 | ALT | Approximation Algorithms for Tensor Clustering. | Stefanie Jegelka, Suvrit Sra, Arindam Banerjee |
| 2009 | ICML | Solution stability in linear programming relaxations: graph partitioning and unsupervised learning. | Sebastian Nowozin, Stefanie Jegelka |
| 2009 | KI | Generalized Clustering via Kernel Embeddings. | Stefanie Jegelka, Arthur Gretton, Bernhard Schlkopf, Bharath K. Sriperumbudur, Ulrike von Luxburg |