Stefanie Jegelka

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 155

Top Venues

CoRR

NeurIPS

ICML

ICLR

Publications

Yinan Huang, William Lu, Joshua Robinson, Yu Yang, Muhan Zhang, Stefanie Jegelka, Pan Li
On the Stability of Expressive Positional Encodings for Graphs. ICLR (2024)
Thien Le, Luana Ruiz, Stefanie Jegelka
A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs. ICLR (2024)
Sharut Gupta, Chenyu Wang, Yifei Wang, Tommi S. Jaakkola, Stefanie Jegelka
In-Context Symmetries: Self-Supervised Learning through Contextual World Models. CoRR (2024)
Xinyi Wu, Amir Ajorlou, Yifei Wang, Stefanie Jegelka, Ali Jadbabaie
On the Role of Attention Masks and LayerNorm in Transformers. CoRR (2024)
Yifei Wang, Yuyang Wu, Zeming Wei, Stefanie Jegelka, Yisen Wang
A Theoretical Understanding of Self-Correction through In-context Alignment. CoRR (2024)
Sharut Gupta, Stefanie Jegelka, David Lopez-Paz, Kartik Ahuja
Context is Environment. ICLR (2024)
Derek Lim, Moe Putterman, Robin Walters, Haggai Maron, Stefanie Jegelka
The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof. CoRR (2024)
Bobak T. Kiani, Thien Le, Hannah Lawrence, Stefanie Jegelka, Melanie Weber
On the hardness of learning under symmetries. ICLR (2024)
Sharut Gupta, Joshua Robinson, Derek Lim, Soledad Villar, Stefanie Jegelka
Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning. ICLR (2024)
Sitao Luan, Chenqing Hua, Qincheng Lu, Liheng Ma, Lirong Wu, Xinyu Wang, Minkai Xu, Xiao-Wen Chang, Doina Precup, Rex Ying, Stan Z. Li, Jian Tang, Guy Wolf, Stefanie Jegelka
The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges. CoRR (2024)
George Ma, Yifei Wang, Derek Lim, Stefanie Jegelka, Yisen Wang
A Canonization Perspective on Invariant and Equivariant Learning. CoRR (2024)
Behrooz Tahmasebi, Ashkan Soleymani, Dara Bahri, Stefanie Jegelka, Patrick Jaillet
A Universal Class of Sharpness-Aware Minimization Algorithms. CoRR (2024)
Yifei Wang, Wenhan Ma, Stefanie Jegelka, Yisen Wang
How to Craft Backdoors with Unlabeled Data Alone? CoRR (2024)
Bobak T. Kiani, Thien Le, Hannah Lawrence, Stefanie Jegelka, Melanie Weber
On the hardness of learning under symmetries. CoRR (2024)
Christopher Morris, Nadav Dym, Haggai Maron, Ismail Ilkan Ceylan, Fabrizio Frasca, Ron Levie, Derek Lim, Michael Bronstein, Martin Grohe, Stefanie Jegelka
Future Directions in Foundations of Graph Machine Learning. CoRR (2024)
Sharut Gupta, Joshua Robinson, Derek Lim, Soledad Villar, Stefanie Jegelka
Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning. CoRR (2023)
Derek Lim, Joshua David Robinson, Lingxiao Zhao, Tess E. Smidt, Suvrit Sra, Haggai Maron, Stefanie Jegelka
Sign and Basis Invariant Networks for Spectral Graph Representation Learning. ICLR (2023)
Behrooz Tahmasebi, Stefanie Jegelka
The Exact Sample Complexity Gain from Invariances for Kernel Regression. NeurIPS (2023)
Khashayar Gatmiry, Zhiyuan Li, Tengyu Ma, Sashank Reddi, Stefanie Jegelka, Ching-Yao Chuang
What is the Inductive Bias of Flatness Regularization? A Study of Deep Matrix Factorization Models. NeurIPS (2023)
Yinan Huang, William Lu, Joshua Robinson, Yu Yang, Muhan Zhang, Stefanie Jegelka, Pan Li
On the Stability of Expressive Positional Encodings for Graph Neural Networks. CoRR (2023)
Ryotaro Okabe, Shangjie Xue, Jiankai Yu, Tongtong Liu, Benoit Forget, Stefanie Jegelka, Gordon Kohse, Lin-wen Hu, Mingda Li
Tetris-inspired detector with neural network for radiation mapping. CoRR (2023)
Thien Le, Luana Ruiz, Stefanie Jegelka
A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs. CoRR (2023)
Behrooz Tahmasebi, Stefanie Jegelka
Sample Complexity Bounds for Estimating Probability Divergences under Invariances. CoRR (2023)
Morris Yau, Eric Lu, Nikolaos Karalias, Jessica Xu, Stefanie Jegelka
Are Graph Neural Networks Optimal Approximation Algorithms? CoRR (2023)
Sharut Gupta, Stefanie Jegelka, David Lopez-Paz, Kartik Ahuja
Context is Environment. CoRR (2023)
Derek Lim, Joshua Robinson, Stefanie Jegelka, Haggai Maron
Expressive Sign Equivariant Networks for Spectral Geometric Learning. NeurIPS (2023)
Derek Lim, Joshua Robinson, Stefanie Jegelka, Haggai Maron
Expressive Sign Equivariant Networks for Spectral Geometric Learning. CoRR (2023)
Ching-Yao Chuang, Varun Jampani, Yuanzhen Li, Antonio Torralba, Stefanie Jegelka
Debiasing Vision-Language Models via Biased Prompts. CoRR (2023)
Thien Le, Stefanie Jegelka
Limits, approximation and size transferability for GNNs on sparse graphs via graphops. CoRR (2023)
Behrooz Tahmasebi, Derek Lim, Stefanie Jegelka
The Power of Recursion in Graph Neural Networks for Counting Substructures. AISTATS (2023)
Michael Murphy, Stefanie Jegelka, Ernest Fraenkel, Tobias Kind, David Healey, Thomas Butler
Efficiently predicting high resolution mass spectra with graph neural networks. CoRR (2023)
Thien Le, Stefanie Jegelka
Limits, approximation and size transferability for GNNs on sparse graphs via graphops. NeurIPS (2023)
Behrooz Tahmasebi, Stefanie Jegelka
The Exact Sample Complexity Gain from Invariances for Kernel Regression on Manifolds. CoRR (2023)
Khashayar Gatmiry, Zhiyuan Li, Ching-Yao Chuang, Sashank Reddi, Tengyu Ma, Stefanie Jegelka
The Inductive Bias of Flatness Regularization for Deep Matrix Factorization. CoRR (2023)
Ching-Yao Chuang, Stefanie Jegelka, David Alvarez-Melis
InfoOT: Information Maximizing Optimal Transport. ICML (2023)
Michael Murphy, Stefanie Jegelka, Ernest Fraenkel, Tobias Kind, David Healey, Thomas Butler
Efficiently predicting high resolution mass spectra with graph neural networks. ICML (2023)
Ching-Yao Chuang, R. Devon Hjelm, Xin Wang, Vibhav Vineet, Neel Joshi, Antonio Torralba, Stefanie Jegelka, Yale Song
Robust Contrastive Learning against Noisy Views. CVPR (2022)
Nikolaos Karalias, Joshua Robinson, Andreas Loukas, Stefanie Jegelka
Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions. NeurIPS (2022)
Nikolaos Karalias, Joshua Robinson, Andreas Loukas, Stefanie Jegelka
Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions. CoRR (2022)
Martin Grohe, Stephan Günnemann, Stefanie Jegelka, Christopher Morris
Graph Embeddings: Theory meets Practice (Dagstuhl Seminar 22132). Dagstuhl Reports 12 (3) (2022)
Thien Le, Stefanie Jegelka
Training invariances and the low-rank phenomenon: beyond linear networks. CoRR (2022)
Nisha Chandramoorthy, Andreas Loukas, Khashayar Gatmiry, Stefanie Jegelka
On the generalization of learning algorithms that do not converge. CoRR (2022)
Ching-Yao Chuang, R. Devon Hjelm, Xin Wang, Vibhav Vineet, Neel Joshi, Antonio Torralba, Stefanie Jegelka, Yale Song
Robust Contrastive Learning against Noisy Views. CoRR (2022)
Ching-Yao Chuang, Stefanie Jegelka
Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks. NeurIPS (2022)
Ching-Yao Chuang, Stefanie Jegelka
Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks. CoRR (2022)
Stefanie Jegelka
Theory of Graph Neural Networks: Representation and Learning. CoRR (2022)
Thien Le, Stefanie Jegelka
Training invariances and the low-rank phenomenon: beyond linear networks. ICLR (2022)
Nisha Chandramoorthy, Andreas Loukas, Khashayar Gatmiry, Stefanie Jegelka
On the generalization of learning algorithms that do not converge. NeurIPS (2022)
Derek Lim, Joshua Robinson, Lingxiao Zhao, Tess Smidt, Suvrit Sra, Haggai Maron, Stefanie Jegelka
Sign and Basis Invariant Networks for Spectral Graph Representation Learning. CoRR (2022)
Khashayar Gatmiry, Stefanie Jegelka, Jonathan A. Kelner
Optimization and Adaptive Generalization of Three layer Neural Networks. ICLR (2022)
Tasuku Soma, Khashayar Gatmiry, Stefanie Jegelka
Optimal algorithms for group distributionally robust optimization and beyond. CoRR (2022)
Ching-Yao Chuang, Stefanie Jegelka, David Alvarez-Melis
InfoOT: Information Maximizing Optimal Transport. CoRR (2022)
Ching-Yao Chuang, Youssef Mroueh, Kristjan H. Greenewald, Antonio Torralba, Stefanie Jegelka
Measuring Generalization with Optimal Transport. NeurIPS (2021)
Joshua Robinson, Li Sun, Ke Yu, Kayhan Batmanghelich, Stefanie Jegelka, Suvrit Sra
Can contrastive learning avoid shortcut solutions? NeurIPS (2021)
Keyulu Xu, Mozhi Zhang, Stefanie Jegelka, Kenji Kawaguchi
Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth. CoRR (2021)
Alkis Gotovos, Rebekka Burkholz, John Quackenbush, Stefanie Jegelka
Scaling up Continuous-Time Markov Chains Helps Resolve Underspecification. CoRR (2021)
Keyulu Xu, Mozhi Zhang, Jingling Li, Simon Shaolei Du, Ken-ichi Kawarabayashi, Stefanie Jegelka
How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks. ICLR (2021)
Andreas Loukas, Marinos Poiitis, Stefanie Jegelka
What training reveals about neural network complexity. NeurIPS (2021)
Ching-Yao Chuang, Youssef Mroueh, Kristjan H. Greenewald, Antonio Torralba, Stefanie Jegelka
Measuring Generalization with Optimal Transport. CoRR (2021)
Alkis Gotovos, Rebekka Burkholz, John Quackenbush, Stefanie Jegelka
Scaling up Continuous-Time Markov Chains Helps Resolve Underspecification. NeurIPS (2021)
Andreas Loukas, Marinos Poiitis, Stefanie Jegelka
What training reveals about neural network complexity. CoRR (2021)
Joshua David Robinson, Ching-Yao Chuang, Suvrit Sra, Stefanie Jegelka
Contrastive Learning with Hard Negative Samples. ICLR (2021)
Peiyuan Liao, Han Zhao, Keyulu Xu, Tommi S. Jaakkola, Geoffrey J. Gordon, Stefanie Jegelka, Ruslan Salakhutdinov
Information Obfuscation of Graph Neural Networks. ICML (2021)
Keyulu Xu, Mozhi Zhang, Stefanie Jegelka, Kenji Kawaguchi
Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth. ICML (2021)
Joshua Robinson, Li Sun, Ke Yu, Kayhan Batmanghelich, Stefanie Jegelka, Suvrit Sra
Can contrastive learning avoid shortcut solutions? CoRR (2021)
Peiyuan Liao, Han Zhao, Keyulu Xu, Tommi S. Jaakkola, Geoffrey J. Gordon, Stefanie Jegelka, Ruslan Salakhutdinov
Graph Adversarial Networks: Protecting Information against Adversarial Attacks. CoRR (2020)
Joshua Robinson, Stefanie Jegelka, Suvrit Sra
Strength from Weakness: Fast Learning Using Weak Supervision. CoRR (2020)
Ching-Yao Chuang, Joshua Robinson, Yen-Chen Lin, Antonio Torralba, Stefanie Jegelka
Debiased Contrastive Learning. NeurIPS (2020)
Khashayar Gatmiry, Maryam Aliakbarpour, Stefanie Jegelka
Testing Determinantal Point Processes. CoRR (2020)
Vikas K. Garg, Stefanie Jegelka, Tommi S. Jaakkola
Generalization and Representational Limits of Graph Neural Networks. CoRR (2020)
Ching-Yao Chuang, Joshua Robinson, Yen-Chen Lin, Antonio Torralba, Stefanie Jegelka
Debiased Contrastive Learning. CoRR (2020)
Yossi Arjevani, Joan Bruna, Bugra Can, Mert Gürbüzbalaban, Stefanie Jegelka, Hongzhou Lin
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method. CoRR (2020)
Ching-Yao Chuang, Antonio Torralba, Stefanie Jegelka
Estimating Generalization under Distribution Shifts via Domain-Invariant Representations. ICML (2020)
Marwa El Halabi, Stefanie Jegelka
Optimal approximation for unconstrained non-submodular minimization. ICML (2020)
Yossi Arjevani, Amit Daniely, Stefanie Jegelka, Hongzhou Lin
On the Complexity of Minimizing Convex Finite Sums Without Using the Indices of the Individual Functions. CoRR (2020)
Khashayar Gatmiry, Maryam Aliakbarpour, Stefanie Jegelka
Testing Determinantal Point Processes. NeurIPS (2020)
Sebastian Curi, Kfir Y. Levy, Stefanie Jegelka, Andreas Krause
Adaptive Sampling for Stochastic Risk-Averse Learning. NeurIPS (2020)
Behrooz Tahmasebi, Stefanie Jegelka
Counting Substructures with Higher-Order Graph Neural Networks: Possibility and Impossibility Results. CoRR (2020)
Ching-Yao Chuang, Antonio Torralba, Stefanie Jegelka
Estimating Generalization under Distribution Shifts via Domain-Invariant Representations. CoRR (2020)
Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S. Du, Ken-ichi Kawarabayashi, Stefanie Jegelka
What Can Neural Networks Reason About? ICLR (2020)
Vikas K. Garg, Stefanie Jegelka, Tommi S. Jaakkola
Generalization and Representational Limits of Graph Neural Networks. ICML (2020)
Joshua Robinson, Stefanie Jegelka, Suvrit Sra
Strength from Weakness: Fast Learning Using Weak Supervision. ICML (2020)
Johannes Kirschner, Ilija Bogunovic, Stefanie Jegelka, Andreas Krause
Distributionally Robust Bayesian Optimization. CoRR (2020)
Yossi Arjevani, Joan Bruna, Bugra Can, Mert Gürbüzbalaban, Stefanie Jegelka, Hongzhou Lin
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method. NeurIPS (2020)
Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S. Du, Ken-ichi Kawarabayashi, Stefanie Jegelka
How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks. CoRR (2020)
Johannes Kirschner, Ilija Bogunovic, Stefanie Jegelka, Andreas Krause
Distributionally Robust Bayesian Optimization. AISTATS (2020)
Joshua Robinson, Ching-Yao Chuang, Suvrit Sra, Stefanie Jegelka
Contrastive Learning with Hard Negative Samples. CoRR (2020)
Edward Kim, Zach Jensen, Alexander van Grootel, Kevin Huang, Matthew Staib, Sheshera Mysore, Haw-Shiuan Chang, Emma Strubell, Andrew McCallum, Stefanie Jegelka, Elsa Olivetti
Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks. J. Chem. Inf. Model. 60 (3) (2020)
Jingzhao Zhang, Hongzhou Lin, Stefanie Jegelka, Suvrit Sra, Ali Jadbabaie
Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions. ICML (2020)
Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka
How Powerful are Graph Neural Networks? ICLR (2019)
Joshua Robinson, Suvrit Sra, Stefanie Jegelka
Flexible Modeling of Diversity with Strongly Log-Concave Distributions. NeurIPS (2019)
Gal Shulkind, Stefanie Jegelka, Gregory W. Wornell
Sensor Array Design Through Submodular Optimization. IEEE Trans. Inf. Theory 65 (1) (2019)
Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S. Du, Ken-ichi Kawarabayashi, Stefanie Jegelka
What Can Neural Networks Reason About? CoRR (2019)
Charlotte Bunne, David Alvarez-Melis, Andreas Krause, Stefanie Jegelka
Learning Generative Models across Incomparable Spaces. ICML (2019)
Ching-Yao Chuang, Antonio Torralba, Stefanie Jegelka
The Role of Embedding Complexity in Domain-invariant Representations. CoRR (2019)
Matthew Staib, Stefanie Jegelka
Distributionally Robust Optimization and Generalization in Kernel Methods. CoRR (2019)
Joshua Robinson, Suvrit Sra, Stefanie Jegelka
Flexible Modeling of Diversity with Strongly Log-Concave Distributions. CoRR (2019)
David Alvarez-Melis, Stefanie Jegelka, Tommi S. Jaakkola
Towards Optimal Transport with Global Invariances. AISTATS (2019)
Matthew Staib, Stefanie Jegelka
Distributionally Robust Optimization and Generalization in Kernel Methods. NeurIPS (2019)
Marwa El Halabi, Stefanie Jegelka
Minimizing approximately submodular functions. CoRR (2019)

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Publication Activity (10 Years)

Top Topics

Top Venues

Stefanie Jegelka