Prenatal development of ocular dominance and orientation maps in a self-organizing model of V1. - Stefanie Jegelka; James A. Bednar; Risto Miikkulainen (Neurocomputing, 2006)

Consistent Minimization of Clustering Objective Functions. - Ulrike von Luxburg; Sébastien Bubeck; Stefanie Jegelka; Michael Kaufmann 001 (NIPS, 2007)

Fast Kernel ICA using an Approximate Newton Method. - Hao Shen 002; Stefanie Jegelka; Arthur Gretton (AISTATS, 2007)

Approximation Algorithms for Bregman Co-clustering and Tensor Clustering - Stefanie Jegelka; Suvrit Sra; Arindam Banerjee 001 (CoRR, 2008)

Approximation Algorithms for Tensor Clustering. - Stefanie Jegelka; Suvrit Sra; Arindam Banerjee 001 (ALT, 2009)

Solution stability in linear programming relaxations: graph partitioning and unsupervised learning. - Sebastian Nowozin; Stefanie Jegelka (ICML, 2009)

Generalized Clustering via Kernel Embeddings. - Stefanie Jegelka; Arthur Gretton; Bernhard Schölkopf; Bharath K. Sriperumbudur; Ulrike von Luxburg (KI, 2009)

Fast kernel-based independent component analysis. - Hao Shen 002; Stefanie Jegelka; Arthur Gretton (IEEE Trans. Signal Process., 2009)

Submodularity beyond submodular energies: Coupling edges in graph cuts. - Stefanie Jegelka; Jeff A. Bilmes (CVPR, 2011)

Online Submodular Minimization for Combinatorial Structures. - Stefanie Jegelka; Jeff A. Bilmes (ICML, 2011)

Approximation Bounds for Inference using Cooperative Cuts. - Stefanie Jegelka; Jeff A. Bilmes (ICML, 2011)

On fast approximate submodular minimization. - Stefanie Jegelka; Hui Lin 001; Jeff A. Bilmes (NIPS, 2011)

Combinatorial problems with submodular coupling in machine learning and computer vision. - Stefanie Sabrina Jegelka (2012)

A Principled Deep Random Field Model for Image Segmentation. - Pushmeet Kohli; Anton Osokin; Stefanie Jegelka (CVPR, 2013)

Fast Semidifferential-based Submodular Function Optimization. - Rishabh K. Iyer; Stefanie Jegelka; Jeff A. Bilmes (ICML (3), 2013)

Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions. - Rishabh K. Iyer; Stefanie Jegelka; Jeff A. Bilmes (NIPS, 2013)

Reflection methods for user-friendly submodular optimization. - Stefanie Jegelka; Francis R. Bach; Suvrit Sra (NIPS, 2013)

Optimistic Concurrency Control for Distributed Unsupervised Learning. - Xinghao Pan; Joseph E. Gonzalez; Stefanie Jegelka; Tamara Broderick; Michael I. Jordan (NIPS, 2013)

Efficient and accurate clustering for large-scale genetic mapping. - Veronika Strnadova; Aydin Buluç; Jarrod Chapman; John R. Gilbert; Joseph Gonzalez 001; Stefanie Jegelka; Daniel Rokhsar; Leonid Oliker (BIBM, 2014)

Learning Scalable Discriminative Dictionary with Sample Relatedness. - Jiashi Feng; Stefanie Jegelka; Shuicheng Yan; Trevor Darrell (CVPR, 2014)

On learning to localize objects with minimal supervision. - Hyun Oh Song; Ross B. Girshick; Stefanie Jegelka; Julien Mairal; Zaïd Harchaoui; Trevor Darrell (ICML, 2014)

On the Convergence Rate of Decomposable Submodular Function Minimization. - Robert Nishihara; Stefanie Jegelka; Michael I. Jordan (NIPS, 2014)

Parallel Double Greedy Submodular Maximization. - Xinghao Pan; Stefanie Jegelka; Joseph E. Gonzalez; Joseph K. Bradley; Michael I. Jordan (NIPS, 2014)

Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets. - Adarsh Prasad; Stefanie Jegelka; Dhruv Batra (NIPS, 2014)

Weakly-supervised Discovery of Visual Pattern Configurations. - Hyun Oh Song; Yong Jae Lee; Stefanie Jegelka; Trevor Darrell (NIPS, 2014)

Monotone Closure of Relaxed Constraints in Submodular Optimization: Connections Between Minimization and Maximization. - Rishabh K. Iyer; Stefanie Jegelka; Jeff A. Bilmes (UAI, 2014)

Graph Cuts with Interacting Edge Costs - Examples, Approximations, and Algorithms. - Stefanie Jegelka; Jeff A. Bilmes (CoRR, 2014)

One-Bit Object Detection: On learning to localize objects with minimal supervision. - Hyun Oh Song; Ross B. Girshick; Stefanie Jegelka; Julien Mairal; Zaïd Harchaoui; Trevor Darrell (CoRR, 2014)

An Interactive Approach to Solving Correspondence Problems. - Stefanie Jegelka; Ashish Kapoor; Eric Horvitz (Int. J. Comput. Vis., 2014)

Inferring and Learning from Neuronal Correspondences. - Ashish Kapoor; Edward Paxon Frady; Stefanie Jegelka; William B. Kristan Jr.; Eric Horvitz (CoRR, 2015)

Convex Optimization for Parallel Energy Minimization. - K. S. Sesh Kumar; Álvaro Barbero Jiménez; Stefanie Jegelka; Suvrit Sra; Francis R. Bach (CoRR, 2015)

Bounds on bilinear inverse forms via Gaussian quadrature with applications. - Chengtao Li; Suvrit Sra; Stefanie Jegelka (CoRR, 2015)

Efficient Sampling for k-Determinantal Point Processes. - Chengtao Li; Stefanie Jegelka; Suvrit Sra (AISTATS, 2016)

Optimization as Estimation with Gaussian Processes in Bandit Settings. - Zi Wang 004; Bolei Zhou; Stefanie Jegelka (AISTATS, 2016)

Deep Metric Learning via Lifted Structured Feature Embedding. - Hyun Oh Song; Yu Xiang; Stefanie Jegelka; Silvio Savarese (CVPR, 2016)

Fast DPP Sampling for Nystrom with Application to Kernel Methods. - Chengtao Li; Stefanie Jegelka; Suvrit Sra (ICML, 2016)

Gaussian quadrature for matrix inverse forms with applications. - Chengtao Li; Suvrit Sra; Stefanie Jegelka (ICML, 2016)

Cooperative Graphical Models. - Josip Djolonga; Stefanie Jegelka; Sebastian Tschiatschek; Andreas Krause 001 (NIPS, 2016)

Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling. - Chengtao Li; Suvrit Sra; Stefanie Jegelka (NIPS, 2016)

Auxiliary Image Regularization for Deep CNNs with Noisy Labels. - Samaneh Azadi; Jiashi Feng; Stefanie Jegelka; Trevor Darrell (ICLR (Poster), 2016)

Fast Sampling for Strongly Rayleigh Measures with Application to Determinantal Point Processes. - Chengtao Li; Stefanie Jegelka; Suvrit Sra (CoRR, 2016)

Learnable Structured Clustering Framework for Deep Metric Learning. - Hyun Oh Song; Stefanie Jegelka; Vivek Rathod; Kevin Murphy 002 (CoRR, 2016)

Deep Metric Learning via Facility Location. - Hyun Oh Song; Stefanie Jegelka; Vivek Rathod; Kevin Murphy 002 (CVPR, 2017)

Multiple wavelength sensing array design. - Gal Shulkind; Stefanie Jegelka; Gregory W. Wornell (ICASSP, 2017)

Robust Budget Allocation via Continuous Submodular Functions. - Matthew Staib; Stefanie Jegelka (ICML, 2017)

Max-value Entropy Search for Efficient Bayesian Optimization. - Zi Wang 004; Stefanie Jegelka (ICML, 2017)

Batched High-dimensional Bayesian Optimization via Structural Kernel Learning. - Zi Wang 004; Chengtao Li; Stefanie Jegelka; Pushmeet Kohli (ICML, 2017)

Focused model-learning and planning for non-Gaussian continuous state-action systems. - Zi Wang 004; Stefanie Jegelka; Leslie Pack Kaelbling; Tomás Lozano-Pérez (ICRA, 2017)

Polynomial time algorithms for dual volume sampling. - Chengtao Li; Stefanie Jegelka; Suvrit Sra (NIPS, 2017)

Parallel Streaming Wasserstein Barycenters. - Matthew Staib; Sebastian Claici; Justin Solomon 001; Stefanie Jegelka (NIPS, 2017)

Graph-Sparse Logistic Regression. - Alexander LeNail; Ludwig Schmidt; Johnathan Li; Tobias Ehrenberger; Karen Sachs; Stefanie Jegelka; Ernest Fraenkel (CoRR, 2017)

Graph cuts with interacting edge weights: examples, approximations, and algorithms. - Stefanie Jegelka; Jeff A. Bilmes (Math. Program., 2017)

Streaming Non-Monotone Submodular Maximization: Personalized Video Summarization on the Fly. - Baharan Mirzasoleiman; Stefanie Jegelka; Andreas Krause 001 (AAAI, 2018)

Structured Optimal Transport. - David Alvarez-Melis; Tommi S. Jaakkola; Stefanie Jegelka (AISTATS, 2018)

Batched Large-scale Bayesian Optimization in High-dimensional Spaces. - Zi Wang 004; Clement Gehring; Pushmeet Kohli; Stefanie Jegelka (AISTATS, 2018)

Distributional Adversarial Networks. - Chengtao Li; David Alvarez-Melis; Keyulu Xu; Stefanie Jegelka; Suvrit Sra (ICLR (Workshop), 2018)

Representation Learning on Graphs with Jumping Knowledge Networks. - Keyulu Xu; Chengtao Li; Yonglong Tian; Tomohiro Sonobe; Ken-ichi Kawarabayashi; Stefanie Jegelka (ICML, 2018)

Adversarially Robust Optimization with Gaussian Processes. - Ilija Bogunovic; Jonathan Scarlett; Stefanie Jegelka; Volkan Cevher (NeurIPS, 2018)

Provable Variational Inference for Constrained Log-Submodular Models. - Josip Djolonga; Stefanie Jegelka; Andreas Krause 001 (NeurIPS, 2018)

ResNet with one-neuron hidden layers is a Universal Approximator. - Hongzhou Lin; Stefanie Jegelka (NeurIPS, 2018)

Exponentiated Strongly Rayleigh Distributions. - Zelda E. Mariet; Suvrit Sra; Stefanie Jegelka (NeurIPS, 2018)

Discrete Sampling using Semigradient-based Product Mixtures. - Alkis Gotovos; S. Hamed Hassani; Andreas Krause 001; Stefanie Jegelka (UAI, 2018)

Robust GANs against Dishonest Adversaries. - Zhi Xu; Chengtao Li; Stefanie Jegelka (CoRR, 2018)

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 (ACL (1), 2019)

Towards Optimal Transport with Global Invariances. - David Alvarez-Melis; Stefanie Jegelka; Tommi S. Jaakkola (AISTATS, 2019)

Distributionally Robust Submodular Maximization. - Matthew Staib; Bryan Wilder; Stefanie Jegelka (AISTATS, 2019)

How Powerful are Graph Neural Networks? - Keyulu Xu; Weihua Hu; Jure Leskovec; Stefanie Jegelka (ICLR, 2019)

Learning Generative Models across Incomparable Spaces. - Charlotte Bunne; David Alvarez-Melis; Andreas Krause 001; Stefanie Jegelka (ICML, 2019)

Flexible Modeling of Diversity with Strongly Log-Concave Distributions. - Joshua Robinson 001; Suvrit Sra; Stefanie Jegelka (NeurIPS, 2019)

Distributionally Robust Optimization and Generalization in Kernel Methods. - Matthew Staib; Stefanie Jegelka (NeurIPS, 2019)

Minimizing approximately submodular functions. - Marwa El Halabi; Stefanie Jegelka (CoRR, 2019)

The Role of Embedding Complexity in Domain-invariant Representations. - Ching-Yao Chuang; Antonio Torralba 001; Stefanie Jegelka (CoRR, 2019)

Sensor Array Design Through Submodular Optimization. - Gal Shulkind; Stefanie Jegelka; Gregory W. Wornell (IEEE Trans. Inf. Theory, 2019)

Distributionally Robust Bayesian Optimization. - Johannes Kirschner; Ilija Bogunovic; Stefanie Jegelka; Andreas Krause 001 (AISTATS, 2020)

What Can Neural Networks Reason About? - Keyulu Xu; Jingling Li; Mozhi Zhang; Simon S. Du; Ken-ichi Kawarabayashi; Stefanie Jegelka (ICLR, 2020)

Estimating Generalization under Distribution Shifts via Domain-Invariant Representations. - Ching-Yao Chuang; Antonio Torralba 001; Stefanie Jegelka (ICML, 2020)

Generalization and Representational Limits of Graph Neural Networks. - Vikas K. Garg 001; Stefanie Jegelka; Tommi S. Jaakkola (ICML, 2020)

Optimal approximation for unconstrained non-submodular minimization. - Marwa El Halabi; Stefanie Jegelka (ICML, 2020)

Strength from Weakness: Fast Learning Using Weak Supervision. - Joshua Robinson 001; Stefanie Jegelka; Suvrit Sra (ICML, 2020)

Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions. - Jingzhao Zhang; Hongzhou Lin; Stefanie Jegelka; Suvrit Sra; Ali Jadbabaie (ICML, 2020)

IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method. - Yossi Arjevani; Joan Bruna; Bugra Can; Mert Gürbüzbalaban; Stefanie Jegelka; Hongzhou Lin (NeurIPS, 2020)

Debiased Contrastive Learning. - Ching-Yao Chuang; Joshua Robinson 001; Yen-Chen Lin; Antonio Torralba 001; Stefanie Jegelka (NeurIPS, 2020)

Adaptive Sampling for Stochastic Risk-Averse Learning. - Sebastian Curi; Kfir Y. Levy; Stefanie Jegelka; Andreas Krause 001 (NeurIPS, 2020)

Testing Determinantal Point Processes. - Khashayar Gatmiry; Maryam Aliakbarpour; Stefanie Jegelka (NeurIPS, 2020)

On the Complexity of Minimizing Convex Finite Sums Without Using the Indices of the Individual Functions. - Yossi Arjevani; Amit Daniely; Stefanie Jegelka; Hongzhou Lin (CoRR, 2020)

Graph Adversarial Networks: Protecting Information against Adversarial Attacks. - Peiyuan Liao; Han Zhao 002; Keyulu Xu; Tommi S. Jaakkola; Geoffrey J. Gordon; Stefanie Jegelka; Ruslan Salakhutdinov (CoRR, 2020)

Counting Substructures with Higher-Order Graph Neural Networks: Possibility and Impossibility Results. - Behrooz Tahmasebi; Stefanie Jegelka (CoRR, 2020)

Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks. - Edward Kim 004; Zach Jensen; Alexander van Grootel; Kevin Huang 004; Matthew Staib; Sheshera Mysore; Haw-Shiuan Chang; Emma Strubell; Andrew McCallum; Stefanie Jegelka; Elsa A. Olivetti (J. Chem. Inf. Model., 2020)

Contrastive Learning with Hard Negative Samples. - Joshua David Robinson; Ching-Yao Chuang; Suvrit Sra; Stefanie Jegelka (ICLR, 2021)

How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks. - Keyulu Xu; Mozhi Zhang; Jingling Li; Simon Shaolei Du; Ken-ichi Kawarabayashi; Stefanie Jegelka (ICLR, 2021)

Information Obfuscation of Graph Neural Networks. - Peiyuan Liao; Han Zhao 002; Keyulu Xu; Tommi S. Jaakkola; Geoffrey J. Gordon; Stefanie Jegelka; Ruslan Salakhutdinov (ICML, 2021)

Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth. - Keyulu Xu; Mozhi Zhang; Stefanie Jegelka; Kenji Kawaguchi (ICML, 2021)

Measuring Generalization with Optimal Transport. - Ching-Yao Chuang; Youssef Mroueh; Kristjan H. Greenewald; Antonio Torralba 001; Stefanie Jegelka (NeurIPS, 2021)

Scaling up Continuous-Time Markov Chains Helps Resolve Underspecification. - Alkis Gotovos; Rebekka Burkholz; John Quackenbush; Stefanie Jegelka (NeurIPS, 2021)

What training reveals about neural network complexity. - Andreas Loukas; Marinos Poiitis; Stefanie Jegelka (NeurIPS, 2021)

Can contrastive learning avoid shortcut solutions? - Joshua Robinson 001; Li Sun 010; Ke Yu; Kayhan Batmanghelich; Stefanie Jegelka; Suvrit Sra (NeurIPS, 2021)

Robust Contrastive Learning against Noisy Views. - Ching-Yao Chuang; R. Devon Hjelm; Xin Wang 066; Vibhav Vineet; Neel Joshi; Antonio Torralba 001; Stefanie Jegelka; Yale Song (CVPR, 2022)

Optimization and Adaptive Generalization of Three layer Neural Networks. - Khashayar Gatmiry; Stefanie Jegelka; Jonathan A. Kelner (ICLR, 2022)

Training invariances and the low-rank phenomenon: beyond linear networks. - Thien Le; Stefanie Jegelka (ICLR, 2022)

On the generalization of learning algorithms that do not converge. - Nisha Chandramoorthy; Andreas Loukas; Khashayar Gatmiry; Stefanie Jegelka (NeurIPS, 2022)

Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks. - Ching-Yao Chuang; Stefanie Jegelka (NeurIPS, 2022)

Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions. - Nikolaos Karalias; Joshua Robinson 001; Andreas Loukas; Stefanie Jegelka (NeurIPS, 2022)

Self-supervised learning of cell type specificity from immunohistochemical images. - Michael Murphy; Stefanie Jegelka; Ernest Fraenkel (Bioinform., 2022)

Theory of Graph Neural Networks: Representation and Learning. - Stefanie Jegelka (CoRR, 2022)

Optimal algorithms for group distributionally robust optimization and beyond. - Tasuku Soma; Khashayar Gatmiry; Stefanie Jegelka (CoRR, 2022)

Graph Embeddings: Theory meets Practice (Dagstuhl Seminar 22132). - Martin Grohe; Stephan Günnemann; Stefanie Jegelka; Christopher Morris 001 (Dagstuhl Reports, 2022)

The Power of Recursion in Graph Neural Networks for Counting Substructures. - Behrooz Tahmasebi; Derek Lim; Stefanie Jegelka (AISTATS, 2023)

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 (ICLR, 2023)

InfoOT: Information Maximizing Optimal Transport. - Ching-Yao Chuang; Stefanie Jegelka; David Alvarez-Melis (ICML, 2023)

Efficiently predicting high resolution mass spectra with graph neural networks. - Michael Murphy; Stefanie Jegelka; Ernest Fraenkel; Tobias Kind; David Healey; Thomas Butler (ICML, 2023)

What is the Inductive Bias of Flatness Regularization? A Study of Deep Matrix Factorization Models. - Khashayar Gatmiry; Zhiyuan Li 005; Tengyu Ma 001; Sashank J. Reddi; Stefanie Jegelka; Ching-Yao Chuang (NeurIPS, 2023)

Limits, approximation and size transferability for GNNs on sparse graphs via graphops. - Thien Le; Stefanie Jegelka (NeurIPS, 2023)

Expressive Sign Equivariant Networks for Spectral Geometric Learning. - Derek Lim; Joshua Robinson 001; Stefanie Jegelka; Haggai Maron (NeurIPS, 2023)

The Exact Sample Complexity Gain from Invariances for Kernel Regression. - Behrooz Tahmasebi; Stefanie Jegelka (NeurIPS, 2023)

Debiasing Vision-Language Models via Biased Prompts. - Ching-Yao Chuang; Varun Jampani; Yuanzhen Li; Antonio Torralba 001; Stefanie Jegelka (CoRR, 2023)

Tetris-inspired detector with neural network for radiation mapping. - Ryotaro Okabe; Shangjie Xue; Jiankai Yu; Tongtong Liu; Benoit Forget; Stefanie Jegelka; Gordon Kohse; Lin-wen Hu; Mingda Li (CoRR, 2023)

The Exact Sample Complexity Gain from Invariances for Kernel Regression on Manifolds. - Behrooz Tahmasebi; Stefanie Jegelka (CoRR, 2023)

The Inductive Bias of Flatness Regularization for Deep Matrix Factorization. - Khashayar Gatmiry; Zhiyuan Li 005; Ching-Yao Chuang; Sashank J. Reddi; Tengyu Ma 001; Stefanie Jegelka (CoRR, 2023)

On the Stability of Expressive Positional Encodings for Graph Neural Networks. - Yinan Huang; William Lu; Joshua Robinson 001; Yu Yang; Muhan Zhang; Stefanie Jegelka; Pan Li 005 (CoRR, 2023)

Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning. - Sharut Gupta; Joshua Robinson 001; Derek Lim; Soledad Villar; Stefanie Jegelka (ICLR, 2024)

Context is Environment. - Sharut Gupta; Stefanie Jegelka; David Lopez-Paz; Kartik Ahuja (ICLR, 2024)

On the Stability of Expressive Positional Encodings for Graphs. - Yinan Huang; William Lu; Joshua Robinson 001; Yu Yang; Muhan Zhang; Stefanie Jegelka; Pan Li 005 (ICLR, 2024)

On the hardness of learning under symmetries. - Bobak T. Kiani; Thien Le; Hannah Lawrence; Stefanie Jegelka; Melanie Weber 001 (ICLR, 2024)

A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs. - Thien Le; Luana Ruiz; Stefanie Jegelka (ICLR, 2024)

Position: Future Directions in the Theory of Graph Machine Learning. - Christopher Morris 001; Fabrizio Frasca; Nadav Dym; Haggai Maron; Ismail Ilkan Ceylan; Ron Levie; Derek Lim; Michael M. Bronstein; Martin Grohe; Stefanie Jegelka (ICML, 2024)

Simplicity Bias via Global Convergence of Sharpness Minimization. - Khashayar Gatmiry; Zhiyuan Li 005; Sashank J. Reddi; Stefanie Jegelka (ICML, 2024)

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 (ICML, 2024)

Sample Complexity Bounds for Estimating Probability Divergences under Invariances. - Behrooz Tahmasebi; Stefanie Jegelka (ICML, 2024)

A Universal Class of Sharpness-Aware Minimization Algorithms. - Behrooz Tahmasebi; Ashkan Soleymani; Dara Bahri; Stefanie Jegelka; Patrick Jaillet (ICML, 2024)

Understanding the Role of Equivariance in Self-supervised Learning. - Yifei Wang 001; Kaiwen Hu; Sharut Gupta; Ziyu Ye; Yisen Wang 001; Stefanie Jegelka (NeurIPS, 2024)

A Theoretical Understanding of Self-Correction through In-context Alignment. - Yifei Wang 001; Yuyang Wu; Zeming Wei; Stefanie Jegelka; Yisen Wang 001 (NeurIPS, 2024)

In-Context Symmetries: Self-Supervised Learning through Contextual World Models. - Sharut Gupta; Chenyu Wang 003; Yifei Wang 001; Tommi S. Jaakkola; Stefanie Jegelka (NeurIPS, 2024)

The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof. - Derek Lim; Theo Putterman; Robin Walters 001; Haggai Maron; Stefanie Jegelka (NeurIPS, 2024)

A Canonicalization Perspective on Invariant and Equivariant Learning. - George Ma; Yifei Wang 001; Derek Lim; Stefanie Jegelka; Yisen Wang 001 (NeurIPS, 2024)

On the Role of Attention Masks and LayerNorm in Transformers. - Xinyi Wu 003; Amir Ajorlou; Yifei Wang 001; Stefanie Jegelka; Ali Jadbabaie (NeurIPS, 2024)

Are Graph Neural Networks Optimal Approximation Algorithms? - Morris Yau; Nikolaos Karalias; Eric Lu; Jessica Xu; Stefanie Jegelka (NeurIPS, 2024)

Future Directions in Foundations of Graph Machine Learning. - Christopher Morris 001; Nadav Dym; Haggai Maron; Ismail Ilkan Ceylan; Fabrizio Frasca; Ron Levie; Derek Lim; Michael M. Bronstein; Martin Grohe; Stefanie Jegelka (CoRR, 2024)

How to Craft Backdoors with Unlabeled Data Alone? - Yifei Wang 001; Wenhan Ma; Stefanie Jegelka; Yisen Wang 001 (CoRR, 2024)

A Canonization Perspective on Invariant and Equivariant Learning. - George Ma; Yifei Wang 001; Derek Lim; Stefanie Jegelka; Yisen Wang 001 (CoRR, 2024)

The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof. - Derek Lim; Moe Putterman; Robin Walters 001; Haggai Maron; Stefanie Jegelka (CoRR, 2024)

The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges. - 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 005; et al. (CoRR, 2024)

Learning on LoRAs: GL-Equivariant Processing of Low-Rank Weight Spaces for Large Finetuned Models. - Moe Putterman; Derek Lim; Yoav Gelberg; Stefanie Jegelka; Haggai Maron (CoRR, 2024)

Learning Linear Attention in Polynomial Time. - Morris Yau; Ekin Akyürek; Jiayuan Mao; Joshua B. Tenenbaum; Stefanie Jegelka; Jacob Andreas (CoRR, 2024)

On the Role of Depth and Looping for In-Context Learning with Task Diversity. - Khashayar Gatmiry; Nikunj Saunshi; Sashank J. Reddi; Stefanie Jegelka; Sanjiv Kumar (CoRR, 2024)

A Robust Kernel Statistical Test of Invariance: Detecting Subtle Asymmetries. - Ashkan Soleymani; Behrooz Tahmasebi; Stefanie Jegelka; Patrick Jaillet (AISTATS, 2025)

Regularity in Canonicalized Models: A Theoretical Perspective. - Behrooz Tahmasebi; Stefanie Jegelka (AISTATS, 2025)

Computing Optimal Regularizers for Online Linear Optimization. - Khashayar Gatmiry; Jon Schneider; Stefanie Jegelka (COLT, 2025)

What is Wrong with Perplexity for Long-context Language Modeling? - Lizhe Fang; Yifei Wang 001; Zhaoyang Liu 003; Chenheng Zhang; Stefanie Jegelka; Jinyang Gao; Bolin Ding; Yisen Wang 001 (ICLR, 2025)

Higher-Order Graphon Neural Networks: Approximation and Cut Distance. - Daniel Herbst; Stefanie Jegelka (ICLR, 2025)

Learning Efficient Positional Encodings with Graph Neural Networks. - Charilaos I. Kanatsoulis; Evelyn Choi; Stefanie Jegelka; Jure Leskovec; Alejandro Ribeiro (ICLR, 2025)

Generalization, Expressivity, and Universality of Graph Neural Networks on Attributed Graphs. - Levi Rauchwerger; Stefanie Jegelka; Ron Levie (ICLR, 2025)

Generalization Bounds for Canonicalization: A Comparative Study with Group Averaging. - Behrooz Tahmasebi; Stefanie Jegelka (ICLR, 2025)

An Information Criterion for Controlled Disentanglement of Multimodal Data. - Chenyu Wang 003; Sharut Gupta; Xinyi Zhang; Sana Tonekaboni; Stefanie Jegelka; Tommi S. Jaakkola; Caroline Uhler (ICLR, 2025)

Beyond Interpretability: The Gains of Feature Monosemanticity on Model Robustness. - Qi Zhang 067; Yifei Wang 001; Jingyi Cui; Xiang Pan 001; Qi Lei; Stefanie Jegelka; Yisen Wang 001 (ICLR, 2025)

On the Emergence of Position Bias in Transformers. - Xinyi Wu 003; Yifei Wang 001; Stefanie Jegelka; Ali Jadbabaie (CoRR, 2025)

When More is Less: Understanding Chain-of-Thought Length in LLMs. - Yuyang Wu; Yifei Wang 001; Tianqi Du; Stefanie Jegelka; Yisen Wang 001 (CoRR, 2025)

Subsampling Graphs with GNN Performance Guarantees. - Mika Sarkin Jain; Stefanie Jegelka; Ishani Karmarkar; Luana Ruiz; Ellen Vitercik (CoRR, 2025)

Learning with Exact Invariances in Polynomial Time. - Ashkan Soleymani; Behrooz Tahmasebi; Stefanie Jegelka; Patrick Jaillet (CoRR, 2025)

Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation. - Tiansheng Wen; Yifei Wang; Zequn Zeng; Zhong Peng; Yudi Su; Xinyang Liu; Bo Chen 001; Hongwei Liu; Stefanie Jegelka; Chenyu You (CoRR, 2025)

Survey on Generalization Theory for Graph Neural Networks. - Antonis Vasileiou; Stefanie Jegelka; Ron Levie; Christopher Morris 001 (CoRR, 2025)

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning. - Xiaojun Guo; Ang Li; Yifei Wang 001; Stefanie Jegelka; Yisen Wang 001 (CoRR, 2025)

Learning equivariant models by discovering symmetries with learnable augmentations. - Eduardo Santos Escriche; Stefanie Jegelka (CoRR, 2025)

A projection-based framework for gradient-free and parallel learning. - Andreas Bergmeister; Manish Krishan Lal; Stefanie Jegelka; Suvrit Sra (CoRR, 2025)

Sequential-Parallel Duality in Prefix Scannable Models. - Morris Yau; Sharut Gupta; Valerie Engelmayer; Kazuki Irie; Stefanie Jegelka; Jacob Andreas (CoRR, 2025)

Learning Diffusion Models with Flexible Representation Guidance. - Chenyu Wang 003; Cai Zhou; Sharut Gupta; Zongyu Lin; Stefanie Jegelka; Stephen Bates; Tommi S. Jaakkola (CoRR, 2025)