Jegelka, Stefanie

142 publications

NeurIPS 2025 $\texttt{G1}$: Teaching LLMs to Reason on Graphs with Reinforcement Learning Xiaojun Guo, Ang Li, Yifei Wang, Stefanie Jegelka, Yisen Wang

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

ICLR 2025 An Information Criterion for Controlled Disentanglement of Multimodal Data Chenyu Wang, Sharut Gupta, Xinyi Zhang, Sana Tonekaboni, Stefanie Jegelka, Tommi Jaakkola, Caroline Uhler

ICLR 2025 Beyond Interpretability: The Gains of Feature Monosemanticity on Model Robustness Qi Zhang, Yifei Wang, Jingyi Cui, Xiang Pan, Qi Lei, Stefanie Jegelka, Yisen Wang

ICML 2025 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

COLT 2025 Computing Optimal Regularizers for Online Linear Optimization Khashayar Gatmiry, Jon Schneider, Stefanie Jegelka

ICLR 2025 Generalization Bounds for Canonicalization: A Comparative Study with Group Averaging Behrooz Tahmasebi, Stefanie Jegelka

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

NeurIPS 2025 Geometric Algorithms for Neural Combinatorial Optimization with Constraints Nikolaos Karalias, Akbar Rafiey, Yifei Xu, Zhishang Luo, Behrooz Tahmasebi, Connie Jiang, Stefanie Jegelka

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

NeurIPS 2025 Learning Diffusion Models with Flexible Representation Guidance Chenyu Wang, Cai Zhou, Sharut Gupta, Zongyu Lin, Stefanie Jegelka, Stephen Bates, Tommi Jaakkola

ICLR 2025 Learning Efficient Positional Encodings with Graph Neural Networks Charilaos Kanatsoulis, Evelyn Choi, Stefanie Jegelka, Jure Leskovec, Alejandro Ribeiro

NeurIPS 2025 Learning Linear Attention in Polynomial Time Morris Yau, Ekin Akyürek, Jiayuan Mao, Joshua B. Tenenbaum, Stefanie Jegelka, Jacob Andreas

ICLRW 2025 Learning on LoRAs: GL-Equivariant Processing of Low-Rank Weight Spaces for Large Finetuned Models Theo Putterman, Derek Lim, Yoav Gelberg, Stefanie Jegelka, Haggai Maron

ICML 2025 Learning with Exact Invariances in Polynomial Time Ashkan Soleymani, Behrooz Tahmasebi, Stefanie Jegelka, Patrick Jaillet

ICML 2025 On the Emergence of Position Bias in Transformers Xinyi Wu, Yifei Wang, Stefanie Jegelka, Ali Jadbabaie

ICLRW 2025 On the Emergence of Position Bias in Transformers Xinyi Wu, Yifei Wang, Stefanie Jegelka, Ali Jadbabaie

AISTATS 2025 Regularity in Canonicalized Models: A Theoretical Perspective Behrooz Tahmasebi, Stefanie Jegelka

ICLR 2025 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

ICLRW 2025 When More Is Less: Understanding Chain-of-Thought Length in LLMs Yuyang Wu, Yifei Wang, Tianqi Du, Stefanie Jegelka, Yisen Wang

NeurIPS 2024 A Canonicalization Perspective on Invariant and Equivariant Learning George Ma, Yifei Wang, Derek Lim, Stefanie Jegelka, Yisen Wang

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

NeurIPS 2024 A Theoretical Understanding of Self-Correction Through In-Context Alignment Yifei Wang, Yuyang Wu, Zeming Wei, Stefanie Jegelka, Yisen Wang

ICMLW 2024 A Theoretical Understanding of Self-Correction Through In-Context Alignment Yifei Wang, Yuyang Wu, Zeming Wei, Stefanie Jegelka, Yisen Wang

ICMLW 2024 A Theoretical Understanding of Self-Correction Through In-Context Alignment Yifei Wang, Yuyang Wu, Zeming Wei, Stefanie Jegelka, Yisen Wang

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

ICMLW 2024 A Universal Class of Sharpness-Aware Minimization Algorithms Behrooz Tahmasebi, Ashkan Soleymani, Dara Bahri, Stefanie Jegelka, Patrick Jaillet

NeurIPSW 2024 An Information Criterion for Controlled Disentanglement of Multimodal Data Chenyu Wang, Sharut Gupta, Xinyi Zhang, Sana Tonekaboni, Stefanie Jegelka, Tommi Jaakkola, Caroline Uhler

NeurIPS 2024 Are Graph Neural Networks Optimal Approximation Algorithms? Morris Yau, Nikolaos Karalias, Eric Lu, Jessica Xu, 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

ICLR 2024 Context Is Environment Sharut Gupta, Stefanie Jegelka, David Lopez-Paz, Kartik Ahuja

ICLRW 2024 How to Craft Backdoors with Unlabeled Data Alone? Yifei Wang, Wenhan Ma, Stefanie Jegelka, Yisen Wang

NeurIPS 2024 In-Context Symmetries: Self-Supervised Learning Through Contextual World Models Sharut Gupta, Chenyu Wang, Yifei Wang, Tommi Jaakkola, Stefanie Jegelka

ICMLW 2024 In-Context Symmetries: Self-Supervised Learning Through Contextual World Models Sharut Gupta, Chenyu Wang, Yifei Wang, Tommi Jaakkola, Stefanie Jegelka

NeurIPSW 2024 In-Context Symmetries: Self-Supervised Learning Through Contextual World Models Sharut Gupta, Chenyu Wang, Yifei Wang, Tommi Jaakkola, Stefanie Jegelka

NeurIPSW 2024 In-Context Symmetries: Self-Supervised Learning Through Contextual World Models Sharut Gupta, Chenyu Wang, Yifei Wang, Tommi Jaakkola, Stefanie Jegelka

NeurIPSW 2024 Invariant Graphon Networks: Approximation and Cut Distance Daniel Herbst, Stefanie Jegelka

ICMLW 2024 Model-Agnostic Graph Dataset Compression with the Tree Mover’s Distance Mika Sarkin Jain, Stefanie Jegelka, Ishani Karmarkar, Luana Ruiz, Ellen Vitercik

ICLR 2024 On the Hardness of Learning Under Symmetries Bobak Kiani, Thien Le, Hannah Lawrence, Stefanie Jegelka, Melanie Weber

NeurIPS 2024 On the Role of Attention Masks and LayerNorm in Transformers Xinyi Wu, Amir Ajorlou, Yifei Wang, Stefanie Jegelka, Ali Jadbabaie

ICLR 2024 On the Stability of Expressive Positional Encodings for Graphs Yinan Huang, William Lu, Joshua Robinson, Yu Yang, Muhan Zhang, Stefanie Jegelka, Pan Li

ICML 2024 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

ICML 2024 Sample Complexity Bounds for Estimating Probability Divergences Under Invariances Behrooz Tahmasebi, Stefanie Jegelka

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

ICLR 2024 Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning Sharut Gupta, Joshua Robinson, Derek Lim, Soledad Villar, Stefanie Jegelka

NeurIPS 2024 The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof Derek Lim, Theo Putterman, Robin Walters, Haggai Maron, Stefanie Jegelka

ICMLW 2024 The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof Derek Lim, Theo Putterman, Robin Walters, Haggai Maron, Stefanie Jegelka

NeurIPS 2024 Understanding the Role of Equivariance in Self-Supervised Learning Yifei Wang, Kaiwen Hu, Sharut Gupta, Ziyu Ye, Yisen Wang, Stefanie Jegelka

ICMLW 2024 Understanding the Role of Equivariance in Self-Supervised Learning Yifei Wang, Kaiwen Hu, Sharut Gupta, Ziyu Ye, Yisen Wang, Stefanie Jegelka

NeurIPSW 2023 Are Graph Neural Networks Optimal Approximation Algorithms? Morris Yau, Eric Lu, Nikolaos Karalias, Jessica Xu, Stefanie Jegelka

NeurIPSW 2023 Context Is Environment Sharut Gupta, David Lopez-Paz, Stefanie Jegelka, Kartik Ahuja

NeurIPSW 2023 Context Is Environment Sharut Gupta, David Lopez-Paz, Stefanie Jegelka, Kartik Ahuja

ICML 2023 Efficiently Predicting High Resolution Mass Spectra with Graph Neural Networks Michael Murphy, Stefanie Jegelka, Ernest Fraenkel, Tobias Kind, David Healey, Thomas Butler

NeurIPS 2023 Expressive Sign Equivariant Networks for Spectral Geometric Learning Derek Lim, Joshua W. Robinson, Stefanie Jegelka, Haggai Maron

ICLRW 2023 Expressive Sign Equivariant Networks for Spectral Geometric Learning Derek Lim, Joshua Robinson, Stefanie Jegelka, Yaron Lipman, Haggai Maron

ICMLW 2023 Expressive Sign Equivariant Networks for Spectral Geometric Learning Derek Lim, Joshua Robinson, Stefanie Jegelka, Haggai Maron

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

ICMLW 2023 Learning Structured Representations with Equivariant Contrastive Learning Sharut Gupta, Joshua Robinson, Derek Lim, Soledad Villar, Stefanie Jegelka

NeurIPS 2023 Limits, Approximation and Size Transferability for GNNs on Sparse Graphs via Graphops Thien Le, Stefanie Jegelka

NeurIPSW 2023 On Scale-Invariant Sharpness Measures Behrooz Tahmasebi, Ashkan Soleymani, Stefanie Jegelka, Patrick Jaillet

ICMLW 2023 Sample Complexity Bounds for Estimating the Wasserstein Distance Under Invariances Behrooz Tahmasebi, Stefanie Jegelka

ICLR 2023 Sign and Basis Invariant Networks for Spectral Graph Representation Learning Derek Lim, Joshua David Robinson, Lingxiao Zhao, Tess Smidt, Suvrit Sra, Haggai Maron, Stefanie Jegelka

NeurIPS 2023 The Exact Sample Complexity Gain from Invariances for Kernel Regression Behrooz Tahmasebi, Stefanie Jegelka

ICMLW 2023 The Exact Sample Complexity Gain from Invariances for Kernel Regression Behrooz Tahmasebi, Stefanie Jegelka

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

NeurIPS 2023 What Is the Inductive Bias of Flatness Regularization? a Study of Deep Matrix Factorization Models Khashayar Gatmiry, Zhiyuan Li, Tengyu Ma, Sashank Reddi, Stefanie Jegelka, Ching-Yao Chuang

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

NeurIPS 2022 On the Generalization of Learning Algorithms That Do Not Converge Nisha Chandramoorthy, Andreas Loukas, Khashayar Gatmiry, Stefanie Jegelka

ICLR 2022 Optimization and Adaptive Generalization of Three Layer Neural Networks Khashayar Gatmiry, Stefanie Jegelka, Jonathan Kelner

CVPR 2022 Robust Contrastive Learning Against Noisy Views Ching-Yao Chuang, R Devon Hjelm, Xin Wang, Vibhav Vineet, Neel Joshi, Antonio Torralba, Stefanie Jegelka, Yale Song

ICLRW 2022 Sign and Basis Invariant Networks for Spectral Graph Representation Learning Derek Lim, Joshua David Robinson, Lingxiao Zhao, Tess Smidt, Suvrit Sra, Haggai Maron, Stefanie Jegelka

ICLR 2022 Training Invariances and the Low-Rank Phenomenon: Beyond Linear Networks Thien Le, Stefanie Jegelka

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

NeurIPS 2021 Can Contrastive Learning Avoid Shortcut Solutions? Joshua W. Robinson, Li Sun, Ke Yu, Kayhan Batmanghelich, Stefanie Jegelka, Suvrit Sra

ICLR 2021 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

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

NeurIPS 2021 Measuring Generalization with Optimal Transport Ching-Yao Chuang, Youssef Mroueh, Kristjan Greenewald, Antonio Torralba, Stefanie Jegelka

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

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

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

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

AISTATS 2020 Distributionally Robust Bayesian Optimization Johannes Kirschner, Ilija Bogunovic, Stefanie Jegelka, Andreas Krause

ICML 2020 Estimating Generalization Under Distribution Shifts via Domain-Invariant Representations Ching-Yao Chuang, Antonio Torralba, Stefanie Jegelka

ICML 2020 Generalization and Representational Limits of Graph Neural Networks Vikas Garg, Stefanie Jegelka, Tommi Jaakkola

NeurIPS 2020 IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method Yossi Arjevani, Joan Bruna, Bugra Can, Mert Gurbuzbalaban, Stefanie Jegelka, Hongzhou Lin

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, Stefanie Jegelka, Suvrit Sra

NeurIPS 2020 Testing Determinantal Point Processes Khashayar Gatmiry, Maryam Aliakbarpour, Stefanie Jegelka

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

NeurIPS 2019 Distributionally Robust Optimization and Generalization in Kernel Methods Matthew Staib, Stefanie Jegelka

AISTATS 2019 Distributionally Robust Submodular Maximization Matthew Staib, Bryan Wilder, Stefanie Jegelka

NeurIPS 2019 Flexible Modeling of Diversity with Strongly Log-Concave Distributions Joshua Robinson, Suvrit Sra, Stefanie Jegelka

ICLR 2019 How Powerful Are Graph Neural Networks? Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka

ICML 2019 Learning Generative Models Across Incomparable Spaces Charlotte Bunne, David Alvarez-Melis, Andreas Krause, Stefanie Jegelka

ICMLW 2019 The Role of Embedding Complexity in Domain-Invariant Representations Ching-Yao Chuang, Antonio Torralba, Stefanie Jegelka

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

NeurIPS 2018 Adversarially Robust Optimization with Gaussian Processes Ilija Bogunovic, Jonathan Scarlett, Stefanie Jegelka, Volkan Cevher

AISTATS 2018 Batched Large-Scale Bayesian Optimization in High-Dimensional Spaces Zi Wang, Clement Gehring, Pushmeet Kohli, Stefanie Jegelka

UAI 2018 Discrete Sampling Using Semigradient-Based Product Mixtures Alkis Gotovos, S. Hamed Hassani, Andreas Krause, Stefanie Jegelka

NeurIPS 2018 Exponentiated Strongly Rayleigh Distributions Zelda E. Mariet, Suvrit Sra, Stefanie Jegelka

NeurIPS 2018 Provable Variational Inference for Constrained Log-Submodular Models Josip Djolonga, Stefanie Jegelka, Andreas Krause

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

NeurIPS 2018 ResNet with One-Neuron Hidden Layers Is a Universal Approximator Hongzhou Lin, Stefanie Jegelka

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

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

ICML 2017 Batched High-Dimensional Bayesian Optimization via Structural Kernel Learning Zi Wang, Chengtao Li, Stefanie Jegelka, Pushmeet Kohli

CVPR 2017 Deep Metric Learning via Facility Location Hyun Oh Song, Stefanie Jegelka, Vivek Rathod, Kevin Murphy

ICML 2017 Max-Value Entropy Search for Efficient Bayesian Optimization Zi Wang, Stefanie Jegelka

NeurIPS 2017 Parallel Streaming Wasserstein Barycenters Matthew Staib, Sebastian Claici, Justin M Solomon, Stefanie Jegelka

NeurIPS 2017 Polynomial Time Algorithms for Dual Volume Sampling Chengtao Li, Stefanie Jegelka, Suvrit Sra

ICML 2017 Robust Budget Allocation via Continuous Submodular Functions Matthew Staib, Stefanie Jegelka

ICLR 2016 Auxiliary Image Regularization for Deep CNNs with Noisy Labels Samaneh Azadi, Jiashi Feng, Stefanie Jegelka, Trevor Darrell

NeurIPS 2016 Cooperative Graphical Models Josip Djolonga, Stefanie Jegelka, Sebastian Tschiatschek, Andreas Krause

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

AISTATS 2016 Efficient Sampling for K-Determinantal Point Processes Chengtao Li, Stefanie Jegelka, Suvrit Sra

ICML 2016 Fast DPP Sampling for Nystrom with Application to Kernel Methods Chengtao Li, Stefanie Jegelka, Suvrit Sra

NeurIPS 2016 Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling Chengtao Li, Suvrit Sra, Stefanie Jegelka

ICML 2016 Gaussian Quadrature for Matrix Inverse Forms with Applications Chengtao Li, Suvrit Sra, Stefanie Jegelka

AISTATS 2016 Optimization as Estimation with Gaussian Processes in Bandit Settings Zi Wang, Bolei Zhou, Stefanie Jegelka

CVPR 2014 Learning Scalable Discriminative Dictionary with Sample Relatedness Jiashi Feng, Stefanie Jegelka, Shuicheng Yan, Trevor Darrell

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

ICML 2014 On Learning to Localize Objects with Minimal Supervision Hyun Oh Song, Ross Girshick, Stefanie Jegelka, Julien Mairal, Zaid Harchaoui, Trevor Darrell

NeurIPS 2014 On the Convergence Rate of Decomposable Submodular Function Minimization Robert Nishihara, Stefanie Jegelka, Michael I Jordan

NeurIPS 2014 Parallel Double Greedy Submodular Maximization Xinghao Pan, Stefanie Jegelka, Joseph E Gonzalez, Joseph K. Bradley, Michael I Jordan

NeurIPS 2014 Submodular Meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets Adarsh Prasad, Stefanie Jegelka, Dhruv Batra

NeurIPS 2014 Weakly-Supervised Discovery of Visual Pattern Configurations Hyun Oh Song, Yong Jae Lee, Stefanie Jegelka, Trevor Darrell

CVPR 2013 A Principled Deep Random Field Model for Image Segmentation Pushmeet Kohli, Anton Osokin, Stefanie Jegelka

NeurIPS 2013 Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions Rishabh K Iyer, Stefanie Jegelka, Jeff A. Bilmes

ICML 2013 Fast Semidifferential-Based Submodular Function Optimization Rishabh Iyer, Stefanie Jegelka, Jeff Bilmes

NeurIPS 2013 Optimistic Concurrency Control for Distributed Unsupervised Learning Xinghao Pan, Joseph E Gonzalez, Stefanie Jegelka, Tamara Broderick, Michael I Jordan

NeurIPS 2013 Reflection Methods for User-Friendly Submodular Optimization Stefanie Jegelka, Francis Bach, Suvrit Sra

ICML 2011 Approximation Bounds for Inference Using Cooperative Cuts Stefanie Jegelka, Jeff A. Bilmes

NeurIPS 2011 On Fast Approximate Submodular Minimization Stefanie Jegelka, Hui Lin, Jeff A. Bilmes

ICML 2011 Online Submodular Minimization for Combinatorial Structures Stefanie Jegelka, Jeff A. Bilmes

CVPR 2011 Submodularity Beyond Submodular Energies: Coupling Edges in Graph Cuts Stefanie Jegelka, Jeff A. Bilmes

ALT 2009 Approximation Algorithms for Tensor Clustering Stefanie Jegelka, Suvrit Sra, Arindam Banerjee

ICML 2009 Solution Stability in Linear Programming Relaxations: Graph Partitioning and Unsupervised Learning Sebastian Nowozin, Stefanie Jegelka

NeurIPS 2007 Consistent Minimization of Clustering Objective Functions Ulrike V. Luxburg, Stefanie Jegelka, Michael Kaufmann, Sébastien Bubeck

AISTATS 2007 Fast Kernel ICA Using an Approximate Newton Method Hao Shen, Stefanie Jegelka, Arthur Gretton