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ORCIDStefanie Jegelka
Stefanie Sabrina Jegelka
Person information
- affiliation: Massachusetts Institute of Technology (MIT), CSAIL, Cambridge, MA, USA
- affiliation: University of California, Berkeley, Department of EECS, Berkeley, CA, USA
- affiliation (PhD 2012): ETH Zurich, Department of Computer Science, Switzerland
- affiliation: Max Planck Institute for Intelligent Systems, Tübingen, Germany
- affiliation: University of Tübingen, Wilhelm Schickard Institute for Computer Sciences, Germany
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2020 – today
- 2024
[i89]Bobak T. Kiani, Thien Le, Hannah Lawrence, Stefanie Jegelka, Melanie Weber:
On the hardness of learning under symmetries. CoRR abs/2401.01869 (2024)- [i88]Christopher Morris, Nadav Dym, Haggai Maron, Ismail Ilkan Ceylan, Fabrizio Frasca, Ron Levie, Derek Lim, Michael M. Bronstein, Martin Grohe, Stefanie Jegelka:
Future Directions in Foundations of Graph Machine Learning. CoRR abs/2402.02287 (2024) - 2023
- [c88]Behrooz Tahmasebi, Derek Lim, Stefanie Jegelka:
The Power of Recursion in Graph Neural Networks for Counting Substructures. AISTATS 2023: 11023-11042 - [c87]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 - [c86]Ching-Yao Chuang, Stefanie Jegelka, David Alvarez-Melis:
InfoOT: Information Maximizing Optimal Transport. ICML 2023: 6228-6242 - [c85]Michael Murphy, Stefanie Jegelka, Ernest Fraenkel, Tobias Kind, David Healey, Thomas Butler:
Efficiently predicting high resolution mass spectra with graph neural networks. ICML 2023: 25549-25562 - [c84]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 - [c83]Thien Le, Stefanie Jegelka:
Limits, approximation and size transferability for GNNs on sparse graphs via graphops. NeurIPS 2023 - [c82]Derek Lim, Joshua Robinson, Stefanie Jegelka, Haggai Maron:
Expressive Sign Equivariant Networks for Spectral Geometric Learning. NeurIPS 2023 - [c81]Behrooz Tahmasebi, Stefanie Jegelka:
The Exact Sample Complexity Gain from Invariances for Kernel Regression. NeurIPS 2023 - [i87]Michael Murphy, Stefanie Jegelka, Ernest Fraenkel
, Tobias Kind, David Healey, Thomas Butler:
Efficiently predicting high resolution mass spectra with graph neural networks. CoRR abs/2301.11419 (2023) - [i86]Ching-Yao Chuang, Varun Jampani, Yuanzhen Li, Antonio Torralba, Stefanie Jegelka:
Debiasing Vision-Language Models via Biased Prompts. CoRR abs/2302.00070 (2023) - [i85]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 abs/2302.07099 (2023) - [i84]Behrooz Tahmasebi, Stefanie Jegelka:
The Exact Sample Complexity Gain from Invariances for Kernel Regression on Manifolds. CoRR abs/2303.14269 (2023) - [i83]Thien Le, Stefanie Jegelka:
Limits, approximation and size transferability for GNNs on sparse graphs via graphops. CoRR abs/2306.04495 (2023) - [i82]Khashayar Gatmiry, Zhiyuan Li, Ching-Yao Chuang, Sashank Reddi, Tengyu Ma, Stefanie Jegelka:
The Inductive Bias of Flatness Regularization for Deep Matrix Factorization. CoRR abs/2306.13239 (2023) - [i81]Sharut Gupta, Joshua Robinson, Derek Lim, Soledad Villar, Stefanie Jegelka:
Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning. CoRR abs/2306.13924 (2023) - [i80]Sharut Gupta, Stefanie Jegelka, David Lopez-Paz, Kartik Ahuja:
Context is Environment. CoRR abs/2309.09888 (2023) - [i79]Morris Yau, Eric Lu, Nikolaos Karalias, Jessica Xu, Stefanie Jegelka:
Are Graph Neural Networks Optimal Approximation Algorithms? CoRR abs/2310.00526 (2023) - [i78]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 abs/2310.02579 (2023) - [i77]Behrooz Tahmasebi, Stefanie Jegelka:
Sample Complexity Bounds for Estimating Probability Divergences under Invariances. CoRR abs/2311.02868 (2023) - [i76]Thien Le, Luana Ruiz, Stefanie Jegelka:
A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs. CoRR abs/2311.10610 (2023) - [i75]Derek Lim, Joshua Robinson, Stefanie Jegelka, Haggai Maron:
Expressive Sign Equivariant Networks for Spectral Geometric Learning. CoRR abs/2312.02339 (2023) - 2022
- [c80]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: 16649-16660 - [c79]Khashayar Gatmiry, Stefanie Jegelka, Jonathan A. Kelner:
Optimization and Adaptive Generalization of Three layer Neural Networks. ICLR 2022 - [c78]Thien Le, Stefanie Jegelka:
Training invariances and the low-rank phenomenon: beyond linear networks. ICLR 2022 - [c77]Nisha Chandramoorthy, Andreas Loukas, Khashayar Gatmiry, Stefanie Jegelka:
On the generalization of learning algorithms that do not converge. NeurIPS 2022 - [c76]Ching-Yao Chuang, Stefanie Jegelka:
Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks. NeurIPS 2022 - [c75]Nikolaos Karalias, Joshua Robinson, Andreas Loukas, Stefanie Jegelka:
Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions. NeurIPS 2022 - [e1]Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvári, Gang Niu, Sivan Sabato:
International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA. Proceedings of Machine Learning Research 162, PMLR 2022 [contents] - [i74]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 abs/2201.04309 (2022) - [i73]Thien Le, Stefanie Jegelka:
Training invariances and the low-rank phenomenon: beyond linear networks. CoRR abs/2201.11968 (2022) - [i72]Derek Lim, Joshua Robinson, Lingxiao Zhao, Tess E. Smidt, Suvrit Sra, Haggai Maron, Stefanie Jegelka:
Sign and Basis Invariant Networks for Spectral Graph Representation Learning. CoRR abs/2202.13013 (2022) - [i71]Stefanie Jegelka:
Theory of Graph Neural Networks: Representation and Learning. CoRR abs/2204.07697 (2022) - [i70]Nikolaos Karalias, Joshua Robinson, Andreas Loukas, Stefanie Jegelka:
Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions. CoRR abs/2208.04055 (2022) - [i69]Nisha Chandramoorthy, Andreas Loukas, Khashayar Gatmiry, Stefanie Jegelka:
On the generalization of learning algorithms that do not converge. CoRR abs/2208.07951 (2022) - [i68]Ching-Yao Chuang, Stefanie Jegelka:
Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks. CoRR abs/2210.01906 (2022) - [i67]Ching-Yao Chuang, Stefanie Jegelka, David Alvarez-Melis:
InfoOT: Information Maximizing Optimal Transport. CoRR abs/2210.03164 (2022) - [i66]Tasuku Soma, Khashayar Gatmiry, Stefanie Jegelka:
Optimal algorithms for group distributionally robust optimization and beyond. CoRR abs/2212.13669 (2022) - [i65]Martin Grohe, Stephan Günnemann, Stefanie Jegelka, Christopher Morris:
Graph Embeddings: Theory meets Practice (Dagstuhl Seminar 22132). Dagstuhl Reports 12(3): 141-155 (2022) - 2021
- [c74]Joshua David Robinson, Ching-Yao Chuang, Suvrit Sra, Stefanie Jegelka:
Contrastive Learning with Hard Negative Samples. ICLR 2021 - [c73]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 - [c72]Peiyuan Liao, Han Zhao, Keyulu Xu, Tommi S. Jaakkola, Geoffrey J. Gordon, Stefanie Jegelka, Ruslan Salakhutdinov:
Information Obfuscation of Graph Neural Networks. ICML 2021: 6600-6610 - [c71]Keyulu Xu, Mozhi Zhang, Stefanie Jegelka, Kenji Kawaguchi:
Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth. ICML 2021: 11592-11602 - [c70]Andreas Loukas, Marinos Poiitis, Stefanie Jegelka:
What training reveals about neural network complexity. NeurIPS 2021: 494-508 - [c69]Joshua Robinson, Li Sun, Ke Yu, Kayhan Batmanghelich, Stefanie Jegelka, Suvrit Sra:
Can contrastive learning avoid shortcut solutions? NeurIPS 2021: 4974-4986 - [c68]Ching-Yao Chuang, Youssef Mroueh, Kristjan H. Greenewald, Antonio Torralba, Stefanie Jegelka:
Measuring Generalization with Optimal Transport. NeurIPS 2021: 8294-8306 - [c67]Alkis Gotovos, Rebekka Burkholz, John Quackenbush, Stefanie Jegelka:
Scaling up Continuous-Time Markov Chains Helps Resolve Underspecification. NeurIPS 2021: 14580-14592 - [i64]Keyulu Xu, Mozhi Zhang, Stefanie Jegelka, Kenji Kawaguchi:
Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth. CoRR abs/2105.04550 (2021) - [i63]Ching-Yao Chuang, Youssef Mroueh, Kristjan H. Greenewald, Antonio Torralba, Stefanie Jegelka:
Measuring Generalization with Optimal Transport. CoRR abs/2106.03314 (2021) - [i62]Andreas Loukas, Marinos Poiitis, Stefanie Jegelka:
What training reveals about neural network complexity. CoRR abs/2106.04186 (2021) - [i61]Joshua Robinson, Li Sun, Ke Yu, Kayhan Batmanghelich, Stefanie Jegelka, Suvrit Sra:
Can contrastive learning avoid shortcut solutions? CoRR abs/2106.11230 (2021) - [i60]Alkis Gotovos, Rebekka Burkholz, John Quackenbush, Stefanie Jegelka:
Scaling up Continuous-Time Markov Chains Helps Resolve Underspecification. CoRR abs/2107.02911 (2021) - 2020
- [j6]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): 1194-1201 (2020) - [c66]Johannes Kirschner, Ilija Bogunovic, Stefanie Jegelka, Andreas Krause:
Distributionally Robust Bayesian Optimization. AISTATS 2020: 2174-2184 - [c65]Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S. Du, Ken-ichi Kawarabayashi, Stefanie Jegelka:
What Can Neural Networks Reason About? ICLR 2020 - [c64]Ching-Yao Chuang, Antonio Torralba, Stefanie Jegelka:
Estimating Generalization under Distribution Shifts via Domain-Invariant Representations. ICML 2020: 1984-1994 - [c63]Vikas K. Garg, Stefanie Jegelka, Tommi S. Jaakkola:
Generalization and Representational Limits of Graph Neural Networks. ICML 2020: 3419-3430 - [c62]Marwa El Halabi, Stefanie Jegelka:
Optimal approximation for unconstrained non-submodular minimization. ICML 2020: 3961-3972 - [c61]Joshua Robinson, Stefanie Jegelka, Suvrit Sra:
Strength from Weakness: Fast Learning Using Weak Supervision. ICML 2020: 8127-8136 - [c60]Jingzhao Zhang, Hongzhou Lin, Stefanie Jegelka, Suvrit Sra, Ali Jadbabaie:
Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions. ICML 2020: 11173-11182 - [c59]Yossi Arjevani, Joan Bruna, Bugra Can, Mert Gürbüzbalaban, Stefanie Jegelka, Hongzhou Lin:
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method. NeurIPS 2020 - [c58]Ching-Yao Chuang, Joshua Robinson, Yen-Chen Lin, Antonio Torralba, Stefanie Jegelka:
Debiased Contrastive Learning. NeurIPS 2020 - [c57]Sebastian Curi, Kfir Y. Levy, Stefanie Jegelka, Andreas Krause:
Adaptive Sampling for Stochastic Risk-Averse Learning. NeurIPS 2020 - [c56]Khashayar Gatmiry, Maryam Aliakbarpour, Stefanie Jegelka:
Testing Determinantal Point Processes. NeurIPS 2020 - [i59]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 abs/2002.03273 (2020) - [i58]Vikas K. Garg, Stefanie Jegelka, Tommi S. Jaakkola:
Generalization and Representational Limits of Graph Neural Networks. CoRR abs/2002.06157 (2020) - [i57]Joshua Robinson, Stefanie Jegelka, Suvrit Sra:
Strength from Weakness: Fast Learning Using Weak Supervision. CoRR abs/2002.08483 (2020) - [i56]Johannes Kirschner, Ilija Bogunovic, Stefanie Jegelka, Andreas Krause:
Distributionally Robust Bayesian Optimization. CoRR abs/2002.09038 (2020) - [i55]Yossi Arjevani, Joan Bruna, Bugra Can, Mert Gürbüzbalaban, Stefanie Jegelka, Hongzhou Lin:
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method. CoRR abs/2006.06733 (2020) - [i54]Ching-Yao Chuang, Joshua Robinson, Yen-Chen Lin, Antonio Torralba, Stefanie Jegelka:
Debiased Contrastive Learning. CoRR abs/2007.00224 (2020) - [i53]Ching-Yao Chuang, Antonio Torralba, Stefanie Jegelka:
Estimating Generalization under Distribution Shifts via Domain-Invariant Representations. CoRR abs/2007.03511 (2020) - [i52]Khashayar Gatmiry, Maryam Aliakbarpour, Stefanie Jegelka:
Testing Determinantal Point Processes. CoRR abs/2008.03650 (2020) - [i51]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 abs/2009.11848 (2020) - [i50]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 abs/2009.13504 (2020) - [i49]Joshua Robinson, Ching-Yao Chuang, Suvrit Sra, Stefanie Jegelka:
Contrastive Learning with Hard Negative Samples. CoRR abs/2010.04592 (2020) - [i48]Behrooz Tahmasebi, Stefanie Jegelka:
Counting Substructures with Higher-Order Graph Neural Networks: Possibility and Impossibility Results. CoRR abs/2012.03174 (2020)
2010 – 2019
- 2019
- [j5]Gal Shulkind, Stefanie Jegelka, Gregory W. Wornell:
Sensor Array Design Through Submodular Optimization. IEEE Trans. Inf. Theory 65(1): 664-675 (2019) - [c55]Mozhi Zhang, Keyulu Xu, Ken-ichi Kawarabayashi, Stefanie Jegelka, Jordan L. Boyd-Graber:
Are Girls Neko or Shōjo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization. ACL (1) 2019: 3180-3189 - [c54]Matthew Staib, Bryan Wilder, Stefanie Jegelka:
Distributionally Robust Submodular Maximization. AISTATS 2019: 506-516 - [c53]David Alvarez-Melis, Stefanie Jegelka, Tommi S. Jaakkola:
Towards Optimal Transport with Global Invariances. AISTATS 2019: 1870-1879 - [c52]Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka:
How Powerful are Graph Neural Networks? ICLR 2019 - [c51]Charlotte Bunne, David Alvarez-Melis, Andreas Krause, Stefanie Jegelka:
Learning Generative Models across Incomparable Spaces. ICML 2019: 851-861 - [c50]Matthew Staib, Stefanie Jegelka:
Distributionally Robust Optimization and Generalization in Kernel Methods. NeurIPS 2019: 9131-9141 - [c49]Joshua Robinson, Suvrit Sra, Stefanie Jegelka:
Flexible Modeling of Diversity with Strongly Log-Concave Distributions. NeurIPS 2019: 15199-15209 - [i47]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. CoRR abs/1901.00032 (2019) - [i46]Charlotte Bunne, David Alvarez-Melis, Andreas Krause, Stefanie Jegelka:
Learning Generative Models across Incomparable Spaces. CoRR abs/1905.05461 (2019) - [i45]Matthew Staib, Stefanie Jegelka:
Distributionally Robust Optimization and Generalization in Kernel Methods. CoRR abs/1905.10943 (2019) - [i44]Marwa El Halabi, Stefanie Jegelka:
Minimizing approximately submodular functions. CoRR abs/1905.12145 (2019) - [i43]Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S. Du, Ken-ichi Kawarabayashi, Stefanie Jegelka:
What Can Neural Networks Reason About? CoRR abs/1905.13211 (2019) - [i42]Mozhi Zhang, Keyulu Xu, Ken-ichi Kawarabayashi, Stefanie Jegelka, Jordan L. Boyd-Graber:
Are Girls Neko or Shōjo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization. CoRR abs/1906.01622 (2019) - [i41]Joshua Robinson, Suvrit Sra, Stefanie Jegelka:
Flexible Modeling of Diversity with Strongly Log-Concave Distributions. CoRR abs/1906.05413 (2019) - [i40]Ching-Yao Chuang, Antonio Torralba, Stefanie Jegelka:
The Role of Embedding Complexity in Domain-invariant Representations. CoRR abs/1910.05804 (2019) - [i39]Sebastian Curi, Kfir Y. Levy, Stefanie Jegelka, Andreas Krause:
Adaptive Sampling for Stochastic Risk-Averse Learning. CoRR abs/1910.12511 (2019) - 2018
- [c48]Baharan Mirzasoleiman, Stefanie Jegelka, Andreas Krause:
Streaming Non-Monotone Submodular Maximization: Personalized Video Summarization on the Fly. AAAI 2018: 1379-1386 - [c47]Zi Wang, Clement Gehring, Pushmeet Kohli, Stefanie Jegelka:
Batched Large-scale Bayesian Optimization in High-dimensional Spaces. AISTATS 2018: 745-754 - [c46]David Alvarez-Melis, Tommi S. Jaakkola, Stefanie Jegelka:
Structured Optimal Transport. AISTATS 2018: 1771-1780 - [c45]Chengtao Li, David Alvarez-Melis, Keyulu Xu, Stefanie Jegelka, Suvrit Sra:
Distributional Adversarial Networks. ICLR (Workshop) 2018 - [c44]Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka:
Representation Learning on Graphs with Jumping Knowledge Networks. ICML 2018: 5449-5458 - [c43]Josip Djolonga, Stefanie Jegelka, Andreas Krause:
Provable Variational Inference for Constrained Log-Submodular Models. NeurIPS 2018: 2702-2712 - [c42]Zelda E. Mariet, Suvrit Sra, Stefanie Jegelka:
Exponentiated Strongly Rayleigh Distributions. NeurIPS 2018: 4464-4474 - [c41]Ilija Bogunovic, Jonathan Scarlett, Stefanie Jegelka, Volkan Cevher:
Adversarially Robust Optimization with Gaussian Processes. NeurIPS 2018: 5765-5775 - [c40]Hongzhou Lin, Stefanie Jegelka:
ResNet with one-neuron hidden layers is a Universal Approximator. NeurIPS 2018: 6172-6181 - [c39]Alkis Gotovos, S. Hamed Hassani, Andreas Krause, Stefanie Jegelka:
Discrete Sampling using Semigradient-based Product Mixtures. UAI 2018: 229-237 - [i38]Matthew Staib, Bryan Wilder, Stefanie Jegelka:
Distributionally Robust Submodular Maximization. CoRR abs/1802.05249 (2018) - [i37]Zhi Xu, Chengtao Li, Stefanie Jegelka:
Robust GANs against Dishonest Adversaries. CoRR abs/1802.09700 (2018) - [i36]Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka:
Representation Learning on Graphs with Jumping Knowledge Networks. CoRR abs/1806.03536 (2018) - [i35]David Alvarez-Melis, Stefanie Jegelka, Tommi S. Jaakkola:
Towards Optimal Transport with Global Invariances. CoRR abs/1806.09277 (2018) - [i34]Hongzhou Lin, Stefanie Jegelka:
ResNet with one-neuron hidden layers is a Universal Approximator. CoRR abs/1806.10909 (2018) - [i33]Alkis Gotovos, S. Hamed Hassani, Andreas Krause, Stefanie Jegelka:
Discrete Sampling using Semigradient-based Product Mixtures. CoRR abs/1807.01808 (2018) - [i32]Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka:
How Powerful are Graph Neural Networks? CoRR abs/1810.00826 (2018) - [i31]Ilija Bogunovic, Jonathan Scarlett, Stefanie Jegelka, Volkan Cevher:
Adversarially Robust Optimization with Gaussian Processes. CoRR abs/1810.10775 (2018) - 2017
- [j4]Stefanie Jegelka, Jeff A. Bilmes:
Graph cuts with interacting edge weights: examples, approximations, and algorithms. Math. Program. 162(1-2): 241-282 (2017) - [c38]Hyun Oh Song, Stefanie Jegelka, Vivek Rathod, Kevin Murphy:
Deep Metric Learning via Facility Location. CVPR 2017: 2206-2214 - [c37]Gal Shulkind, Stefanie Jegelka, Gregory W. Wornell:
Multiple wavelength sensing array design. ICASSP 2017: 3424-3428 - [c36]Matthew Staib, Stefanie Jegelka:
Robust Budget Allocation via Continuous Submodular Functions. ICML 2017: 3230-3240 - [c35]Zi Wang, Stefanie Jegelka:
Max-value Entropy Search for Efficient Bayesian Optimization. ICML 2017: 3627-3635 - [c34]Zi Wang, Chengtao Li, Stefanie Jegelka, Pushmeet Kohli:
Batched High-dimensional Bayesian Optimization via Structural Kernel Learning. ICML 2017: 3656-3664 - [c33]Zi Wang, Stefanie Jegelka, Leslie Pack Kaelbling, Tomás Lozano-Pérez:
Focused model-learning and planning for non-Gaussian continuous state-action systems. ICRA 2017: 3754-3761 - [c32]Matthew Staib, Sebastian Claici, Justin Solomon, Stefanie Jegelka:
Parallel Streaming Wasserstein Barycenters. NIPS 2017: 2647-2658 - [c31]Chengtao Li, Stefanie Jegelka, Suvrit Sra:
Polynomial time algorithms for dual volume sampling. NIPS 2017: 5038-5047 - [i30]Matthew Staib, Stefanie Jegelka:
Robust Budget Allocation via Continuous Submodular Functions. CoRR abs/1702.08791 (2017) - [i29]Zi Wang, Stefanie Jegelka:
Max-value Entropy Search for Efficient Bayesian Optimization. CoRR abs/1703.01968 (2017) - [i28]Zi Wang, Chengtao Li, Stefanie Jegelka, Pushmeet Kohli:
Batched High-dimensional Bayesian Optimization via Structural Kernel Learning. CoRR abs/1703.01973 (2017) - [i27]Gal Shulkind, Stefanie Jegelka, Gregory W. Wornell:
Sensor Array Design Through Submodular Optimization. CoRR abs/1705.06616 (2017) - [i26]Matthew Staib, Sebastian Claici, Justin Solomon, Stefanie Jegelka:
Parallel Streaming Wasserstein Barycenters. CoRR abs/1705.07443 (2017) - [i25]Zi Wang, Clement Gehring, Pushmeet Kohli, Stefanie Jegelka:
Batched Large-scale Bayesian Optimization in High-dimensional Spaces. CoRR abs/1706.01445 (2017) - [i24]Baharan Mirzasoleiman, Stefanie Jegelka, Andreas Krause:
Streaming Non-monotone Submodular Maximization: Personalized Video Summarization on the Fly. CoRR abs/1706.03583 (2017) - [i23]Chengtao Li, David Alvarez-Melis, Keyulu Xu, Stefanie Jegelka, Suvrit Sra:
Distributional Adversarial Networks. CoRR abs/1706.09549 (2017) - [i22]Alexander LeNail, Ludwig Schmidt, Johnathan Li, Tobias Ehrenberger, Karen Sachs, Stefanie Jegelka, Ernest Fraenkel:
Graph-Sparse Logistic Regression. CoRR abs/1712.05510 (2017) - [i21]David Alvarez-Melis, Tommi S. Jaakkola, Stefanie Jegelka:
Structured Optimal Transport. CoRR abs/1712.06199 (2017) - 2016
- [c30]Zi Wang, Bolei Zhou, Stefanie Jegelka:
Optimization as Estimation with Gaussian Processes in Bandit Settings. AISTATS 2016: 1022-1031 - [c29]Chengtao Li, Stefanie Jegelka, Suvrit Sra:
Efficient Sampling for k-Determinantal Point Processes. AISTATS 2016: 1328-1337 - [c28]Hyun Oh Song, Yu Xiang, Stefanie Jegelka, Silvio Savarese:
Deep Metric Learning via Lifted Structured Feature Embedding. CVPR 2016: 4004-4012 - [c27]Chengtao Li, Suvrit Sra, Stefanie Jegelka:
Gaussian quadrature for matrix inverse forms with applications. ICML 2016: 1766-1775 - [c26]Chengtao Li, Stefanie Jegelka, Suvrit Sra:
Fast DPP Sampling for Nystrom with Application to Kernel Methods. ICML 2016: 2061-2070 - [c25]Josip Djolonga, Stefanie Jegelka, Sebastian Tschiatschek, Andreas Krause:
Cooperative Graphical Models. NIPS 2016: 262-270 - [c24]Chengtao Li, Suvrit Sra, Stefanie Jegelka:
Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling. NIPS 2016: 4188-4196 - [c23]Samaneh Azadi, Jiashi Feng, Stefanie Jegelka, Trevor Darrell:
Auxiliary Image Regularization for Deep CNNs with Noisy Labels. ICLR (Poster) 2016 - [i20]Chengtao Li, Stefanie Jegelka, Suvrit Sra:
Fast DPP Sampling for Nyström with Application to Kernel Methods. CoRR abs/1603.06052 (2016) - [i19]Chengtao Li, Stefanie Jegelka, Suvrit Sra:
Fast Sampling for Strongly Rayleigh Measures with Application to Determinantal Point Processes. CoRR abs/1607.03559 (2016) - [i18]Zi Wang, Stefanie Jegelka, Leslie Pack Kaelbling, Tomás Lozano-Pérez:
Focused Model-Learning and Planning for Non-Gaussian Continuous State-Action Systems. CoRR abs/1607.07762 (2016) - [i17]Hyun Oh Song, Stefanie Jegelka, Vivek Rathod, Kevin Murphy:
Learnable Structured Clustering Framework for Deep Metric Learning. CoRR abs/1612.01213 (2016) - 2015
- [i16]Ashish Kapoor, Edward Paxon Frady, Stefanie Jegelka, William B. Kristan Jr., Eric Horvitz:
Inferring and Learning from Neuronal Correspondences. CoRR abs/1501.05973 (2015) - [i15]K. S. Sesh Kumar, Álvaro Barbero Jiménez, Stefanie Jegelka, Suvrit Sra, Francis R. Bach:
Convex Optimization for Parallel Energy Minimization. CoRR abs/1503.01563 (2015) - [i14]Chengtao Li, Stefanie Jegelka, Suvrit Sra:
Efficient Sampling for k-Determinantal Point Processes. CoRR abs/1509.01618 (2015) - [i13]Zi Wang, Bolei Zhou, Stefanie Jegelka:
Optimization as Estimation with Gaussian Processes in Bandit Settings. CoRR abs/1510.06423 (2015) - [i12]Hyun Oh Song, Yu Xiang, Stefanie Jegelka, Silvio Savarese:
Deep Metric Learning via Lifted Structured Feature Embedding. CoRR abs/1511.06452 (2015) - [i11]Chengtao Li, Suvrit Sra, Stefanie Jegelka:
Bounds on bilinear inverse forms via Gaussian quadrature with applications. CoRR abs/1512.01904 (2015) - 2014
- [j3]Stefanie Jegelka, Ashish Kapoor, Eric Horvitz:
An Interactive Approach to Solving Correspondence Problems. Int. J. Comput. Vis. 108(1-2): 49-58 (2014) - [c22]Veronika Strnadova, Aydin Buluç, Jarrod Chapman, John R. Gilbert, Joseph Gonzalez, Stefanie Jegelka, Daniel Rokhsar, Leonid Oliker:
Efficient and accurate clustering for large-scale genetic mapping. BIBM 2014: 3-10 - [c21]Jiashi Feng, Stefanie Jegelka, Shuicheng Yan, Trevor Darrell:
Learning Scalable Discriminative Dictionary with Sample Relatedness. CVPR 2014: 1645-1652 - [c20]Hyun Oh Song, Ross B. Girshick, Stefanie Jegelka, Julien Mairal, Zaïd Harchaoui, Trevor Darrell:
On learning to localize objects with minimal supervision. ICML 2014: 1611-1619 - [c19]Xinghao Pan, Stefanie Jegelka, Joseph E. Gonzalez, Joseph K. Bradley, Michael I. Jordan:
Parallel Double Greedy Submodular Maximization. NIPS 2014: 118-126 - [c18]Robert Nishihara, Stefanie Jegelka, Michael I. Jordan:
On the Convergence Rate of Decomposable Submodular Function Minimization. NIPS 2014: 640-648 - [c17]Hyun Oh Song, Yong Jae Lee, Stefanie Jegelka, Trevor Darrell:
Weakly-supervised Discovery of Visual Pattern Configurations. NIPS 2014: 1637-1645 - [c16]Adarsh Prasad, Stefanie Jegelka, Dhruv Batra:
Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets. NIPS 2014: 2645-2653 - [c15]Rishabh K. Iyer, Stefanie Jegelka, Jeff A. Bilmes:
Monotone Closure of Relaxed Constraints in Submodular Optimization: Connections Between Minimization and Maximization. UAI 2014: 360-369 - [i10]Stefanie Jegelka, Jeff A. Bilmes:
Graph Cuts with Interacting Edge Costs - Examples, Approximations, and Algorithms. CoRR abs/1402.0240 (2014) - [i9]Hyun Oh Song, Ross B. Girshick, Stefanie Jegelka, Julien Mairal, Zaïd Harchaoui, Trevor Darrell:
One-Bit Object Detection: On learning to localize objects with minimal supervision. CoRR abs/1403.1024 (2014) - [i8]Robert Nishihara, Stefanie Jegelka, Michael I. Jordan:
On the Convergence Rate of Decomposable Submodular Function Minimization. CoRR abs/1406.6474 (2014) - [i7]Hyun Oh Song, Yong Jae Lee, Stefanie Jegelka, Trevor Darrell:
Weakly-supervised Discovery of Visual Pattern Configurations. CoRR abs/1406.6507 (2014) - [i6]Adarsh Prasad, Stefanie Jegelka, Dhruv Batra:
Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets. CoRR abs/1411.1752 (2014) - 2013
- [c14]Pushmeet Kohli, Anton Osokin, Stefanie Jegelka:
A Principled Deep Random Field Model for Image Segmentation. CVPR 2013: 1971-1978 - [c13]Rishabh K. Iyer, Stefanie Jegelka, Jeff A. Bilmes:
Fast Semidifferential-based Submodular Function Optimization. ICML (3) 2013: 855-863 - [c12]Stefanie Jegelka, Francis R. Bach, Suvrit Sra:
Reflection methods for user-friendly submodular optimization. NIPS 2013: 1313-1321 - [c11]Xinghao Pan, Joseph E. Gonzalez, Stefanie Jegelka, Tamara Broderick, Michael I. Jordan:
Optimistic Concurrency Control for Distributed Unsupervised Learning. NIPS 2013: 1403-1411 - [c10]Rishabh K. Iyer, Stefanie Jegelka, Jeff A. Bilmes:
Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions. NIPS 2013: 2742-2750 - [i5]Xinghao Pan, Joseph E. Gonzalez, Stefanie Jegelka, Tamara Broderick, Michael I. Jordan:
Optimistic Concurrency Control for Distributed Unsupervised Learning. CoRR abs/1307.8049 (2013) - [i4]Rishabh K. Iyer, Stefanie Jegelka, Jeff A. Bilmes:
Fast Semidifferential-based Submodular Function Optimization. CoRR abs/1308.1006 (2013) - [i3]Rishabh K. Iyer, Stefanie Jegelka, Jeff A. Bilmes:
Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions. CoRR abs/1311.2110 (2013) - [i2]Stefanie Jegelka, Francis R. Bach, Suvrit Sra:
Reflection methods for user-friendly submodular optimization. CoRR abs/1311.4296 (2013) - 2012
- [b1]Stefanie Sabrina Jegelka:
Combinatorial problems with submodular coupling in machine learning and computer vision. ETH Zurich, 2012, pp. I-XVI, 1-202 - 2011
- [c9]Stefanie Jegelka, Jeff A. Bilmes:
Submodularity beyond submodular energies: Coupling edges in graph cuts. CVPR 2011: 1897-1904 - [c8]Stefanie Jegelka, Jeff A. Bilmes:
Online Submodular Minimization for Combinatorial Structures. ICML 2011: 345-352 - [c7]Stefanie Jegelka, Jeff A. Bilmes:
Approximation Bounds for Inference using Cooperative Cuts. ICML 2011: 577-584 - [c6]Stefanie Jegelka, Hui Lin, Jeff A. Bilmes:
On fast approximate submodular minimization. NIPS 2011: 460-468
2000 – 2009
- 2009
- [j2]Hao Shen, Stefanie Jegelka, Arthur Gretton:
Fast kernel-based independent component analysis. IEEE Trans. Signal Process. 57(9): 3498-3511 (2009) - [c5]Stefanie Jegelka, Suvrit Sra, Arindam Banerjee:
Approximation Algorithms for Tensor Clustering. ALT 2009: 368-383 - [c4]Sebastian Nowozin, Stefanie Jegelka:
Solution stability in linear programming relaxations: graph partitioning and unsupervised learning. ICML 2009: 769-776 - [c3]Stefanie Jegelka, Arthur Gretton, Bernhard Schölkopf, Bharath K. Sriperumbudur, Ulrike von Luxburg:
Generalized Clustering via Kernel Embeddings. KI 2009: 144-152 - 2008
- [i1]Stefanie Jegelka, Suvrit Sra, Arindam Banerjee:
Approximation Algorithms for Bregman Co-clustering and Tensor Clustering. CoRR abs/0812.0389 (2008) - 2007
- [c2]Ulrike von Luxburg, Sébastien Bubeck, Stefanie Jegelka, Michael Kaufmann:
Consistent Minimization of Clustering Objective Functions. NIPS 2007: 961-968 - [c1]Hao Shen, Stefanie Jegelka, Arthur Gretton:
Fast Kernel ICA using an Approximate Newton Method. AISTATS 2007: 476-483 - 2006
- [j1]Stefanie Jegelka, James A. Bednar, Risto Miikkulainen:
Prenatal development of ocular dominance and orientation maps in a self-organizing model of V1. Neurocomputing 69(10-12): 1291-1296 (2006)
Coauthor Index
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