Stefanie Jegelka

Affiliations:
  • Massachusetts Institute of Technology (MIT), CSAIL, Cambridge, MA, USA
  • University of California, Berkeley, Department of EECS, Berkeley, CA, USA
  • ETH Zurich, Department of Computer Science, Switzerland (PhD 2012)
  • Max Planck Institute for Intelligent Systems, Tübingen, Germany
  • University of Tübingen, Wilhelm Schickard Institute for Computer Sciences, Germany


According to our database1, Stefanie Jegelka authored at least 125 papers between 2006 and 2024.

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Bibliography

2024
Future Directions in Foundations of Graph Machine Learning.
CoRR, 2024

On the hardness of learning under symmetries.
CoRR, 2024

2023
A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs.
CoRR, 2023

Sample Complexity Bounds for Estimating Probability Divergences under Invariances.
CoRR, 2023

On the Stability of Expressive Positional Encodings for Graph Neural Networks.
CoRR, 2023

Are Graph Neural Networks Optimal Approximation Algorithms?
CoRR, 2023

Context is Environment.
CoRR, 2023

Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning.
CoRR, 2023

The Inductive Bias of Flatness Regularization for Deep Matrix Factorization.
CoRR, 2023

The Exact Sample Complexity Gain from Invariances for Kernel Regression on Manifolds.
CoRR, 2023

Tetris-inspired detector with neural network for radiation mapping.
CoRR, 2023

Debiasing Vision-Language Models via Biased Prompts.
CoRR, 2023

The Exact Sample Complexity Gain from Invariances for Kernel Regression.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Expressive Sign Equivariant Networks for Spectral Geometric Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Limits, approximation and size transferability for GNNs on sparse graphs via graphops.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

What is the Inductive Bias of Flatness Regularization? A Study of Deep Matrix Factorization Models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Efficiently predicting high resolution mass spectra with graph neural networks.
Proceedings of the International Conference on Machine Learning, 2023

InfoOT: Information Maximizing Optimal Transport.
Proceedings of the International Conference on Machine Learning, 2023

Sign and Basis Invariant Networks for Spectral Graph Representation Learning.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

The Power of Recursion in Graph Neural Networks for Counting Substructures.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Graph Embeddings: Theory meets Practice (Dagstuhl Seminar 22132).
Dagstuhl Reports, 2022

Optimal algorithms for group distributionally robust optimization and beyond.
CoRR, 2022

Theory of Graph Neural Networks: Representation and Learning.
CoRR, 2022

Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On the generalization of learning algorithms that do not converge.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Training invariances and the low-rank phenomenon: beyond linear networks.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Optimization and Adaptive Generalization of Three layer Neural Networks.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Robust Contrastive Learning against Noisy Views.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021
Can contrastive learning avoid shortcut solutions?
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

What training reveals about neural network complexity.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Scaling up Continuous-Time Markov Chains Helps Resolve Underspecification.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Measuring Generalization with Optimal Transport.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth.
Proceedings of the 38th International Conference on Machine Learning, 2021

Information Obfuscation of Graph Neural Networks.
Proceedings of the 38th International Conference on Machine Learning, 2021

How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks.
Proceedings of the 9th International Conference on Learning Representations, 2021

Contrastive Learning with Hard Negative Samples.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks.
J. Chem. Inf. Model., 2020

Counting Substructures with Higher-Order Graph Neural Networks: Possibility and Impossibility Results.
CoRR, 2020

Graph Adversarial Networks: Protecting Information against Adversarial Attacks.
CoRR, 2020

On the Complexity of Minimizing Convex Finite Sums Without Using the Indices of the Individual Functions.
CoRR, 2020

Testing Determinantal Point Processes.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Adaptive Sampling for Stochastic Risk-Averse Learning.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Debiased Contrastive Learning.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions.
Proceedings of the 37th International Conference on Machine Learning, 2020

Strength from Weakness: Fast Learning Using Weak Supervision.
Proceedings of the 37th International Conference on Machine Learning, 2020

Optimal approximation for unconstrained non-submodular minimization.
Proceedings of the 37th International Conference on Machine Learning, 2020

Generalization and Representational Limits of Graph Neural Networks.
Proceedings of the 37th International Conference on Machine Learning, 2020

Estimating Generalization under Distribution Shifts via Domain-Invariant Representations.
Proceedings of the 37th International Conference on Machine Learning, 2020

What Can Neural Networks Reason About?
Proceedings of the 8th International Conference on Learning Representations, 2020

Distributionally Robust Bayesian Optimization.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Sensor Array Design Through Submodular Optimization.
IEEE Trans. Inf. Theory, 2019

The Role of Embedding Complexity in Domain-invariant Representations.
CoRR, 2019

Minimizing approximately submodular functions.
CoRR, 2019

Distributionally Robust Optimization and Generalization in Kernel Methods.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Flexible Modeling of Diversity with Strongly Log-Concave Distributions.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Learning Generative Models across Incomparable Spaces.
Proceedings of the 36th International Conference on Machine Learning, 2019

How Powerful are Graph Neural Networks?
Proceedings of the 7th International Conference on Learning Representations, 2019

Distributionally Robust Submodular Maximization.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Towards Optimal Transport with Global Invariances.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Are Girls Neko or Shōjo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization.
Proceedings of the 57th Conference of the Association for Computational Linguistics, 2019

2018
Robust GANs against Dishonest Adversaries.
CoRR, 2018

Discrete Sampling using Semigradient-based Product Mixtures.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Exponentiated Strongly Rayleigh Distributions.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

ResNet with one-neuron hidden layers is a Universal Approximator.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Provable Variational Inference for Constrained Log-Submodular Models.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Adversarially Robust Optimization with Gaussian Processes.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Representation Learning on Graphs with Jumping Knowledge Networks.
Proceedings of the 35th International Conference on Machine Learning, 2018

Distributional Adversarial Networks.
Proceedings of the 6th International Conference on Learning Representations, 2018

Batched Large-scale Bayesian Optimization in High-dimensional Spaces.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Structured Optimal Transport.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Streaming Non-Monotone Submodular Maximization: Personalized Video Summarization on the Fly.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Graph cuts with interacting edge weights: examples, approximations, and algorithms.
Math. Program., 2017

Graph-Sparse Logistic Regression.
CoRR, 2017

Parallel Streaming Wasserstein Barycenters.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Polynomial time algorithms for dual volume sampling.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Focused model-learning and planning for non-Gaussian continuous state-action systems.
Proceedings of the 2017 IEEE International Conference on Robotics and Automation, 2017

Batched High-dimensional Bayesian Optimization via Structural Kernel Learning.
Proceedings of the 34th International Conference on Machine Learning, 2017

Max-value Entropy Search for Efficient Bayesian Optimization.
Proceedings of the 34th International Conference on Machine Learning, 2017

Robust Budget Allocation via Continuous Submodular Functions.
Proceedings of the 34th International Conference on Machine Learning, 2017

Multiple wavelength sensing array design.
Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

Deep Metric Learning via Facility Location.
Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

2016
Learnable Structured Clustering Framework for Deep Metric Learning.
CoRR, 2016

Fast Sampling for Strongly Rayleigh Measures with Application to Determinantal Point Processes.
CoRR, 2016

Auxiliary Image Regularization for Deep CNNs with Noisy Labels.
Proceedings of the 4th International Conference on Learning Representations, 2016

Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Cooperative Graphical Models.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Gaussian quadrature for matrix inverse forms with applications.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Fast DPP Sampling for Nystrom with Application to Kernel Methods.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Deep Metric Learning via Lifted Structured Feature Embedding.
Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016

Optimization as Estimation with Gaussian Processes in Bandit Settings.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Efficient Sampling for k-Determinantal Point Processes.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
Bounds on bilinear inverse forms via Gaussian quadrature with applications.
CoRR, 2015

Convex Optimization for Parallel Energy Minimization.
CoRR, 2015

Inferring and Learning from Neuronal Correspondences.
CoRR, 2015

2014
An Interactive Approach to Solving Correspondence Problems.
Int. J. Comput. Vis., 2014

One-Bit Object Detection: On learning to localize objects with minimal supervision.
CoRR, 2014

Graph Cuts with Interacting Edge Costs - Examples, Approximations, and Algorithms.
CoRR, 2014

Monotone Closure of Relaxed Constraints in Submodular Optimization: Connections Between Minimization and Maximization.
Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Weakly-supervised Discovery of Visual Pattern Configurations.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Parallel Double Greedy Submodular Maximization.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

On the Convergence Rate of Decomposable Submodular Function Minimization.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

On learning to localize objects with minimal supervision.
Proceedings of the 31th International Conference on Machine Learning, 2014

Learning Scalable Discriminative Dictionary with Sample Relatedness.
Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014

Efficient and accurate clustering for large-scale genetic mapping.
Proceedings of the 2014 IEEE International Conference on Bioinformatics and Biomedicine, 2014

2013
Optimistic Concurrency Control for Distributed Unsupervised Learning.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Reflection methods for user-friendly submodular optimization.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Fast Semidifferential-based Submodular Function Optimization.
Proceedings of the 30th International Conference on Machine Learning, 2013

A Principled Deep Random Field Model for Image Segmentation.
Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013

2012
Combinatorial problems with submodular coupling in machine learning and computer vision.
PhD thesis, 2012

2011
On fast approximate submodular minimization.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Approximation Bounds for Inference using Cooperative Cuts.
Proceedings of the 28th International Conference on Machine Learning, 2011

Online Submodular Minimization for Combinatorial Structures.
Proceedings of the 28th International Conference on Machine Learning, 2011

Submodularity beyond submodular energies: Coupling edges in graph cuts.
Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition, 2011

2009
Fast kernel-based independent component analysis.
IEEE Trans. Signal Process., 2009

Generalized Clustering via Kernel Embeddings.
Proceedings of the KI 2009: Advances in Artificial Intelligence, 2009

Solution stability in linear programming relaxations: graph partitioning and unsupervised learning.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

Approximation Algorithms for Tensor Clustering.
Proceedings of the Algorithmic Learning Theory, 20th International Conference, 2009

2008
Approximation Algorithms for Bregman Co-clustering and Tensor Clustering
CoRR, 2008

2007
Fast Kernel ICA using an Approximate Newton Method.
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

Consistent Minimization of Clustering Objective Functions.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

2006
Prenatal development of ocular dominance and orientation maps in a self-organizing model of V1.
Neurocomputing, 2006


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