Stefanie Jegelka
92 publications
9 venues
H Index 32
Affiliation
Massachusetts Institute of Technology (MIT), CSAIL, Cambridge, MA, USAUniversity of California, Berkeley, Department of EECS, Berkeley, CA, USA
ETH Zurich, Department of Computer Science, Switzerland
Max Planck Institute for Intelligent Systems, T bingen, Germany
University of T bingen, Wilhelm Schickard Institute for Computer Sciences, Germany
Links
DBLPGoogle Scholar
- https://people.csail.mit.edu/stefje/
- http://www.is.tuebingen.mpg.de/nc/employee/details/jegelka.html
- https://www.wikidata.org/entity/Q102683371
- https://en.wikipedia.org/wiki/Stefanie_Jegelka
- https://d-nb.info/gnd/1029803285
- https://mathgenealogy.org/id.php?id=218795
- https://dblp.uni-trier.de/pers/hd/j/Jegelka:Stefanie_Sabrina
| Name | Venue | Year | citations |
|---|---|---|---|
Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning?
|
ICML | 2024 | 0 |
| A Universal Class of Sharpness-Aware Minimization Algorithms.
|
ICML | 2024 | 0 |
| Sample Complexity Bounds for Estimating Probability Divergences under Invariances.
|
ICML | 2024 | 0 |
| Simplicity Bias via Global Convergence of Sharpness Minimization.
|
ICML | 2024 | 0 |
| Position: Future Directions in the Theory of Graph Machine Learning.
|
ICML | 2024 | 0 |
| A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs.
|
ICLR | 2024 | 0 |
| Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning.
|
ICLR | 2024 | 0 |
| Context is Environment.
|
ICLR | 2024 | 0 |
| On the hardness of learning under symmetries.
|
ICLR | 2024 | 0 |
| On the Stability of Expressive Positional Encodings for Graphs.
|
ICLR | 2024 | 0 |
| Efficiently predicting high resolution mass spectra with graph neural networks.
|
ICML | 2023 | 0 |
| InfoOT: Information Maximizing Optimal Transport.
|
ICML | 2023 | 0 |
| The Power of Recursion in Graph Neural Networks for Counting Substructures.
|
AISTATS | 2023 | 0 |
| What is the Inductive Bias of Flatness Regularization? A Study of Deep Matrix Factorization Models.
|
NIPS/NeurIPS | 2023 | 0 |
| The Exact Sample Complexity Gain from Invariances for Kernel Regression.
|
NIPS/NeurIPS | 2023 | 0 |
| Limits, approximation and size transferability for GNNs on sparse graphs via graphops.
|
NIPS/NeurIPS | 2023 | 0 |
| Expressive Sign Equivariant Networks for Spectral Geometric Learning.
|
NIPS/NeurIPS | 2023 | 0 |
| Sign and Basis Invariant Networks for Spectral Graph Representation Learning.
|
ICLR | 2023 | 0 |
| Training invariances and the low-rank phenomenon: beyond linear networks.
|
ICLR | 2022 | 4 |
| Robust Contrastive Learning against Noisy Views.
|
CVPR | 2022 | 14 |
| Optimization and Adaptive Generalization of Three layer Neural Networks.
|
ICLR | 2022 | 0 |
| On the generalization of learning algorithms that do not converge.
|
NIPS/NeurIPS | 2022 | 0 |
| Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks.
|
NIPS/NeurIPS | 2022 | 0 |
| Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions.
|
NIPS/NeurIPS | 2022 | 0 |
| Can contrastive learning avoid shortcut solutions?
|
NIPS/NeurIPS | 2021 | 44 |
| Measuring Generalization with Optimal Transport.
|
NIPS/NeurIPS | 2021 | 8 |
| Scaling up Continuous-Time Markov Chains Helps Resolve Underspecification.
|
NIPS/NeurIPS | 2021 | 4 |
| Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth.
|
ICML | 2021 | 29 |
| What training reveals about neural network complexity.
|
NIPS/NeurIPS | 2021 | 4 |
| Information Obfuscation of Graph Neural Networks.
|
ICML | 2021 | 0 |
| Contrastive Learning with Hard Negative Samples.
|
ICLR | 2021 | 0 |
| How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks.
|
ICLR | 2021 | 0 |
| IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method.
|
NIPS/NeurIPS | 2020 | 13 |
| Estimating Generalization under Distribution Shifts via Domain-Invariant Representations.
|
ICML | 2020 | 29 |
| Debiased Contrastive Learning.
|
NIPS/NeurIPS | 2020 | 248 |
| Distributionally Robust Bayesian Optimization.
|
AISTATS | 2020 | 51 |
| Strength from Weakness: Fast Learning Using Weak Supervision.
|
ICML | 2020 | 23 |
| Testing Determinantal Point Processes.
|
NIPS/NeurIPS | 2020 | 1 |
| Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions.
|
ICML | 2020 | 19 |
| Generalization and Representational Limits of Graph Neural Networks.
|
ICML | 2020 | 171 |
| Optimal approximation for unconstrained non-submodular minimization.
|
ICML | 2020 | 0 |
| Adaptive Sampling for Stochastic Risk-Averse Learning.
|
NIPS/NeurIPS | 2020 | 0 |
| What Can Neural Networks Reason About?
|
ICLR | 2020 | 0 |
| Are Girls Neko or Shōjo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization.
|
ACL | 2019 | 39 |
| Learning Generative Models across Incomparable Spaces.
|
ICML | 2019 | 76 |
| Flexible Modeling of Diversity with Strongly Log-Concave Distributions.
|
NIPS/NeurIPS | 2019 | 10 |
| Distributionally Robust Optimization and Generalization in Kernel Methods.
|
NIPS/NeurIPS | 2019 | 70 |
| Towards Optimal Transport with Global Invariances.
|
AISTATS | 2019 | 0 |
| Distributionally Robust Submodular Maximization.
|
AISTATS | 2019 | 0 |
| How Powerful are Graph Neural Networks?
|
ICLR | 2019 | 0 |
| Exponentiated Strongly Rayleigh Distributions.
|
NIPS/NeurIPS | 2018 | 12 |
| Representation Learning on Graphs with Jumping Knowledge Networks.
|
ICML | 2018 | 1102 |
| Provable Variational Inference for Constrained Log-Submodular Models.
|
NIPS/NeurIPS | 2018 | 3 |
| ResNet with one-neuron hidden layers is a Universal Approximator.
|
NIPS/NeurIPS | 2018 | 169 |
| Adversarially Robust Optimization with Gaussian Processes.
|
NIPS/NeurIPS | 2018 | 86 |
| Discrete Sampling using Semigradient-based Product Mixtures.
|
UAI | 2018 | 2 |
| Streaming Non-Monotone Submodular Maximization: Personalized Video Summarization on the Fly.
|
AAAI | 2018 | 0 |
| Structured Optimal Transport.
|
AISTATS | 2018 | 0 |
| Batched Large-scale Bayesian Optimization in High-dimensional Spaces.
|
AISTATS | 2018 | 0 |
| Max-value Entropy Search for Efficient Bayesian Optimization.
|
ICML | 2017 | 276 |
| Batched High-dimensional Bayesian Optimization via Structural Kernel Learning.
|
ICML | 2017 | 99 |
| Polynomial time algorithms for dual volume sampling.
|
NIPS/NeurIPS | 2017 | 31 |
| Parallel Streaming Wasserstein Barycenters.
|
NIPS/NeurIPS | 2017 | 75 |
| Robust Budget Allocation via Continuous Submodular Functions.
|
ICML | 2017 | 50 |
| Focused model-learning and planning for non-Gaussian continuous state-action systems.
|
ICRA | 2017 | 0 |
| Deep Metric Learning via Facility Location.
|
CVPR | 2017 | 0 |
| Deep Metric Learning via Lifted Structured Feature Embedding.
|
CVPR | 2016 | 183 |
| Fast DPP Sampling for Nystrom with Application to Kernel Methods.
|
ICML | 2016 | 73 |
| Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling.
|
NIPS/NeurIPS | 2016 | 33 |
| Cooperative Graphical Models.
|
NIPS/NeurIPS | 2016 | 1 |
| Gaussian quadrature for matrix inverse forms with applications.
|
ICML | 2016 | 0 |
| Efficient Sampling for k-Determinantal Point Processes.
|
AISTATS | 2016 | 0 |
| Optimization as Estimation with Gaussian Processes in Bandit Settings.
|
AISTATS | 2016 | 0 |
| On the Convergence Rate of Decomposable Submodular Function Minimization.
|
NIPS/NeurIPS | 2014 | 43 |
| Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets.
|
NIPS/NeurIPS | 2014 | 63 |
| Weakly-supervised Discovery of Visual Pattern Configurations.
|
NIPS/NeurIPS | 2014 | 159 |
| On learning to localize objects with minimal supervision.
|
ICML | 2014 | 253 |
| Learning Scalable Discriminative Dictionary with Sample Relatedness.
|
CVPR | 2014 | 17 |
| Monotone Closure of Relaxed Constraints in Submodular Optimization: Connections Between Minimization and Maximization.
|
UAI | 2014 | 34 |
| Parallel Double Greedy Submodular Maximization.
|
NIPS/NeurIPS | 2014 | 36 |
| Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions.
|
NIPS/NeurIPS | 2013 | 110 |
| A Principled Deep Random Field Model for Image Segmentation.
|
CVPR | 2013 | 73 |
| Reflection methods for user-friendly submodular optimization.
|
NIPS/NeurIPS | 2013 | 76 |
| Optimistic Concurrency Control for Distributed Unsupervised Learning.
|
NIPS/NeurIPS | 2013 | 34 |
| Fast Semidifferential-based Submodular Function Optimization.
|
ICML | 2013 | 103 |
| Approximation Bounds for Inference using Cooperative Cuts.
|
ICML | 2011 | 27 |
| On fast approximate submodular minimization.
|
NIPS/NeurIPS | 2011 | 64 |
| Submodularity beyond submodular energies: Coupling edges in graph cuts.
|
CVPR | 2011 | 201 |
| Online Submodular Minimization for Combinatorial Structures.
|
ICML | 2011 | 32 |
| Solution stability in linear programming relaxations: graph partitioning and unsupervised learning.
|
ICML | 2009 | 41 |
| Consistent Minimization of Clustering Objective Functions.
|
NIPS/NeurIPS | 2007 | 16 |
| Fast Kernel ICA using an Approximate Newton Method.
|
AISTATS | 2007 | 14 |