← Authors

Stefanie Jegelka

103 papers · 2007–2025 · 7 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (30) 🐝 Cross-Pollinator (12) πŸƒ Academic Marathon (18) 🌍 Conference Polyglot (7) 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (7) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌟 Keyword Trendsetter Combo (3) 🏠 Conference Loyalist (41) 🀝 Dynamic Duo (13) πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (19) πŸ† Keyword Champion ⚑ Prolific Year (11) πŸ”₯ Unstoppable (10) ❓ The Questioner (8) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (108) πŸš€ Conference Pioneer πŸ’Ž Century Club (103)

Conferences

NIPS (41) ICML (26) ICLR (19) AISTATS (10) CVPR (5) ACL (1) COLT (1)

Top co-authors

Suvrit Sra (13) Yifei Wang (9) Ching-Yao Chuang (8) Behrooz Tahmasebi (8) Chengtao Li (7) Khashayar Gatmiry (7) Derek Lim (7) Keyulu Xu (6) Tommi Jaakkola (5) Sharut Gupta (5)

Research topics

Applications (1) Optimization & Theory (1)

Keywords

graph neural network (10) submodular optimization (9) determinantal point process (9) representation learning (7) generalization bound (7) bayesian optimization (6) combinatorial optimization (6) optimal transport (6) gaussian process (5) submodular function (5) contrastive learning (4) function minimization (4) discrete optimization (4) distributionally robust optimization (4) submodular minimization (3) mcmc sampling (3) structured prediction (3) domain adaptation (3) message passing (3) self-supervised learning (3)

Papers

Generalization Bounds for Canonicalization: A Comparative Study with Group Averaging ICLR 2025
Beyond Interpretability: The Gains of Feature Monosemanticity on Model Robustness ICLR 2025
Higher-Order Graphon Neural Networks: Approximation and Cut Distance ICLR 2025
Learning Efficient Positional Encodings with Graph Neural Networks ICLR 2025
Learning with Exact Invariances in Polynomial Time ICML 2025
Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation ICML 2025
Computing Optimal Regularizers for Online Linear Optimization COLT 2025
A Robust Kernel Statistical Test of Invariance: Detecting Subtle Asymmetries AISTATS 2025
Regularity in Canonicalized Models: A Theoretical Perspective AISTATS 2025
On the Emergence of Position Bias in Transformers ICML 2025
What is Wrong with Perplexity for Long-context Language Modeling? ICLR 2025
An Information Criterion for Controlled Disentanglement of Multimodal Data ICLR 2025
Generalization, Expressivity, and Universality of Graph Neural Networks on Attributed Graphs ICLR 2025
Position: Future Directions in the Theory of Graph Machine Learning ICML 2024
Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning? ICML 2024
Simplicity Bias via Global Convergence of Sharpness Minimization ICML 2024
A Universal Class of Sharpness-Aware Minimization Algorithms ICML 2024
On the Role of Attention Masks and LayerNorm in Transformers NIPS 2024
The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof NIPS 2024
A Canonicalization Perspective on Invariant and Equivariant Learning NIPS 2024
Are Graph Neural Networks Optimal Approximation Algorithms? NIPS 2024
A Theoretical Understanding of Self-Correction through In-context Alignment NIPS 2024
In-Context Symmetries: Self-Supervised Learning through Contextual World Models NIPS 2024
Understanding the Role of Equivariance in Self-supervised Learning NIPS 2024
Context is Environment ICLR 2024
Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning ICLR 2024
On the hardness of learning under symmetries ICLR 2024
A PoincarΓ© Inequality and Consistency Results for Signal Sampling on Large Graphs ICLR 2024
On the Stability of Expressive Positional Encodings for Graphs ICLR 2024
Sample Complexity Bounds for Estimating Probability Divergences under Invariances ICML 2024
Expressive Sign Equivariant Networks for Spectral Geometric Learning NIPS 2023
What is the Inductive Bias of Flatness Regularization? A Study of Deep Matrix Factorization Models NIPS 2023
Limits, approximation and size transferability for GNNs on sparse graphs via graphops NIPS 2023
The Exact Sample Complexity Gain from Invariances for Kernel Regression NIPS 2023
The Power of Recursion in Graph Neural Networks for Counting Substructures AISTATS 2023
InfoOT: Information Maximizing Optimal Transport ICML 2023
Efficiently predicting high resolution mass spectra with graph neural networks ICML 2023
Sign and Basis Invariant Networks for Spectral Graph Representation Learning ICLR 2023
Robust Contrastive Learning Against Noisy Views CVPR 2022
Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks NIPS 2022
Training invariances and the low-rank phenomenon: beyond linear networks ICLR 2022
On the generalization of learning algorithms that do not converge NIPS 2022
Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions NIPS 2022
Optimization and Adaptive Generalization of Three layer Neural Networks ICLR 2022
Scaling up Continuous-Time Markov Chains Helps Resolve Underspecification NIPS 2021
How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks ICLR 2021
What training reveals about neural network complexity NIPS 2021
Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth ICML 2021
Contrastive Learning with Hard Negative Samples ICLR 2021
Information Obfuscation of Graph Neural Networks ICML 2021
Can contrastive learning avoid shortcut solutions? NIPS 2021
Measuring Generalization with Optimal Transport NIPS 2021
Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions ICML 2020
Adaptive Sampling for Stochastic Risk-Averse Learning NIPS 2020
Debiased Contrastive Learning NIPS 2020
Testing Determinantal Point Processes NIPS 2020
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method NIPS 2020
Distributionally Robust Bayesian Optimization AISTATS 2020
What Can Neural Networks Reason About? ICLR 2020
Estimating Generalization under Distribution Shifts via Domain-Invariant Representations ICML 2020
Generalization and Representational Limits of Graph Neural Networks ICML 2020
Optimal approximation for unconstrained non-submodular minimization ICML 2020
Strength from Weakness: Fast Learning Using Weak Supervision ICML 2020
Are Girls Neko or Shōjo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization ACL 2019
Flexible Modeling of Diversity with Strongly Log-Concave Distributions NIPS 2019
Distributionally Robust Optimization and Generalization in Kernel Methods NIPS 2019
Towards Optimal Transport with Global Invariances AISTATS 2019
Distributionally Robust Submodular Maximization AISTATS 2019
How Powerful are Graph Neural Networks? ICLR 2019
Learning Generative Models across Incomparable Spaces ICML 2019
ResNet with one-neuron hidden layers is a Universal Approximator NIPS 2018
Structured Optimal Transport AISTATS 2018
Batched Large-scale Bayesian Optimization in High-dimensional Spaces AISTATS 2018
Adversarially Robust Optimization with Gaussian Processes NIPS 2018
Exponentiated Strongly Rayleigh Distributions NIPS 2018
Provable Variational Inference for Constrained Log-Submodular Models NIPS 2018
Representation Learning on Graphs with Jumping Knowledge Networks ICML 2018
Polynomial time algorithms for dual volume sampling NIPS 2017
Parallel Streaming Wasserstein Barycenters NIPS 2017
Robust Budget Allocation via Continuous Submodular Functions ICML 2017
Max-value Entropy Search for Efficient Bayesian Optimization ICML 2017
Batched High-dimensional Bayesian Optimization via Structural Kernel Learning ICML 2017
Deep Metric Learning via Facility Location CVPR 2017
Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling NIPS 2016
Deep Metric Learning via Lifted Structured Feature Embedding CVPR 2016
Optimization as Estimation with Gaussian Processes in Bandit Settings AISTATS 2016
Gaussian quadrature for matrix inverse forms with applications ICML 2016
Fast DPP Sampling for Nystrom with Application to Kernel Methods ICML 2016
Cooperative Graphical Models NIPS 2016
Efficient Sampling for k-Determinantal Point Processes AISTATS 2016
Learning Scalable Discriminative Dictionary with Sample Relatedness CVPR 2014
On learning to localize objects with minimal supervision ICML 2014
On the Convergence Rate of Decomposable Submodular Function Minimization NIPS 2014
Weakly-supervised Discovery of Visual Pattern Configurations NIPS 2014
Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets NIPS 2014
Parallel Double Greedy Submodular Maximization NIPS 2014
A Principled Deep Random Field Model for Image Segmentation CVPR 2013
Fast Semidifferential-based Submodular Function Optimization ICML 2013
Optimistic Concurrency Control for Distributed Unsupervised Learning NIPS 2013
Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions NIPS 2013
Reflection methods for user-friendly submodular optimization NIPS 2013
On fast approximate submodular minimization NIPS 2011
Consistent Minimization of Clustering Objective Functions NIPS 2007