Stefanie Jegelka

research

∙ 09/18/2023

Context is Environment

Two lines of work are taking the central stage in AI research. On the on...

0 Sharut Gupta, et al. ∙

research

∙ 06/24/2023

Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning

Self-supervised learning converts raw perceptual data such as images to ...

0 Sharut Gupta, et al. ∙

research

∙ 06/22/2023

The Inductive Bias of Flatness Regularization for Deep Matrix Factorization

Recent works on over-parameterized neural networks have shown that the s...

0 Khashayar Gatmiry, et al. ∙

research

∙ 06/07/2023

Limits, approximation and size transferability for GNNs on sparse graphs via graphops

Can graph neural networks generalize to graphs that are different from t...

0 Thien Le, et al. ∙

research

∙ 03/24/2023

The Exact Sample Complexity Gain from Invariances for Kernel Regression on Manifolds

In practice, encoding invariances into models helps sample complexity. I...

0 Behrooz Tahmasebi, et al. ∙

research

∙ 01/31/2023

Debiasing Vision-Language Models via Biased Prompts

Machine learning models have been shown to inherit biases from their tra...

0 Ching-Yao Chuang, et al. ∙

research

∙ 01/26/2023

Efficiently predicting high resolution mass spectra with graph neural networks

Identifying a small molecule from its mass spectrum is the primary open ...

0 Michael Murphy, et al. ∙

research

∙ 12/28/2022

Optimal algorithms for group distributionally robust optimization and beyond

Distributionally robust optimization (DRO) can improve the robustness an...

0 Tasuku Soma, et al. ∙

research

∙ 10/06/2022

InfoOT: Information Maximizing Optimal Transport

Optimal transport aligns samples across distributions by minimizing the ...

5 Ching-Yao Chuang, et al. ∙

research

∙ 10/04/2022

Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks

Understanding generalization and robustness of machine learning models f...

0 Ching-Yao Chuang, et al. ∙

research

∙ 08/16/2022

On the generalization of learning algorithms that do not converge

Generalization analyses of deep learning typically assume that the train...

0 Nisha Chandramoorthy, et al. ∙

research

∙ 08/08/2022

Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions

Integrating functions on discrete domains into neural networks is key to...

0 Nikolaos Karalias, et al. ∙

research

∙ 04/16/2022

Theory of Graph Neural Networks: Representation and Learning

Graph Neural Networks (GNNs), neural network architectures targeted to l...

0 Stefanie Jegelka, et al. ∙

research

∙ 01/28/2022

Training invariances and the low-rank phenomenon: beyond linear networks

The implicit bias induced by the training of neural networks has become ...

0 Thien Le, et al. ∙

research

∙ 01/12/2022

Robust Contrastive Learning against Noisy Views

Contrastive learning relies on an assumption that positive pairs contain...

12 Ching-Yao Chuang, et al. ∙

research

∙ 07/06/2021

Scaling up Continuous-Time Markov Chains Helps Resolve Underspecification

Modeling the time evolution of discrete sets of items (e.g., genetic mut...

0 Alkis Gotovos, et al. ∙

research

∙ 06/08/2021

What training reveals about neural network complexity

This work explores the hypothesis that the complexity of the function a ...

0 Andreas Loukas, et al. ∙

research

∙ 06/07/2021

Measuring Generalization with Optimal Transport

Understanding the generalization of deep neural networks is one of the m...

0 Ching-Yao Chuang, et al. ∙

research

∙ 05/10/2021

Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth

Graph Neural Networks (GNNs) have been studied through the lens of expre...

0 Keyulu Xu, et al. ∙

research

∙ 12/06/2020

Counting Substructures with Higher-Order Graph Neural Networks: Possibility and Impossibility Results

While massage passing based Graph Neural Networks (GNNs) have become inc...

0 Behrooz Tahmasebi, et al. ∙

research

∙ 10/09/2020

Contrastive Learning with Hard Negative Samples

We consider the question: how can you sample good negative examples for ...

0 Joshua Robinson, et al. ∙

research

∙ 09/28/2020

Graph Adversarial Networks: Protecting Information against Adversarial Attacks

We study the problem of protecting information when learning with graph ...

20 Peiyuan Liao, et al. ∙

research

∙ 09/24/2020

How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks

We study how neural networks trained by gradient descent extrapolate, i....

68 Keyulu Xu, et al. ∙

research

∙ 08/09/2020

Testing Determinantal Point Processes

Determinantal point processes (DPPs) are popular probabilistic models of...

0 Khashayar Gatmiry, et al. ∙

research

∙ 07/06/2020

Estimating Generalization under Distribution Shifts via Domain-Invariant Representations

When machine learning models are deployed on a test distribution differe...

0 Ching-Yao Chuang, et al. ∙

research

∙ 07/01/2020

Debiased Contrastive Learning

A prominent technique for self-supervised representation learning has be...

0 Ching-Yao Chuang, et al. ∙

research

∙ 06/11/2020

IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method

We introduce a framework for designing primal methods under the decentra...

0 Yossi Arjevani, et al. ∙

research

∙ 02/20/2020

Distributionally Robust Bayesian Optimization

Robustness to distributional shift is one of the key challenges of conte...

6 Johannes Kirschner, et al. ∙

research

∙ 02/19/2020

Strength from Weakness: Fast Learning Using Weak Supervision

We study generalization properties of weakly supervised learning. That i...

8 Joshua Robinson, et al. ∙

research

∙ 02/14/2020

Generalization and Representational Limits of Graph Neural Networks

We address two fundamental questions about graph neural networks (GNNs)....

0 Vikas K. Garg, et al. ∙

research

∙ 02/09/2020

On the Complexity of Minimizing Convex Finite Sums Without Using the Indices of the Individual Functions

Recent advances in randomized incremental methods for minimizing L-smoot...

5 Yossi Arjevani, et al. ∙

research

∙ 10/28/2019

Adaptive Sampling for Stochastic Risk-Averse Learning

We consider the problem of training machine learning models in a risk-av...

24 Sebastian Curi, et al. ∙

research

∙ 10/13/2019

The Role of Embedding Complexity in Domain-invariant Representations

Unsupervised domain adaptation aims to generalize the hypothesis trained...

0 Ching-Yao Chuang, et al. ∙

research

∙ 06/12/2019

Flexible Modeling of Diversity with Strongly Log-Concave Distributions

Strongly log-concave (SLC) distributions are a rich class of discrete pr...

4 Joshua Robinson, et al. ∙

research

∙ 06/04/2019

Are Girls Neko or Shōjo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization

Cross-lingual word embeddings (CLWE) underlie many multilingual natural ...

0 Mozhi Zhang, et al. ∙

research

∙ 05/30/2019

What Can Neural Networks Reason About?

Neural networks have successfully been applied to solving reasoning task...

0 Keyulu Xu, et al. ∙

research

∙ 05/29/2019

Minimizing approximately submodular functions

The problem of minimizing a submodular function is well studied; several...

0 Marwa El Halabi, et al. ∙

research

∙ 05/27/2019

Distributionally Robust Optimization and Generalization in Kernel Methods

Distributionally robust optimization (DRO) has attracted attention in ma...

0 Matthew Staib, et al. ∙

research

∙ 05/14/2019

Learning Generative Models across Incomparable Spaces

Generative Adversarial Networks have shown remarkable success in learnin...

1 Charlotte Bunne, et al. ∙

research

∙ 12/31/2018

Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks

Leveraging new data sources is a key step in accelerating the pace of ma...

0 Edward Kim, et al. ∙

research

∙ 10/25/2018

Adversarially Robust Optimization with Gaussian Processes

In this paper, we consider the problem of Gaussian process (GP) optimiza...

0 Ilija Bogunovic, et al. ∙

research

∙ 10/01/2018

How Powerful are Graph Neural Networks?

Graph Neural Networks (GNNs) for representation learning of graphs broad...

14 Keyulu Xu, et al. ∙

research

∙ 07/04/2018

Discrete Sampling using Semigradient-based Product Mixtures

We consider the problem of inference in discrete probabilistic models, t...

0 Alkis Gotovos, et al. ∙

research

∙ 06/28/2018

ResNet with one-neuron hidden layers is a Universal Approximator

We demonstrate that a very deep ResNet with stacked modules with one neu...

10 Hongzhou Lin, et al. ∙

research

∙ 06/25/2018

Towards Optimal Transport with Global Invariances

Many problems in machine learning involve calculating correspondences be...

0 David Alvarez-Melis, et al. ∙

research

∙ 06/09/2018

Representation Learning on Graphs with Jumping Knowledge Networks

Recent deep learning approaches for representation learning on graphs fo...

0 Keyulu Xu, et al. ∙

research

∙ 02/27/2018

Robust GANs against Dishonest Adversaries

Robustness of deep learning models is a property that has recently gaine...

0 Zhi Xu, et al. ∙

research

∙ 02/14/2018

Distributionally Robust Submodular Maximization

Submodular functions have applications throughout machine learning, but ...

0 Matthew Staib, et al. ∙

research

∙ 12/17/2017

Structured Optimal Transport

Optimal Transport has recently gained interest in machine learning for a...

0 David Alvarez-Melis, et al. ∙

research

∙ 12/15/2017

Graph-Sparse Logistic Regression

We introduce Graph-Sparse Logistic Regression, a new algorithm for class...

0 Alexander LeNail, et al. ∙

Stefanie Jegelka

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