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09/18/2023
Context is Environment
Two lines of work are taking the central stage in AI research. On the on...
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06/24/2023
Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning
Self-supervised learning converts raw perceptual data such as images to ...
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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...
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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...
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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...
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01/31/2023
Debiasing Vision-Language Models via Biased Prompts
Machine learning models have been shown to inherit biases from their tra...
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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 ...
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12/28/2022
Optimal algorithms for group distributionally robust optimization and beyond
Distributionally robust optimization (DRO) can improve the robustness an...
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10/06/2022
InfoOT: Information Maximizing Optimal Transport
Optimal transport aligns samples across distributions by minimizing the ...
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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...
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08/16/2022
On the generalization of learning algorithms that do not converge
Generalization analyses of deep learning typically assume that the train...
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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...
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04/16/2022
Theory of Graph Neural Networks: Representation and Learning
Graph Neural Networks (GNNs), neural network architectures targeted to l...
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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 ...
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01/12/2022
Robust Contrastive Learning against Noisy Views
Contrastive learning relies on an assumption that positive pairs contain...
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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...
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06/08/2021
What training reveals about neural network complexity
This work explores the hypothesis that the complexity of the function a ...
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06/07/2021
Measuring Generalization with Optimal Transport
Understanding the generalization of deep neural networks is one of the m...
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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...
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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...
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10/09/2020
Contrastive Learning with Hard Negative Samples
We consider the question: how can you sample good negative examples for ...
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09/28/2020
Graph Adversarial Networks: Protecting Information against Adversarial Attacks
We study the problem of protecting information when learning with graph ...
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09/24/2020
How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks
We study how neural networks trained by gradient descent extrapolate, i....
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08/09/2020
Testing Determinantal Point Processes
Determinantal point processes (DPPs) are popular probabilistic models of...
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07/06/2020
Estimating Generalization under Distribution Shifts via Domain-Invariant Representations
When machine learning models are deployed on a test distribution differe...
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07/01/2020
Debiased Contrastive Learning
A prominent technique for self-supervised representation learning has be...
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06/11/2020
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method
We introduce a framework for designing primal methods under the decentra...
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02/20/2020
Distributionally Robust Bayesian Optimization
Robustness to distributional shift is one of the key challenges of conte...
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02/19/2020
Strength from Weakness: Fast Learning Using Weak Supervision
We study generalization properties of weakly supervised learning. That i...
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02/14/2020
Generalization and Representational Limits of Graph Neural Networks
We address two fundamental questions about graph neural networks (GNNs)....
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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...
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10/28/2019
Adaptive Sampling for Stochastic Risk-Averse Learning
We consider the problem of training machine learning models in a risk-av...
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10/13/2019
The Role of Embedding Complexity in Domain-invariant Representations
Unsupervised domain adaptation aims to generalize the hypothesis trained...
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06/12/2019
Flexible Modeling of Diversity with Strongly Log-Concave Distributions
Strongly log-concave (SLC) distributions are a rich class of discrete pr...
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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 ...
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05/30/2019
What Can Neural Networks Reason About?
Neural networks have successfully been applied to solving reasoning task...
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05/29/2019
Minimizing approximately submodular functions
The problem of minimizing a submodular function is well studied; several...
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05/27/2019
Distributionally Robust Optimization and Generalization in Kernel Methods
Distributionally robust optimization (DRO) has attracted attention in ma...
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05/14/2019
Learning Generative Models across Incomparable Spaces
Generative Adversarial Networks have shown remarkable success in learnin...
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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...
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10/25/2018
Adversarially Robust Optimization with Gaussian Processes
In this paper, we consider the problem of Gaussian process (GP) optimiza...
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10/01/2018
How Powerful are Graph Neural Networks?
Graph Neural Networks (GNNs) for representation learning of graphs broad...
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07/04/2018
Discrete Sampling using Semigradient-based Product Mixtures
We consider the problem of inference in discrete probabilistic models, t...
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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...
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06/25/2018
Towards Optimal Transport with Global Invariances
Many problems in machine learning involve calculating correspondences be...
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06/09/2018
Representation Learning on Graphs with Jumping Knowledge Networks
Recent deep learning approaches for representation learning on graphs fo...
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02/27/2018
Robust GANs against Dishonest Adversaries
Robustness of deep learning models is a property that has recently gaine...
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02/14/2018
Distributionally Robust Submodular Maximization
Submodular functions have applications throughout machine learning, but ...
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12/17/2017
Structured Optimal Transport
Optimal Transport has recently gained interest in machine learning for a...
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12/15/2017