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06/24/2023
Boosting Multitask Learning on Graphs through Higher-Order Task Affinities
Predicting node labels on a given graph is a widely studied problem with...
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06/14/2023
Noise Stability Optimization for Flat Minima with Optimal Convergence Rates
We consider finding flat, local minimizers by adding average weight pert...
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03/25/2023
Identification of Negative Transfers in Multitask Learning Using Surrogate Models
Multitask learning is widely used in practice to train a low-resource ta...
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03/16/2023
Optimal Intervention on Weighted Networks via Edge Centrality
Suppose there is a spreading process such as an infectious disease propa...
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02/09/2023
Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion
Graph neural networks are widely used tools for graph prediction tasks. ...
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06/06/2022
Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees
We consider transfer learning approaches that fine-tune a pretrained dee...
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04/20/2022
Improved Worst-Group Robustness via Classifier Retraining on Independent Splits
High-capacity deep neural networks (DNNs) trained with Empirical Risk Mi...
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03/03/2022
Correct-N-Contrast: A Contrastive Approach for Improving Robustness to Spurious Correlations
Spurious correlations pose a major challenge for robust machine learning...
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11/08/2021
Improved Regularization and Robustness for Fine-tuning in Neural Networks
A widely used algorithm for transfer learning is fine-tuning, where a pr...
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10/22/2020
Sharp Bias-variance Tradeoffs of Hard Parameter Sharing in High-dimensional Linear Regression
Hard parameter sharing for multi-task learning is widely used in empiric...
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07/09/2020
Learning Over-Parametrized Two-Layer ReLU Neural Networks beyond NTK
We consider the dynamic of gradient descent for learning a two-layer neu...
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05/02/2020
Understanding and Improving Information Transfer in Multi-Task Learning
We investigate multi-task learning approaches that use a shared feature ...
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05/02/2020