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03/29/2023
Queer In AI: A Case Study in Community-Led Participatory AI
We present Queer in AI as a case study for community-led participatory d...
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03/10/2023
Exphormer: Sparse Transformers for Graphs
Graph transformers have emerged as a promising architecture for a variet...
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03/03/2023
Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation
Maximum mean discrepancy (MMD) is a particularly useful distance metric ...
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02/11/2023
How to prepare your task head for finetuning
In deep learning, transferring information from a pretrained network to ...
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12/16/2022
Efficient Conditionally Invariant Representation Learning
We introduce the Conditional Independence Regression CovariancE (CIRCE),...
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11/15/2022
MMD-B-Fair: Learning Fair Representations with Statistical Testing
We introduce a method, MMD-B-Fair, to learn fair representations of data...
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10/21/2022
A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models
We prove a new generalization bound that shows for any class of linear p...
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08/16/2022
Object Discovery via Contrastive Learning for Weakly Supervised Object Detection
Weakly Supervised Object Detection (WSOD) is a task that detects objects...
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06/25/2022
Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels
We propose a new method for approximating active learning acquisition st...
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06/25/2022
A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel
Empirical neural tangent kernels (eNTKs) can provide a good understandin...
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06/13/2022
Evaluating Graph Generative Models with Contrastively Learned Features
A wide range of models have been proposed for Graph Generative Models, n...
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05/25/2022
Differentially Private Data Generation Needs Better Features
Training even moderately-sized generative models with differentially-pri...
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05/02/2022
One Weird Trick to Improve Your Semi-Weakly Supervised Semantic Segmentation Model
Semi-weakly supervised semantic segmentation (SWSSS) aims to train a mod...
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03/04/2022
Better Supervisory Signals by Observing Learning Paths
Better-supervised models might have better performance. In this paper, w...
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12/08/2021
Optimistic Rates: A Unifying Theory for Interpolation Learning and Regularization in Linear Regression
We study a localized notion of uniform convergence known as an "optimist...
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06/17/2021
Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds, and Benign Overfitting
We consider interpolation learning in high-dimensional linear regression...
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06/15/2021
Self-Supervised Learning with Kernel Dependence Maximization
We approach self-supervised learning of image representations from a sta...
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06/14/2021
Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data
Modern kernel-based two-sample tests have shown great success in disting...
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01/04/2021