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A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs
Thien Le, Luana Ruiz, Stefanie Jegelka
Large-scale graph machine learning is challenging as the complexity of learning models scales with the graph size. Subsampling the graph is a viable alternative, but sampling on gr…
Sample Complexity Bounds for Estimating Probability Divergences under Invariances
Behrooz Tahmasebi, Stefanie Jegelka
Group-invariant probability distributions appear in many data-generative models in machine learning, such as graphs, point clouds, and images. In practice, one often needs to estim…
Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning
Sharut Gupta, Joshua Robinson, Derek Lim +2
Self-supervised learning converts raw perceptual data such as images to a compact space where simple Euclidean distances measure meaningful variations in data. In this paper, we ex…
The Inductive Bias of Flatness Regularization for Deep Matrix Factorization
Khashayar Gatmiry, Zhiyuan Li, Ching-Yao Chuang +3
Recent works on over-parameterized neural networks have shown that the stochasticity in optimizers has the implicit regularization effect of minimizing the sharpness of the loss fu…
Limits, approximation and size transferability for GNNs on sparse graphs via graphops
Thien Le, Stefanie Jegelka
Can graph neural networks generalize to graphs that are different from the graphs they were trained on, e.g., in size? In this work, we study this question from a theoretical persp…
The Exact Sample Complexity Gain from Invariances for Kernel Regression
Behrooz Tahmasebi, Stefanie Jegelka
In practice, encoding invariances into models improves sample complexity. In this work, we study this phenomenon from a theoretical perspective. In particular, we provide minimax o…
Debiasing Vision-Language Models via Biased Prompts
Ching-Yao Chuang, Varun Jampani, Yuanzhen Li +2
Machine learning models have been shown to inherit biases from their training datasets. This can be particularly problematic for vision-language foundation models trained on uncura…
Efficiently predicting high resolution mass spectra with graph neural networks
Michael Murphy, Stefanie Jegelka, Ernest Fraenkel +3
Identifying a small molecule from its mass spectrum is the primary open problem in computational metabolomics. This is typically cast as information retrieval: an unknown spectrum…