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9 papers · 2 filters
Multi-Entity Dependence Learning with Rich Context via Conditional Variational Auto-encoder
Luming Tang, Yexiang Xue, Di Chen +1
Multi-Entity Dependence Learning (MEDL) explores conditional correlations among multiple entities. The availability of rich contextual information requires a nimble learning scheme…
Multi-view Graph Embedding with Hub Detection for Brain Network Analysis
Guixiang Ma, Chun-Ta Lu, Lifang He +2
Multi-view graph embedding has become a widely studied problem in the area of graph learning. Most of the existing works on multi-view graph embedding aim to find a shared common n…
Gaussian Quadrature for Kernel Features
Tri Dao, Christopher De Sa, Christopher Ré
Kernel methods have recently attracted resurgent interest, showing performance competitive with deep neural networks in tasks such as speech recognition. The random Fourier feature…
On Fairness and Calibration
Geoff Pleiss, Manish Raghavan, Felix Wu +2
The machine learning community has become increasingly concerned with the potential for bias and discrimination in predictive models. This has motivated a growing line of work on w…
A Data Prism: Semi-Verified Learning in the Small-Alpha Regime
Michela Meister, Gregory Valiant
We consider a model of unreliable or crowdsourced data where there is an underlying set of binary variables, each evaluator contributes a (possibly unreliable or adversarial) e…
Spectrally-normalized margin bounds for neural networks
Peter Bartlett, Dylan J. Foster, Matus Telgarsky
This paper presents a margin-based multiclass generalization bound for neural networks that scales with their margin-normalized "spectral complexity": their Lipschitz constant, mea…