970 citations · 3k across the 88 of their papers we have counts for
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Deep Metric Learning via Facility Location
Hyun Oh Song, Stefanie Jegelka, Vivek Rathod +1
Learning the representation and the similarity metric in an end-to-end fashion with deep networks have demonstrated outstanding results for clustering and retrieval. However, these…
Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling
Chengtao Li, Stefanie Jegelka, Suvrit Sra
We study probability measures induced by set functions with constraints. Such measures arise in a variety of real-world settings, where prior knowledge, resource limitations, or ot…
Fast Sampling for Strongly Rayleigh Measures with Application to Determinantal Point Processes
Chengtao Li, Stefanie Jegelka, Suvrit Sra
In this note we consider sampling from (non-homogeneous) strongly Rayleigh probability measures. As an important corollary, we obtain a fast mixing Markov Chain sampler for Determi…
Focused Model-Learning and Planning for Non-Gaussian Continuous State-Action Systems
Zi Wang, Stefanie Jegelka, Leslie Pack Kaelbling +1
We introduce a framework for model learning and planning in stochastic domains with continuous state and action spaces and non-Gaussian transition models. It is efficient because (…
Fast DPP Sampling for Nyström with Application to Kernel Methods
Chengtao Li, Stefanie Jegelka, Suvrit Sra
The Nyström method has long been popular for scaling up kernel methods. Its theoretical guarantees and empirical performance rely critically on the quality of the landmarks selecte…