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Christopher Harshaw
Christopher Harshaw
PhD Candidate in Computer Science, Yale University
Verified email at yale.edu - Homepage
Title
Cited by
Cited by
Year
Greed is good: Near-optimal submodular maximization via greedy optimization
M Feldman, C Harshaw, A Karbasi
Conference on Learning Theory, 758-784, 2017
732017
Submodular maximization beyond non-negativity: Guarantees, fast algorithms, and applications
C Harshaw, M Feldman, J Ward, A Karbasi
International Conference on Machine Learning, 2634-2643, 2019
642019
Projection-free online optimization with stochastic gradient: From convexity to submodularity
L Chen, C Harshaw, H Hassani, A Karbasi
International Conference on Machine Learning, 814-823, 2018
492018
Graphprints: Towards a graph analytic method for network anomaly detection
CR Harshaw, RA Bridges, MD Iannacone, JW Reed, JR Goodall
Proceedings of the 11th Annual Cyber and Information Security Research …, 2016
342016
Balancing covariates in randomized experiments with the Gram--Schmidt Walk design
C Harshaw, F Sävje, D Spielman, P Zhang
arXiv preprint arXiv:1911.03071, 2019
24*2019
Design and analysis of bipartite experiments under a linear exposure-response model
C Harshaw, F Sävje, D Eisenstat, V Mirrokni, J Pouget-Abadie
arXiv preprint arXiv:2103.06392, 2021
52021
How Do You Want Your Greedy: Simultaneous or Repeated?
M Feldman, C Harshaw, A Karbasi
arXiv preprint arXiv:2009.13998, 2020
2*2020
The power of subsampling in submodular maximization
C Harshaw, E Kazemi, M Feldman, A Karbasi
Mathematics of Operations Research 47 (2), 1365-1393, 2022
12022
Optimized variance estimation under interference and complex experimental designs
C Harshaw, JA Middleton, F Sävje
arXiv preprint arXiv:2112.01709, 2021
2021
Algorithmic Advances for the Design and Analysis of Randomized Experiments
C Harshaw
Yale University, 2021
2021
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Articles 1–10