Christopher Harshaw
Christopher Harshaw
Postdoctoral Fellow, UC Berkeley
Verified email at - Homepage
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Greed is good: Near-optimal submodular maximization via greedy optimization
M Feldman, C Harshaw, A Karbasi
Conference on Learning Theory, 758-784, 2017
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
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
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
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
Design and analysis of bipartite experiments under a linear exposure-response model
C Harshaw, F Sävje, D Eisenstat, V Mirrokni, J Pouget-Abadie
Electronic Journal of Statistics 17 (1), 464-518, 2023
How do you want your greedy: Simultaneous or repeated?
M Feldman, C Harshaw, A Karbasi
arXiv preprint arXiv:2009.13998, 2020
Optimized variance estimation under interference and complex experimental designs
C Harshaw, JA Middleton, F Sävje
arXiv preprint arXiv:2112.01709, 2021
The power of subsampling in submodular maximization
C Harshaw, E Kazemi, M Feldman, A Karbasi
Mathematics of Operations Research 47 (2), 1365-1393, 2022
A Design-based Riesz Representation Framework for Randomized Experiments
C Harshaw, F Sävje, Y Wang
arXiv preprint arXiv:2210.08698, 2022
Clip-OGD: An Experimental Design for Adaptive Neyman Allocation in Sequential Experiments
J Dai, P Gradu, C Harshaw
arXiv preprint arXiv:2305.17187, 2023
Algorithmic Advances for the Design and Analysis of Randomized Experiments
C Harshaw
Yale University, 2021
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