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ORCIDJustin Whitehouse
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2020 – today
- 2024
[i9]Abhineet Agarwal, Anish Agarwal, Lorenzo Masoero, Justin Whitehouse:
Multi-Armed Bandits with Network Interference. CoRR abs/2405.18621 (2024)- [i8]Justin Whitehouse, Christopher Jung, Vasilis Syrgkanis, Bryan Wilder, Zhiwei Steven Wu:
Orthogonal Causal Calibration. CoRR abs/2406.01933 (2024) - 2023
- [c7]Justin Whitehouse, Aaditya Ramdas, Ryan Rogers, Steven Wu:
Fully-Adaptive Composition in Differential Privacy. ICML 2023: 36990-37007 - [c6]Anish Agarwal, Keegan Harris, Justin Whitehouse, Zhiwei Steven Wu:
Adaptive Principal Component Regression with Applications to Panel Data. NeurIPS 2023 - [c5]Justin Whitehouse, Aaditya Ramdas, Zhiwei Steven Wu:
On the Sublinear Regret of GP-UCB. NeurIPS 2023 - [i7]Anish Agarwal, Keegan Harris, Justin Whitehouse, Zhiwei Steven Wu:
Adaptive Principal Component Regression with Applications to Panel Data. CoRR abs/2307.01357 (2023) - [i6]Justin Whitehouse, Zhiwei Steven Wu, Aaditya Ramdas:
Improved Self-Normalized Concentration in Hilbert Spaces: Sublinear Regret for GP-UCB. CoRR abs/2307.07539 (2023) - 2022
- [j3]Benjamin Berg, Justin Whitehouse, Benjamin Moseley
, Weina Wang, Mor Harchol-Balter:
The case for phase-aware scheduling of parallelizable jobs. Perform. Evaluation 153: 102246 (2022) - [j2]Benjamin Berg, Justin Whitehouse, Benjamin Moseley, Weina Wang, Mor Harchol-Balter:
The Case for Phase-Aware Scheduling of Parallelizable Jobs. SIGMETRICS Perform. Evaluation Rev. 49(3): 65-66 (2022) - [c4]Justin Whitehouse, Aaditya Ramdas, Zhiwei Steven Wu, Ryan M. Rogers:
Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints. NeurIPS 2022 - [i5]Justin Whitehouse, Aaditya Ramdas, Ryan Rogers, Zhiwei Steven Wu:
Fully Adaptive Composition in Differential Privacy. CoRR abs/2203.05481 (2022) - [i4]Justin Whitehouse, Zhiwei Steven Wu, Aaditya Ramdas, Ryan Rogers:
Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints. CoRR abs/2206.07234 (2022) - 2020
- [c3]Benjamin Berg, Mor Harchol-Balter, Benjamin Moseley, Weina Wang, Justin Whitehouse:
Optimal Resource Allocation for Elastic and Inelastic Jobs. SPAA 2020: 75-87 - [i3]Benjamin Berg, Mor Harchol-Balter, Benjamin Moseley, Weina Wang, Justin Whitehouse:
Optimal Resource Allocation for Elastic and Inelastic Jobs. CoRR abs/2005.09745 (2020)
2010 – 2019
- 2019
- [j1]Kexin Pei, Shiqi Wang, Yuchi Tian, Justin Whitehouse, Carl Vondrick, Yinzhi Cao, Baishakhi Ray, Suman Jana, Junfeng Yang:
Bringing Engineering Rigor to Deep Learning. ACM SIGOPS Oper. Syst. Rev. 53(1): 59-67 (2019) - 2018
- [c2]Shiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang, Suman Jana:
Efficient Formal Safety Analysis of Neural Networks. NeurIPS 2018: 6369-6379 - [c1]Shiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang, Suman Jana:
Formal Security Analysis of Neural Networks using Symbolic Intervals. USENIX Security Symposium 2018: 1599-1614 - [i2]Shiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang, Suman Jana:
Formal Security Analysis of Neural Networks using Symbolic Intervals. CoRR abs/1804.10829 (2018) - [i1]Shiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang, Suman Jana:
Efficient Formal Safety Analysis of Neural Networks. CoRR abs/1809.08098 (2018)
Coauthor Index
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