Justin Whitehouse

According to our database, Justin Whitehouse authored at least 12 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Orthogonal Causal Calibration.

Justin Whitehouse, Christopher Jung, Vasilis Syrgkanis, Bryan Wilder, Zhiwei Steven Wu

CoRR, 2024

Multi-Armed Bandits with Network Interference.

Abhineet Agarwal, Anish Agarwal, Lorenzo Masoero, Justin Whitehouse

CoRR, 2024

2023

Improved Self-Normalized Concentration in Hilbert Spaces: Sublinear Regret for GP-UCB.

Justin Whitehouse, Zhiwei Steven Wu, Aaditya Ramdas

CoRR, 2023

On the Sublinear Regret of GP-UCB.

Justin Whitehouse, Aaditya Ramdas, Zhiwei Steven Wu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Adaptive Principal Component Regression with Applications to Panel Data.

Anish Agarwal, Keegan Harris, Justin Whitehouse, Zhiwei Steven Wu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Fully-Adaptive Composition in Differential Privacy.

Justin Whitehouse, Aaditya Ramdas, Ryan Rogers, Steven Wu

Proceedings of the International Conference on Machine Learning, 2023

2022

The case for phase-aware scheduling of parallelizable jobs.

Benjamin Berg, Justin Whitehouse, Benjamin Moseley, Weina Wang, Mor Harchol-Balter

Perform. Evaluation, 2022

Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints.

Justin Whitehouse, Aaditya Ramdas, Zhiwei Steven Wu, Ryan M. Rogers

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2020

Optimal Resource Allocation for Elastic and Inelastic Jobs.

Benjamin Berg, Mor Harchol-Balter, Benjamin Moseley, Weina Wang, Justin Whitehouse

Proceedings of the SPAA '20: 32nd ACM Symposium on Parallelism in Algorithms and Architectures, 2020

2019

Bringing Engineering Rigor to Deep Learning.

Kexin Pei, Shiqi Wang, Yuchi Tian, Justin Whitehouse, Carl Vondrick, Yinzhi Cao, Baishakhi Ray, Suman Jana, Junfeng Yang

ACM SIGOPS Oper. Syst. Rev., 2019

2018

Formal Security Analysis of Neural Networks using Symbolic Intervals.

Shiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang, Suman Jana

Proceedings of the 27th USENIX Security Symposium, 2018

Efficient Formal Safety Analysis of Neural Networks.

Shiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang, Suman Jana

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018