Joshua R. Wang

I graduated from Stanford University in 2018 with a Ph.D. in Computer Science, advised by Tim Roughgarden. My current research interests include submodular functions, graph algorithms, and algorithmic game theory. You can reach me at joshw0 on Gmail.

Research Scientist at Google

Publications

2024

- G. Guruganesh, Y. Kolumbus, J. Schneider, I. Talgam-Cohen, E. Vlatakis-Gkaragkounis, J. R. Wang, and S. M. Weinberg. Contracting with a Learning Agent [arXiv].
  To appear in the 38th Conference on Neural Information Processing Systems (NeurIPS 2024).
- G. Guruganesh, J. Schneider, and J. R. Wang. Prior-Free Mechanisms with Welfare Guarantees [ACM].
  In the 33rd ACM Web Conference (WWW 2024).

2023

- S. Ibrahimpur, M. Purohit, Z. Svitkina, E. Vee, and J. R. Wang. Efficient Caching for Reserves via Marking [arXiv].
  In the 50th International Colloquium on Automata, Languages, and Programming (ICALP 2023).
- P. Dütting, G. Guruganesh, J. Schneider, and J. R. Wang. Optimal No-Regret Learning for One-Sided Lipschitz Functions [PMLR].
  In the 40th Annual International Conference on Machine Learning (ICML 2023).
- G. Guruganesh, J. Schneider, J. R. Wang, and J. Zhao. The Power of Menus in Contract Design [arXiv].
  In the 24th ACM Conference on Economics and Computation (EC 2023).

2022

- S. Ibrahimpur, M. Purohit, Z. Svitkina, E. Vee, and J. R. Wang. Caching with Reserves [arXiv].
  In the 25th International Conference on Approximation Algorithms for Combinatorial Optimization Problems (APPROX 2022).
- Q. C. Liu, M. Purohit, Z. Svitkina, E. Vee, and J. R. Wang. Scheduling with Communication Delay in Near-Linear Time [arXiv].
  In the 39th International Symposium on the Theoretical Aspects of Computer Science (STACS 2022).

2021

- G. Guruganesh, J. Schneider, and J. R. Wang. Margin-Independent Online Multiclass Learning Via Convex Geometry [arXiv].
  In the 35th Conference on Neural Information Processing Systems (NeurIPS 2021).
- R. Niazadeh, N. Golrezaei, J. R. Wang, F. Susan, and A. Badanidiyuru. Online Learning via Offline Greedy Algorithms: Applications in Market Design and Optimization [arXiv]
  In the 22nd ACM Conference on Economics and Computation (EC 2021).
  Minor revision, Management Science (MS).
- G. Guruganesh, J. Schneider, and J. R. Wang. Contracts Under Moral Hazard and Adverse Selection [arXiv].
  In the 22nd ACM Conference on Economics and Computation (EC 2021).

2020

- R. Niazadeh, T. Roughgarden, and J. R. Wang. Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization [JMLR].
  Journal of Machine Learning Research (JMLR).

2019

- R. Kumar, M. Purohit, Z. Svitkina, E. Vee, and J. R. Wang. Efficient Rematerialization for Deep Networks [NeurIPS Proceeding].
  In the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019).
- B. Ghazi, R. Panigrahy, and J. R. Wang. Recursive Sketches for Modular Deep Learning [arXiv].
  In the 36th Annual International Conference on Machine Learning (ICML 2019).
- V. Chatziafratis, T. Roughgarden, and J. R. Wang. On the Computational Power of Gradient Descent [arXiv].
  In the 32nd Annual Conference on Learning Theory (COLT 2019).

2018

- R. Niazadeh, T. Roughgarden, and J. R. Wang. Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization [arXiv].
  In the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018).
  Oral Presentation (top 30 out of 4856 submissions).
- T. Roughgarden and J. R. Wang. An Optimal Learning Algorithm for Online Unconstrained Submodular Maximization [arXiv].
  In the 31st Annual Conference on Learning Theory (COLT 2018).
- J. Alman, J. R. Wang, and H. Yu. Cell-Probe Lower Bounds from Online Communication Complexity [arXiv].
  In the 50th Annual ACM Symposium on the Theory of Computing (STOC 2018).
- T. Roughgarden, S. Vassilvitskii, and J. R. Wang. Shuffles and Circuits (On Lower Bounds for Modern Parallel Computation) [ACM].
  Journal of the ACM (JACM).

2017

- B. Moseley and J. R. Wang. Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-Means, and Local Search [NeurIPS Proceedings].
  In the 31st Conference on Neural Information Processing Systems (NeurIPS 2017).
  Oral Presentation (top 40 out of 3240 submissions).

2016

- T. Roughgarden and J. R. Wang. The Complexity of the k-means Method.
  In the 24th Annual European Symposium on Algorithms (ESA 2016).
- T. Roughgarden and J. R. Wang. Minimizing Regret with Multiple Reserves.
  In the 17th ACM Conference on Economics and Computation (EC 2016).
- A. Lincoln, V. Vassilevska Williams, J. R. Wang, and R. Williams. Deterministic Time-Space Tradeoffs for k-SUM [arXiv].
  In the 43rd International Colloquium on Automata, Languages, and Programming (ICALP 2016).
- T. Roughgarden, S. Vassilvitskii, and J. R. Wang. Shuffles and Circuits (On Lower Bounds for Modern Parallel Computation)
  In the 28th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 2016).
  Best paper award.
- A. Abboud, V. Vassilevska Williams, and J. R. Wang. Approximation and Fixed Parameter Subquadratic Algorithms for Radius and Diameter in Sparse Graphs [arXiv].
  In the 26th ACM-SIAM Symposium On Discrete Algorithms (SODA 2016).

2015

- V. Vassilevska Williams, J. R. Wang, R. Williams, and H. Yu. Finding four-node subgraphs in triangle time.
  In the 25th ACM-SIAM Symposium On Discrete Algorithms (SODA 2015).

2014

- J. R. Wang. Space-efficient randomized algorithms for k-sum.
  In the 22nd Annual European Symposium on Algorithms (ESA 2014).
  Best student paper award.