Joshua R. Wang

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.