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ORCIDJoshua R. Wang
Person information
- affiliation: Google Research, Mountain View, CA, USA
- affiliation: Stanford University, Department of Computer Science, CA, USA
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Books and Theses
- 2018
[b1]Joshua R. Wang:
Theoretical models for practical problems: dynamic data structures, hierarchical clustering, and modern parallel computing. Stanford University, USA, 2018
Journal Articles
- 2023
- [j5]Benjamin Moseley, Joshua R. Wang:
Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search. J. Mach. Learn. Res. 24: 1:1-1:36 (2023) - [j4]Rad Niazadeh
, Negin Golrezaei, Joshua R. Wang, Fransisca Susan, Ashwinkumar Badanidiyuru:
Online Learning via Offline Greedy Algorithms: Applications in Market Design and Optimization. Manag. Sci. 69(7): 3797-3817 (2023) - 2020
- [j3]Rad Niazadeh, Tim Roughgarden, Joshua R. Wang:
Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization. J. Mach. Learn. Res. 21: 125:1-125:31 (2020) - 2019
- [j2]Tim Roughgarden, Joshua R. Wang:
Minimizing Regret with Multiple Reserves. ACM Trans. Economics and Comput. 7(3): 17:1-17:18 (2019) - 2018
- [j1]Tim Roughgarden, Sergei Vassilvitskii, Joshua R. Wang:
Shuffles and Circuits (On Lower Bounds for Modern Parallel Computation). J. ACM 65(6): 41:1-41:24 (2018)
Conference and Workshop Papers
- 2024
- [c23]Guru Guruganesh, Jon Schneider, Joshua R. Wang:
Prior-Free Mechanism with Welfare Guarantees. WWW 2024: 135-143 - 2023
- [c22]Sharat Ibrahimpur, Manish Purohit, Zoya Svitkina, Erik Vee, Joshua R. Wang:
Efficient Caching with Reserves via Marking. ICALP 2023: 80:1-80:20 - [c21]Paul Duetting, Guru Guruganesh, Jon Schneider, Joshua Ruizhi Wang:
Optimal No-Regret Learning for One-Sided Lipschitz Functions. ICML 2023: 8836-8850 - [c20]Guru Guruganesh, Jon Schneider, Joshua R. Wang, Junyao Zhao:
The Power of Menus in Contract Design. EC 2023: 818-848 - 2022
- [c19]Sharat Ibrahimpur, Manish Purohit, Zoya Svitkina, Erik Vee, Joshua R. Wang:
Caching with Reserves. APPROX/RANDOM 2022: 52:1-52:16 - [c18]Quanquan C. Liu, Manish Purohit, Zoya Svitkina, Erik Vee, Joshua R. Wang:
Scheduling with Communication Delay in Near-Linear Time. STACS 2022: 47:1-47:23 - 2021
- [c17]Guru Guruganesh, Allen Liu, Jon Schneider, Joshua R. Wang:
Margin-Independent Online Multiclass Learning via Convex Geometry. NeurIPS 2021: 29156-29167 - [c16]Guru Guruganesh, Jon Schneider, Joshua R. Wang:
Contracts under Moral Hazard and Adverse Selection. EC 2021: 563-582 - [c15]Rad Niazadeh, Negin Golrezaei, Joshua R. Wang, Fransisca Susan, Ashwinkumar Badanidiyuru:
Online Learning via Offline Greedy Algorithms: Applications in Market Design and Optimization. EC 2021: 737-738 - 2019
- [c14]Vaggos Chatziafratis, Tim Roughgarden, Joshua R. Wang:
On the Computational Power of Online Gradient Descent. COLT 2019: 624-662 - [c13]Badih Ghazi, Rina Panigrahy, Joshua R. Wang:
Recursive Sketches for Modular Deep Learning. ICML 2019: 2211-2220 - [c12]Ravi Kumar, Manish Purohit, Zoya Svitkina, Erik Vee, Joshua R. Wang:
Efficient Rematerialization for Deep Networks. NeurIPS 2019: 15146-15155 - 2018
- [c11]Tim Roughgarden, Joshua R. Wang:
An Optimal Learning Algorithm for Online Unconstrained Submodular Maximization. COLT 2018: 1307-1325 - [c10]Rad Niazadeh, Tim Roughgarden, Joshua R. Wang:
Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization. NeurIPS 2018: 9617-9627 - [c9]Josh Alman, Joshua R. Wang, Huacheng Yu:
Cell-probe lower bounds from online communication complexity. STOC 2018: 1003-1012 - 2017
- [c8]Benjamin Moseley, Joshua R. Wang:
Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search. NIPS 2017: 3094-3103 - 2016
- [c7]Tim Roughgarden, Joshua R. Wang:
The Complexity of the k-means Method. ESA 2016: 78:1-78:14 - [c6]Andrea Lincoln, Virginia Vassilevska Williams, Joshua R. Wang, R. Ryan Williams:
Deterministic Time-Space Trade-Offs for k-SUM. ICALP 2016: 58:1-58:14 - [c5]Tim Roughgarden, Joshua R. Wang:
Minimizing Regret with Multiple Reserves. EC 2016: 601-616 - [c4]Amir Abboud, Virginia Vassilevska Williams, Joshua R. Wang:
Approximation and Fixed Parameter Subquadratic Algorithms for Radius and Diameter in Sparse Graphs. SODA 2016: 377-391 - [c3]Tim Roughgarden, Sergei Vassilvitskii, Joshua R. Wang:
Shuffles and Circuits: (On Lower Bounds for Modern Parallel Computation). SPAA 2016: 1-12 - 2015
- [c2]Virginia Vassilevska Williams, Joshua R. Wang, Richard Ryan Williams, Huacheng Yu:
Finding Four-Node Subgraphs in Triangle Time. SODA 2015: 1671-1680 - 2014
- [c1]Joshua R. Wang:
Space-Efficient Randomized Algorithms for K-SUM. ESA 2014: 810-829
Reference Works
- 2016
- [r1]Joshua R. Wang, Richard Ryan Williams:
Exact Algorithms and Strong Exponential Time Hypothesis. Encyclopedia of Algorithms 2016: 657-661
Informal and Other Publications
- 2024
- [i18]Guru Guruganesh, Yoav Kolumbus, Jon Schneider, Inbal Talgam-Cohen, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Joshua R. Wang, S. Matthew Weinberg:
Contracting with a Learning Agent. CoRR abs/2401.16198 (2024) - 2023
- [i17]Sharat Ibrahimpur, Manish Purohit, Zoya Svitkina, Erik Vee, Joshua R. Wang:
Efficient Caching with Reserves via Marking. CoRR abs/2305.02508 (2023) - [i16]Guru Guruganesh, Jon Schneider, Joshua R. Wang, Junyao Zhao:
The Power of Menus in Contract Design. CoRR abs/2306.12667 (2023) - [i15]Ce Jin, Manish Purohit, Zoya Svitkina, Erik Vee, Joshua R. Wang:
New Tools for Peak Memory Scheduling. CoRR abs/2312.13526 (2023) - 2022
- [i14]Sharat Ibrahimpur, Manish Purohit, Zoya Svitkina, Erik Vee, Joshua R. Wang:
Caching with Reserves. CoRR abs/2207.05975 (2022) - 2021
- [i13]Rad Niazadeh, Negin Golrezaei, Joshua R. Wang, Fransisca Susan, Ashwinkumar Badanidiyuru:
Online Learning via Offline Greedy Algorithms: Applications in Market Design and Optimization. CoRR abs/2102.11050 (2021) - [i12]Quanquan C. Liu, Manish Purohit, Zoya Svitkina, Erik Vee, Joshua R. Wang:
Scheduling with Communication Delay in Near-Linear Time. CoRR abs/2108.02770 (2021) - [i11]Guru Guruganesh, Allen Liu, Jon Schneider, Joshua R. Wang:
Margin-Independent Online Multiclass Learning via Convex Geometry. CoRR abs/2111.08057 (2021) - 2020
- [i10]Guru Guruganesh, Jon Schneider, Joshua R. Wang:
Contracts under Moral Hazard and Adverse Selection. CoRR abs/2010.06742 (2020) - 2019
- [i9]Badih Ghazi, Rina Panigrahy, Joshua R. Wang:
Recursive Sketches for Modular Deep Learning. CoRR abs/1905.12730 (2019) - 2018
- [i8]Rad Niazadeh, Tim Roughgarden, Joshua R. Wang:
Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization. CoRR abs/1805.09480 (2018) - [i7]Tim Roughgarden, Joshua R. Wang:
An Optimal Algorithm for Online Unconstrained Submodular Maximization. CoRR abs/1806.03349 (2018) - [i6]Vaggos Chatziafratis, Tim Roughgarden, Joshua R. Wang:
On the Computational Power of Online Gradient Descent. CoRR abs/1807.01280 (2018) - 2017
- [i5]Josh Alman, Joshua R. Wang, Huacheng Yu:
Cell-Probe Lower Bounds from Online Communication Complexity. CoRR abs/1704.06185 (2017) - [i4]Josh Alman, Joshua R. Wang, Huacheng Yu:
Cell-Probe Lower Bounds from Online Communication Complexity. Electron. Colloquium Comput. Complex. TR17 (2017) - 2016
- [i3]Andrea Lincoln, Virginia Vassilevska Williams, Joshua R. Wang, R. Ryan Williams:
Deterministic Time-Space Tradeoffs for k-SUM. CoRR abs/1605.07285 (2016) - 2015
- [i2]Amir Abboud, Virginia Vassilevska Williams, Joshua R. Wang:
Approximation and Fixed Parameter Subquadratic Algorithms for Radius and Diameter. CoRR abs/1506.01799 (2015) - 2013
- [i1]Joshua R. Wang:
Space-Efficient Las Vegas Algorithms for K-SUM. CoRR abs/1303.1016 (2013)
Coauthor Index
aka: Tim Roughgarden
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