Di Wang

Publications

  • Deterministic Near-Linear Time Minimum Cut in Weighted Graphs.
    Monika Henzinger, Jason Li, Satish Rao, Di Wang
    SODA 2024 (best paper award).

  • Prior-Independent Auctions for Heterogeneous Bidders.
    Guru Guruganesh, Aranyak Mehta, Di Wang, Kangning Wang
    SODA 2024. [arXiv]

  • Robust Budget Pacing with a Single Sample.
    Santiago Balseiro, Rachitesh Kumar, Vahab Mirrokni, Balasubramanian Sivan, Di Wang
    ICML 2023 (oral presentation).

  • Online Bidding Algorithms for Return-on-Spend Constrained Advertisers.
    Zhe Feng, Swati Padmanabhan, Di Wang
    The Web Conference (WWW) 2023. [arXiv]

  • Targeted pandemic containment through identifying local contact network bottlenecks.
    Shenghao Yang, Priyabrata Senapati, Di Wang, Chris T Bauch, Kimon Fountoulakis
    PLoS computational biology, Volume 17, Issue 8, August 2021. . [link]

  • Learning Robust Algorithms for Online Allocation Problems Using Adversarial Training.
    Goran Zuzic, Di Wang, Aranyak Mehta, D Sivakumar
    ICLR 2022 Workshop on Gamification and Multiagent Solutions. [arXiv]

  • Minimum cost flows, MDPs, and ℓ1-regression in nearly linear time for dense instances.
    Jan Van Den Brand, Yin Tat Lee, Yang P Liu, Thatchaphol Saranurak, Aaron Sidford, Zhao Song, Di Wang
    STOC 2021. [arXiv]

  • 2-norm Flow Diffusion in Near-Linear Time.
    Li Chen, Richard Peng, Di Wang
    FOCS 2021. [arXiv]

  • Bipartite Matching in Nearly-linear Time on Moderately Dense Graphs.
    Jan van den Brand, Yin-Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Aaron Sidford, Zhao Song, Di Wang
    FOCS 2020. [arXiv]

  • p-Norm Flow Diffusion for Local Graph Clustering.
    Kimon Fountoulakis, Di Wang, Shenghao Yang
    In ICML 2020. [arXiv]

  • Greedy++: An Iterative Peeling Algorithm for Dense Subgraph Discovery.
    Digvijay Boob, Yu Gao, Richard Peng, Saurabh Sawlani, Charalampos E. Tsourakakis, Di Wang, Junxing Wang
    In TheWebConf (WWW) 2020. [arXiv]

  • Packing LPs are Hard to Solve Accurately, Assuming Linear Equations are Hard.
    Rasmus Kyng, Di Wang, Peng Zhang
    In SODA 2020.

  • Faster Width-dependent Algorithm for Mixed Packing and Covering LPs.
    Digvijay Boob, Saurabh Sawlani, Di Wang
    In Neurips 2019 (accepted for oral presentation). [arXiv]

  • Flows in Almost Linear Time via Adaptive Preconditioning.
    Rasmus Kyng, Richard Peng, Sushant Sachdeva, Di Wang
    In STOC 2019. [arXiv]

  • Expander Decomposition and Pruning: Faster, Stronger, and Simpler.
    Thatchaphol Saranurak, Di Wang
    In SODA 2019. [arXiv]

  • Fast Approximation Algorithms for Positive Linear Programs.
    Di Wang
    My PhD Dissertation. [link]

  • Capacity Releasing Diffusion for Speed and Locality.
    Di Wang, Kimon Fountoulakis, Monika Henzinger, Michael W Mahoney, Satish Rao
    In ICML 2017. [arXiv][talk]
    Implemented in Local Graph Clustering codebase.

  • Local Flow Partitioning for Faster Edge Connectivity.
    Monika Henzinger, Satish Rao, Di Wang
    SIAM Journal on Computing, Volume 49, Issue 1, Page 1-36, January 2020.
    Preliminary version in SODA 2017. [arXiv]

  • Approximating the Solution to Mixed Packing and Covering LPs in Parallel widetilde{O}(epsilon^{-3}) Time.
    Michael W Mahoney, Satish Rao, Di Wang, Peng Zhang
    In ICALP 2016. [pdf]

  • Unified Acceleration Method for Packing and Covering Problems via Diameter Reduction.
    Di Wang, Satish Rao, Michael W Mahoney
    In ICALP 2016. [arXiv]

  • Faster Parallel Solver for Positive Linear Programs via Dynamically-bucketed Selective Coordinate Descent.
    Di Wang, Michael Mahoney, Nishanth Mohan, Satish Rao
    Technical Report, Preprint: arXiv:1511.06468 (2015). [arXiv]

  • On the security of trustee-based social authentications.
    Neil Zhenqiang Gong, Di Wang
    In IEEE Trans. on Information Forensics and Security, Vol.9, No.8, August 2014.

  • Analyzing Quadratic Unconstrained Binary Optimization Problems Via Multicommodity Flows.
    Di Wang, Robert Kleinberg
    Discrete Applied Mathematics 157 (18) (2009) 3746-3753.