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2020 – today
- 2024
- [j11]Janardhan Kulkarni, Yang P. Liu
, Ashwin Sah, Mehtaab S. Sawhney, Jakub Tarnawski:
Online Edge Coloring via Tree Recurrences and Correlation Decay. SIAM J. Comput. 53(1): 87-110 (2024) - [j10]Da Yu, Sivakanth Gopi, Janardhan Kulkarni, Zinan Lin, Saurabh Naik, Tomasz Lukasz Religa, Jian Yin, Huishuai Zhang:
Selective Pre-training for Private Fine-tuning. Trans. Mach. Learn. Res. 2024 (2024) - [c61]Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Harsha Nori, Sergey Yekhanin:
Differentially Private Synthetic Data via Foundation Model APIs 1: Images. ICLR 2024 - [c60]Xinyu Tang, Richard Shin, Huseyin A. Inan, Andre Manoel, Fatemehsadat Mireshghallah, Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Robert Sim:
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation. ICLR 2024 - [c59]Fan Wu, Huseyin A. Inan, Arturs Backurs, Varun Chandrasekaran, Janardhan Kulkarni, Robert Sim:
Privately Aligning Language Models with Reinforcement Learning. ICLR 2024 - [c58]Janardhan Kulkarni
Optimal Online Discrepancy Minimization. STOC 2024: 1832-1840 - [i48]Pierre Tholoniat, Huseyin A. Inan, Janardhan Kulkarni, Robert Sim:
Differentially Private Training of Mixture of Experts Models. CoRR abs/2402.07334 (2024) - [i47]Muhammad Adnan, Amar Phanishayee, Janardhan Kulkarni, Prashant J. Nair, Divya Mahajan:
Workload-Aware Hardware Accelerator Mining for Distributed Deep Learning Training. CoRR abs/2404.14632 (2024) - 2023
- [j9]Da Yu, Gautam Kamath, Janardhan Kulkarni, Tie-Yan Liu, Jian Yin, Huishuai Zhang:
Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent. Trans. Mach. Learn. Res. 2023 (2023) - [c57]Ruixiang Tang
Assessing Privacy Risks in Language Models: A Case Study on Summarization Tasks. EMNLP (Findings) 2023: 15406-15418 - [c56]Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, Jiang Bian:
Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping. ICLR 2023 - [i46]Da Yu, Sivakanth Gopi, Janardhan Kulkarni, Zinan Lin, Saurabh Naik, Tomasz Lukasz Religa, Jian Yin, Huishuai Zhang:
Selective Pre-training for Private Fine-tuning. CoRR abs/2305.13865 (2023) - [i45]Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Harsha Nori, Sergey Yekhanin:
Differentially Private Synthetic Data via Foundation Model APIs 1: Images. CoRR abs/2305.15560 (2023) - [i44]Janardhan Kulkarni, Victor Reis, Thomas Rothvoss:
Optimal Online Discrepancy Minimization. CoRR abs/2308.01406 (2023) - [i43]Xinyu Tang, Richard Shin, Huseyin A. Inan, Andre Manoel, Fatemehsadat Mireshghallah, Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Robert Sim:
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation. CoRR abs/2309.11765 (2023) - [i42]Ruixiang Tang, Gord Lueck, Rodolfo Quispe, Huseyin A. Inan, Janardhan Kulkarni, Xia Hu:
Assessing Privacy Risks in Language Models: A Case Study on Summarization Tasks. CoRR abs/2310.13291 (2023) - [i41]Fan Wu, Huseyin A. Inan, Arturs Backurs, Varun Chandrasekaran, Janardhan Kulkarni, Robert Sim:
Privately Aligning Language Models with Reinforcement Learning. CoRR abs/2310.16960 (2023) - [i40]Zeyu Shen, Anilesh K. Krishnaswamy, Janardhan Kulkarni, Kamesh Munagala:
Classification with Partially Private Features. CoRR abs/2312.07583 (2023) - [i39]Bingbin Liu, Sébastien Bubeck, Ronen Eldan, Janardhan Kulkarni, Yuanzhi Li, Anh Nguyen, Rachel Ward, Yi Zhang:
TinyGSM: achieving >80% on GSM8k with small language models. CoRR abs/2312.09241 (2023) - 2022
- [j8]Sayan Bhattacharya, Fabrizio Grandoni
Fully Dynamic (Δ +1)-Coloring in O(1) Update Time. ACM Trans. Algorithms 18(2): 10:1-10:25 (2022) - [j7]Gautam Kamath
Introduction to the Special Issue on ACM-SIAM Symposium on Discrete Algorithms (SODA) 2020. ACM Trans. Algorithms 18(4): 30:1-30:2 (2022) - [c55]Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A. Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, Huishuai Zhang:
Differentially Private Fine-tuning of Language Models. ICLR 2022 - [c54]Xuechen Li, Daogao Liu, Tatsunori B. Hashimoto, Huseyin A. Inan, Janardhan Kulkarni, Yin Tat Lee, Abhradeep Guha Thakurta:
When Does Differentially Private Learning Not Suffer in High Dimensions? NeurIPS 2022 - [c53]Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin A. Inan, Lukas Wutschitz, Janardhan Kulkarni:
Differentially Private Model Compression. NeurIPS 2022 - [c52]Jayashree Mohan, Amar Phanishayee, Janardhan Kulkarni, Vijay Chidambaram:
Looking Beyond GPUs for DNN Scheduling on Multi-Tenant Clusters. OSDI 2022: 579-596 - [c51]Sami Davies, Janardhan Kulkarni, Thomas Rothvoss, Sai Sandeep, Jakub Tarnawski, Yihao Zhang:
On the Hardness of Scheduling With Non-Uniform Communication Delays. SODA 2022: 316-328 - [c50]Janardhan Kulkarni, Yang P. Liu
Online edge coloring via tree recurrences and correlation decay. STOC 2022: 104-116 - [i38]Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin A. Inan, Lukas Wutschitz, Janardhan Kulkarni:
Differentially Private Model Compression. CoRR abs/2206.01838 (2022) - [i37]Da Yu, Gautam Kamath
Per-Instance Privacy Accounting for Differentially Private Stochastic Gradient Descent. CoRR abs/2206.02617 (2022) - [i36]Xuechen Li, Daogao Liu, Tatsunori Hashimoto, Huseyin A. Inan, Janardhan Kulkarni, Yin Tat Lee, Abhradeep Guha Thakurta:
When Does Differentially Private Learning Not Suffer in High Dimensions? CoRR abs/2207.00160 (2022) - [i35]Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, Jiang Bian:
Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping. CoRR abs/2212.01539 (2022) - 2021
- [j6]Sivakanth Gopi, Pankaj Gulhane, Janardhan Kulkarni, Judy Hanwen Shen
Differentially Private Set Union. J. Priv. Confidentiality 11(3) (2021) - [c49]Xiangyu Guo, Janardhan Kulkarni, Shi Li, Jiayi Xian:
Consistent k-Median: Simpler, Better and Robust. AISTATS 2021: 1135-1143 - [c48]Mark Bun, Marek Eliás, Janardhan Kulkarni:
Differentially Private Correlation Clustering. ICML 2021: 1136-1146 - [c47]Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni:
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting. ICML 2021: 8227-8237 - [c46]Janardhan Kulkarni, Yin Tat Lee, Daogao Liu:
Private Non-smooth ERM and SCO in Subquadratic Steps. NeurIPS 2021: 4053-4064 - [c45]Kunho Kim, Sivakanth Gopi, Janardhan Kulkarni, Sergey Yekhanin:
Differentially Private n-gram Extraction. NeurIPS 2021: 5102-5111 - [c44]Zhiqi Bu, Sivakanth Gopi, Janardhan Kulkarni, Yin Tat Lee, Judy Hanwen Shen, Uthaipon Tantipongpipat:
Fast and Memory Efficient Differentially Private-SGD via JL Projections. NeurIPS 2021: 19680-19691 - [c43]Sami Davies, Janardhan Kulkarni, Thomas Rothvoss, Jakub Tarnawski, Yihao Zhang:
Scheduling with Communication Delays via LP Hierarchies and Clustering II: Weighted Completion Times on Related Machines. SODA 2021: 2958-2977 - [c42]Janardhan Kulkarni, Stefan Schmid
Scheduling Opportunistic Links in Two-Tiered Reconfigurable Datacenters. SPAA 2021: 318-327 - [i34]Zhiqi Bu, Sivakanth Gopi, Janardhan Kulkarni, Yin Tat Lee, Judy Hanwen Shen, Uthaipon Tantipongpipat:
Fast and Memory Efficient Differentially Private-SGD via JL Projections. CoRR abs/2102.03013 (2021) - [i33]Mark Bun, Marek Eliás, Janardhan Kulkarni:
Differentially Private Correlation Clustering. CoRR abs/2102.08885 (2021) - [i32]Janardhan Kulkarni, Yin Tat Lee, Daogao Liu:
Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps. CoRR abs/2103.15352 (2021) - [i31]Sami Davies, Janardhan Kulkarni, Thomas Rothvoss, Sai Sandeep, Jakub Tarnawski, Yihao Zhang:
On the Hardness of Scheduling With Non-Uniform Communication Delays. CoRR abs/2105.00111 (2021) - [i30]Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni:
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting. CoRR abs/2106.09680 (2021) - [i29]Kunho Kim, Sivakanth Gopi, Janardhan Kulkarni, Sergey Yekhanin:
Differentially Private n-gram Extraction. CoRR abs/2108.02831 (2021) - [i28]Jayashree Mohan, Amar Phanishayee, Janardhan Kulkarni, Vijay Chidambaram:
Synergy: Resource Sensitive DNN Scheduling in Multi-Tenant Clusters. CoRR abs/2110.06073 (2021) - [i27]Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A. Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, Huishuai Zhang:
Differentially Private Fine-tuning of Language Models. CoRR abs/2110.06500 (2021) - [i26]Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski:
Online Edge Coloring via Tree Recurrences and Correlation Decay. CoRR abs/2111.00721 (2021) - 2020
- [j5]Sayan Bhattacharya, Elias Koutsoupias, Janardhan Kulkarni, Stefano Leonardi, Tim Roughgarden, Xiaoming Xu:
Prior-free multi-unit auctions with ordered bidders. Theor. Comput. Sci. 846: 160-171 (2020) - [c41]Xiangyu Guo, Janardhan Kulkarni, Shi Li, Jiayi Xian:
On the Facility Location Problem in Online and Dynamic Models. APPROX-RANDOM 2020: 42:1-42:23 - [c40]Sivakanth Gopi, Gautam Kamath, Janardhan Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang:
Locally Private Hypothesis Selection. COLT 2020: 1785-1816 - [c39]Sami Davies, Janardhan Kulkarni, Thomas Rothvoss, Jakub Tarnawski, Yihao Zhang:
Scheduling with Communication Delays via LP Hierarchies and Clustering. FOCS 2020: 822-833 - [c38]Sivakanth Gopi, Pankaj Gulhane, Janardhan Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin:
Differentially Private Set Union. ICML 2020: 3627-3636 - [c37]Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni, Zhiwei Steven Wu:
Privately Learning Markov Random Fields. ICML 2020: 11129-11140 - [c36]Marek Eliás, Michael Kapralov
Differentially Private Release of Synthetic Graphs. SODA 2020: 560-578 - [c35]Laxman Dhulipala, David Durfee, Janardhan Kulkarni, Richard Peng, Saurabh Sawlani, Xiaorui Sun:
Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds. SODA 2020: 1300-1319 - [c34]Sayan Bhattacharya, Janardhan Kulkarni:
An Improved Algorithm for Incremental Cycle Detection and Topological Ordering in Sparse Graphs. SODA 2020: 2509-2521 - [c33]Janardhan Kulkarni, Shi Li, Jakub Tarnawski, Minwei Ye:
Hierarchy-Based Algorithms for Minimizing Makespan under Precedence and Communication Constraints. SODA 2020: 2770-2789 - [i25]Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni, Zhiwei Steven Wu:
Privately Learning Markov Random Fields. CoRR abs/2002.09463 (2020) - [i24]Sivakanth Gopi, Gautam Kamath, Janardhan Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang:
Locally Private Hypothesis Selection. CoRR abs/2002.09465 (2020) - [i23]Sivakanth Gopi, Pankaj Gulhane, Janardhan Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin:
Differentially Private Set Union. CoRR abs/2002.09745 (2020) - [i22]Xiangyu Guo, Janardhan Kulkarni, Shi Li, Jiayi Xian:
The Power of Recourse: Better Algorithms for Facility Location in Online and Dynamic Models. CoRR abs/2002.10658 (2020) - [i21]Sami Davies, Janardhan Kulkarni, Thomas Rothvoss, Jakub Tarnawski, Yihao Zhang:
Scheduling with Communication Delays via LP Hierarchies and Clustering. CoRR abs/2004.09682 (2020) - [i20]Janardhan Kulkarni, Shi Li, Jakub Tarnawski, Minwei Ye:
Hierarchy-Based Algorithms for Minimizing Makespan under Precedence and Communication Constraints. CoRR abs/2004.13891 (2020) - [i19]Xiangyu Guo, Janardhan Kulkarni, Shi Li, Jiayi Xian:
Consistent k-Median: Simpler, Better and Robust. CoRR abs/2008.06101 (2020) - [i18]Janardhan Kulkarni, Stefan Schmid, Pawel Schmidt:
Scheduling Opportunistic Links in Two-Tiered Reconfigurable Datacenters. CoRR abs/2010.07920 (2020)
2010 – 2019
- 2019
- [j4]Kyle Fox, Sungjin Im, Janardhan Kulkarni, Benjamin Moseley:
Non-clairvoyantly Scheduling to Minimize Convex Functions. Algorithmica 81(9): 3746-3764 (2019) - [j3]Sungjin Im, Nathaniel Kell, Janardhan Kulkarni, Debmalya Panigrahi:
Tight Bounds for Online Vector Scheduling. SIAM J. Comput. 48(1): 93-121 (2019) - [c32]Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Zhiwei Steven Wu:
Locally Private Gaussian Estimation. NeurIPS 2019: 2980-2989 - [c31]Joshua Allen, Bolin Ding, Janardhan Kulkarni, Harsha Nori, Olga Ohrimenko
An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors. NeurIPS 2019: 13635-13646 - [c30]Shashwat Garg, Janardhan Kulkarni, Shi Li:
Lift and Project Algorithms for Precedence Constrained Scheduling to Minimize Completion Time. SODA 2019: 1570-1584 - [c29]Uriel Feige, Janardhan Kulkarni, Shi Li:
A Polynomial Time Constant Approximation For Minimizing Total Weighted Flow-time. SODA 2019: 1585-1595 - [c28]Sayan Bhattacharya, Janardhan Kulkarni:
Deterministically Maintaining a (2 + ∊)-Approximate Minimum Vertex Cover in O(1/∊2) Amortized Update Time. SODA 2019: 1872-1885 - [i17]David Durfee, Laxman Dhulipala, Janardhan Kulkarni, Richard Peng, Saurabh Sawlani, Xiaorui Sun:
Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds. CoRR abs/1908.01956 (2019) - [i16]Haotian Jiang, Janardhan Kulkarni, Sahil Singla:
Online Geometric Discrepancy for Stochastic Arrivals with Applications to Envy Minimization. CoRR abs/1910.01073 (2019) - [i15]Sayan Bhattacharya, Fabrizio Grandoni, Janardhan Kulkarni, Quanquan C. Liu, Shay Solomon:
Fully Dynamic (Δ+1)-Coloring in Constant Update Time. CoRR abs/1910.02063 (2019) - 2018
- [j2]Sungjin Im, Janardhan Kulkarni, Kamesh Munagala:
Competitive Algorithms from Competitive Equilibria: Non-Clairvoyant Scheduling under Polyhedral Constraints. J. ACM 65(1): 3:1-3:33 (2018) - [c27]Janardhan Kulkarni, Shi Li:
Flow-time Optimization for Concurrent Open-Shop and Precedence Constrained Scheduling Models. APPROX-RANDOM 2018: 16:1-16:21 - [c26]Nikhil R. Devanur, Janardhan Kulkarni:
A Unified Rounding Algorithm For Unrelated Machines Scheduling Problems. SPAA 2018: 283-290 - [i14]Sayan Bhattacharya, Janardhan Kulkarni:
Deterministically Maintaining a (2+ε)-Approximate Minimum Vertex Cover in O(1/ε2) Amortized Update Time. CoRR abs/1805.03498 (2018) - [i13]Joshua Allen, Bolin Ding, Janardhan Kulkarni, Harsha Nori, Olga Ohrimenko, Sergey Yekhanin:
An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors. CoRR abs/1807.00736 (2018) - [i12]Janardhan Kulkarni, Shi Li:
Flow-time Optimization For Concurrent Open-Shop and Precedence Constrained Scheduling Models. CoRR abs/1807.02553 (2018) - [i11]Uriel Feige, Janardhan Kulkarni, Shi Li:
A Polynomial Time Constant Approximation For Minimizing Total Weighted Flow-time. CoRR abs/1807.09885 (2018) - [i10]Sayan Bhattacharya, Janardhan Kulkarni:
An Improved Algorithm for Incremental Cycle Detection and Topological Ordering in Sparse Graphs. CoRR abs/1810.03491 (2018) - [i9]Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Zhiwei Steven Wu:
Locally Private Gaussian Estimation. CoRR abs/1811.08382 (2018) - 2017
- [c25]Janardhan Kulkarni, Euiwoong Lee
Minimum Birkhoff-von Neumann Decomposition. IPCO 2017: 343-354 - [c24]Bolin Ding, Janardhan Kulkarni, Sergey Yekhanin:
Collecting Telemetry Data Privately. NIPS 2017: 3571-3580 - [c23]Shuchi Chawla, Nikhil R. Devanur, Janardhan Kulkarni, Rad Niazadeh
Truth and Regret in Online Scheduling. EC 2017: 423-440 - [i8]Shuchi Chawla, Nikhil R. Devanur, Janardhan Kulkarni, Rad Niazadeh:
Truth and Regret in Online Scheduling. CoRR abs/1703.00484 (2017) - [i7]Bolin Ding, Janardhan Kulkarni, Sergey Yekhanin:
Collecting Telemetry Data Privately. CoRR abs/1712.01524 (2017) - 2016
- [c22]Sungjin Im, Janardhan Kulkarni, Benjamin Moseley, Kamesh Munagala:
A Competitive Flow Time Algorithm for Heterogeneous Clusters Under Polytope Constraints. APPROX-RANDOM 2016: 10:1-10:15 - [c21]Sungjin Im, Janardhan Kulkarni, Kamesh Munagala:
Competitive Analysis of Constrained Queueing Systems. ICALP 2016: 143:1-143:13 - [c20]Robert Grandl, Srikanth Kandula, Sriram Rao, Aditya Akella, Janardhan Kulkarni:
GRAPHENE: Packing and Dependency-Aware Scheduling for Data-Parallel Clusters. OSDI 2016: 81-97 - [c19]Sangeetha Abdu Jyothi
Morpheus: Towards Automated SLOs for Enterprise Clusters. OSDI 2016: 117-134 - [c18]Monia Ghobadi, Ratul Mahajan, Amar Phanishayee, Nikhil R. Devanur, Janardhan Kulkarni, Gireeja Ranade, Pierre-Alexandre Blanche, Houman Rastegarfar, Madeleine Glick, Daniel C. Kilper
ProjecToR: Agile Reconfigurable Data Center Interconnect. SIGCOMM 2016: 216-229 - [c17]Sungjin Im, Janardhan Kulkarni:
Fair Online Scheduling for Selfish Jobs on Heterogeneous Machines. SPAA 2016: 185-194 - [i6]Robert Grandl, Srikanth Kandula, Sriram Rao, Aditya Akella, Janardhan Kulkarni:
Do the Hard Stuff First: Scheduling Dependent Computations in Data-Analytics Clusters. CoRR abs/1604.07371 (2016) - 2015
- [j1]Janardhan Kulkarni, Vahab S. Mirrokni:
Dynamic Coordination Mechanisms: [Extended Abstract]. SIGMETRICS Perform. Evaluation Rev. 43(3): 77 (2015) - [c16]Sungjin Im, Janardhan Kulkarni, Kamesh Munagala
Competitive Flow Time Algorithms for Polyhedral Scheduling. FOCS 2015: 506-524 - [c15]Sungjin Im, Nathaniel Kell, Janardhan Kulkarni, Debmalya Panigrahi:
Tight Bounds for Online Vector Scheduling. FOCS 2015: 525-544 - [c14]Janardhan Kulkarni, Vahab S. Mirrokni:
Robust Price of Anarchy Bounds via LP and Fenchel Duality. SODA 2015: 1030-1049 - [c13]Sungjin Im, Janardhan Kulkarni, Benjamin Moseley:
Temporal Fairness of Round Robin: Competitive Analysis for Lk-norms of Flow Time. SPAA 2015: 155-160 - [c12]Nikhil Bansal, Janardhan Kulkarni:
Minimizing Flow-Time on Unrelated Machines. STOC 2015: 851-860 - 2014
- [c11]Sungjin Im, Janardhan Kulkarni, Kamesh Munagala, Kirk Pruhs:
SelfishMigrate: A Scalable Algorithm for Non-clairvoyantly Scheduling Heterogeneous Processors. FOCS 2014: 531-540 - [c10]Sayan Bhattacharya, Janardhan Kulkarni, Vahab S. Mirrokni:
Coordination Mechanisms for Selfish Routing over Time on a Tree. ICALP (1) 2014: 186-197 - [c9]Sayan Bhattacharya, Sungjin Im, Janardhan Kulkarni, Kamesh Munagala:
Coordination mechanisms from (almost) all scheduling policies. ITCS 2014: 121-134 - [c8]Sungjin Im, Janardhan Kulkarni, Kamesh Munagala:
Competitive algorithms from competitive equilibria: non-clairvoyant scheduling under polyhedral constraints. STOC 2014: 313-322 - [i5]Nikhil Bansal, Janardhan Kulkarni:
Minimizing Flow-Time on Unrelated Machines. CoRR abs/1401.7284 (2014) - [i4]Sungjin Im, Janardhan Kulkarni, Kamesh Munagala:
Competitive Algorithms from Competitive Equilibria: Non-Clairvoyant Scheduling under Polyhedral Constraints. CoRR abs/1404.1097 (2014) - [i3]Sungjin Im, Janardhan Kulkarni, Kamesh Munagala, Kirk Pruhs:
SELFISHMIGRATE: A Scalable Algorithm for Non-clairvoyantly Scheduling Heterogeneous Processors. CoRR abs/1404.1943 (2014) - [i2]Sungjin Im, Nathaniel Kell, Janardhan Kulkarni, Debmalya Panigrahi:
Tight Bounds for Online Vector Scheduling. CoRR abs/1411.3887 (2014) - 2013
- [c7]Kyle Fox, Sungjin Im, Janardhan Kulkarni, Benjamin Moseley:
Online Non-clairvoyant Scheduling to Simultaneously Minimize All Convex Functions. APPROX-RANDOM 2013: 142-157 - [c6]Sayan Bhattacharya, Elias Koutsoupias, Janardhan Kulkarni, Stefano Leonardi, Tim Roughgarden, Xiaoming Xu:
Near-optimal multi-unit auctions with ordered bidders. EC 2013: 91-102 - 2012
- [c5]Janardhan Kulkarni, Kamesh Munagala:
Algorithms for Cost-Aware Scheduling. WAOA 2012: 201-214 - [i1]Sayan Bhattacharya, Janardhan Kulkarni, Xiaoming Xu:
Constant-Competitive Prior-Free Auction with Ordered Bidders. CoRR abs/1212.3079 (2012) - 2011
- [c4]Sayan Bhattacharya, Janardhan Kulkarni, Kamesh Munagala, Xiaoming Xu:
On Allocations with Negative Externalities. WINE 2011: 25-36 - 2010
- [c3]Pradeesha Ashok, Sathish Govindarajan, Janardhan Kulkarni:
Small strong epsilon nets. CCCG 2010: 155-158 - [c2]Janardhan Kulkarni, Sathish Govindarajan:
New epsilon-net constructions. CCCG 2010: 159-162
2000 – 2009
- 2008
- [c1]Deepak D'Souza, Raveendra Holla, Janardhan Kulkarni, Raghavendra K. Ramesh, Barbara Sprick:
On the Decidability of Model-Checking Information Flow Properties. ICISS 2008: 26-40
Coauthor Index
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