Janardhan Kulkarni

ORCID

Publication Activity (10 Years)

Years Active: 2008-2024
Publications (10 Years): 104

Top Venues

Publications

Janardhan Kulkarni, Victor Reis, Thomas Rothvoss
Optimal Online Discrepancy Minimization. STOC (2024)
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)
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)
Muhammad Adnan, Amar Phanishayee, Janardhan Kulkarni, Prashant J. Nair, Divya Mahajan
Workload-Aware Hardware Accelerator Mining for Distributed Deep Learning Training. CoRR (2024)
Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Harsha Nori, Sergey Yekhanin
Differentially Private Synthetic Data via Foundation Model APIs 1: Images. ICLR (2024)
Pierre Tholoniat, Huseyin A. Inan, Janardhan Kulkarni, Robert Sim
Differentially Private Training of Mixture of Experts Models. CoRR (2024)
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) (2024)
Fan Wu, Huseyin A. Inan, Arturs Backurs, Varun Chandrasekaran, Janardhan Kulkarni, Robert Sim
Privately Aligning Language Models with Reinforcement Learning. ICLR (2024)
Fan Wu, Huseyin A. Inan, Arturs Backurs, Varun Chandrasekaran, Janardhan Kulkarni, Robert Sim
Privately Aligning Language Models with Reinforcement Learning. CoRR (2023)
Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Harsha Nori, Sergey Yekhanin
Differentially Private Synthetic Data via Foundation Model APIs 1: Images. CoRR (2023)
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)
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 (2023)
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 (2023)
Janardhan Kulkarni, Victor Reis, Thomas Rothvoss
Optimal Online Discrepancy Minimization. CoRR (2023)
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 (2023)
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)
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. EMNLP (Findings) (2023)
Zeyu Shen, Anilesh K. Krishnaswamy, Janardhan Kulkarni, Kamesh Munagala
Classification with Partially Private Features. CoRR (2023)
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 (2023)
Sayan Bhattacharya, Fabrizio Grandoni, Janardhan Kulkarni, Quanquan C. Liu, Shay Solomon
(1) Update Time. ACM Trans. Algorithms 18 (2) (2022)
Da Yu, Gautam Kamath, Janardhan Kulkarni, Tie-Yan Liu, Jian Yin, Huishuai Zhang
Per-Instance Privacy Accounting for Differentially Private Stochastic Gradient Descent. CoRR (2022)
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)
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 (2022)
Jayashree Mohan, Amar Phanishayee, Janardhan Kulkarni, Vijay Chidambaram
Looking Beyond GPUs for DNN Scheduling on Multi-Tenant Clusters. OSDI (2022)
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)
Gautam Kamath, Sepehr Assadi, Anne Driemel, Janardhan Kulkarni
Introduction to the Special Issue on ACM-SIAM Symposium on Discrete Algorithms (SODA) 2020. ACM Trans. Algorithms 18 (4) (2022)
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 (2022)
Sami Davies, Janardhan Kulkarni, Thomas Rothvoss, Sai Sandeep, Jakub Tarnawski, Yihao Zhang
On the Hardness of Scheduling With Non-Uniform Communication Delays. SODA (2022)
Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski
Online edge coloring via tree recurrences and correlation decay. STOC (2022)
Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin A. Inan, Lukas Wutschitz, Janardhan Kulkarni
Differentially Private Model Compression. CoRR (2022)
Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin A. Inan, Lukas Wutschitz, Janardhan Kulkarni
Differentially Private Model Compression. NeurIPS (2022)
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 (2021)
Kunho Kim, Sivakanth Gopi, Janardhan Kulkarni, Sergey Yekhanin
Differentially Private n-gram Extraction. NeurIPS (2021)
Janardhan Kulkarni, Stefan Schmid, Pawel Schmidt
Scheduling Opportunistic Links in Two-Tiered Reconfigurable Datacenters. SPAA (2021)
Mark Bun, Marek Eliás, Janardhan Kulkarni
Differentially Private Correlation Clustering. CoRR (2021)
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)
Sami Davies, Janardhan Kulkarni, Thomas Rothvoss, Sai Sandeep, Jakub Tarnawski, Yihao Zhang
On the Hardness of Scheduling With Non-Uniform Communication Delays. CoRR (2021)
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)
Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting. ICML (2021)
Mark Bun, Marek Eliás, Janardhan Kulkarni
Differentially Private Correlation Clustering. ICML (2021)
Sivakanth Gopi, Pankaj Gulhane, Janardhan Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin
Differentially Private Set Union. J. Priv. Confidentiality 11 (3) (2021)
Janardhan Kulkarni, Yin Tat Lee, Daogao Liu
Private Non-smooth ERM and SCO in Subquadratic Steps. NeurIPS (2021)
Janardhan Kulkarni, Yin Tat Lee, Daogao Liu
Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps. CoRR (2021)
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 (2021)
Xiangyu Guo, Janardhan Kulkarni, Shi Li, Jiayi Xian
Consistent k-Median: Simpler, Better and Robust. AISTATS (2021)
Jayashree Mohan, Amar Phanishayee, Janardhan Kulkarni, Vijay Chidambaram
Synergy: Resource Sensitive DNN Scheduling in Multi-Tenant Clusters. CoRR (2021)
Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski
Online Edge Coloring via Tree Recurrences and Correlation Decay. CoRR (2021)
Kunho Kim, Sivakanth Gopi, Janardhan Kulkarni, Sergey Yekhanin
Differentially Private n-gram Extraction. CoRR (2021)
Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting. CoRR (2021)
Janardhan Kulkarni, Shi Li, Jakub Tarnawski, Minwei Ye
Hierarchy-Based Algorithms for Minimizing Makespan under Precedence and Communication Constraints. SODA (2020)
Xiangyu Guo, Janardhan Kulkarni, Shi Li, Jiayi Xian
The Power of Recourse: Better Algorithms for Facility Location in Online and Dynamic Models. CoRR (2020)
Sivakanth Gopi, Gautam Kamath, Janardhan Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang
Locally Private Hypothesis Selection. CoRR (2020)
Sivakanth Gopi, Pankaj Gulhane, Janardhan Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin
Differentially Private Set Union. ICML (2020)
Sami Davies, Janardhan Kulkarni, Thomas Rothvoss, Jakub Tarnawski, Yihao Zhang
Scheduling with Communication Delays via LP Hierarchies and Clustering. FOCS (2020)
Xiangyu Guo, Janardhan Kulkarni, Shi Li, Jiayi Xian
Consistent k-Median: Simpler, Better and Robust. CoRR (2020)
Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni, Zhiwei Steven Wu
Privately Learning Markov Random Fields. CoRR (2020)
Sivakanth Gopi, Pankaj Gulhane, Janardhan Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin
Differentially Private Set Union. CoRR (2020)
Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni, Zhiwei Steven Wu
Privately Learning Markov Random Fields. ICML (2020)
Sayan Bhattacharya, Janardhan Kulkarni
An Improved Algorithm for Incremental Cycle Detection and Topological Ordering in Sparse Graphs. SODA (2020)
Sayan Bhattacharya, Elias Koutsoupias, Janardhan Kulkarni, Stefano Leonardi, Tim Roughgarden, Xiaoming Xu
Prior-free multi-unit auctions with ordered bidders. Theor. Comput. Sci. 846 (2020)
Laxman Dhulipala, David Durfee, Janardhan Kulkarni, Richard Peng, Saurabh Sawlani, Xiaorui Sun
Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds. SODA (2020)
Janardhan Kulkarni, Shi Li, Jakub Tarnawski, Minwei Ye
Hierarchy-Based Algorithms for Minimizing Makespan under Precedence and Communication Constraints. CoRR (2020)
Sami Davies, Janardhan Kulkarni, Thomas Rothvoss, Jakub Tarnawski, Yihao Zhang
Scheduling with Communication Delays via LP Hierarchies and Clustering. CoRR (2020)
Marek Eliás, Michael Kapralov, Janardhan Kulkarni, Yin Tat Lee
Differentially Private Release of Synthetic Graphs. SODA (2020)
Sivakanth Gopi, Gautam Kamath, Janardhan Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang
Locally Private Hypothesis Selection. COLT (2020)
Xiangyu Guo, Janardhan Kulkarni, Shi Li, Jiayi Xian
On the Facility Location Problem in Online and Dynamic Models. APPROX-RANDOM (2020)
Janardhan Kulkarni, Stefan Schmid, Pawel Schmidt
Scheduling Opportunistic Links in Two-Tiered Reconfigurable Datacenters. CoRR (2020)
Kyle Fox, Sungjin Im, Janardhan Kulkarni, Benjamin Moseley
Non-clairvoyantly Scheduling to Minimize Convex Functions. Algorithmica 81 (9) (2019)
Joshua Allen, Bolin Ding, Janardhan Kulkarni, Harsha Nori, Olga Ohrimenko, Sergey Yekhanin
An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors. NeurIPS (2019)
Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Zhiwei Steven Wu
Locally Private Gaussian Estimation. NeurIPS (2019)
Shashwat Garg, Janardhan Kulkarni, Shi Li
Lift and Project Algorithms for Precedence Constrained Scheduling to Minimize Completion Time. SODA (2019)
Sungjin Im, Nathaniel Kell, Janardhan Kulkarni, Debmalya Panigrahi
Tight Bounds for Online Vector Scheduling. SIAM J. Comput. 48 (1) (2019)
Haotian Jiang, Janardhan Kulkarni, Sahil Singla
Online Geometric Discrepancy for Stochastic Arrivals with Applications to Envy Minimization. CoRR (2019)
David Durfee, Laxman Dhulipala, Janardhan Kulkarni, Richard Peng, Saurabh Sawlani, Xiaorui Sun
Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds. CoRR (2019)
Uriel Feige, Janardhan Kulkarni, Shi Li
A Polynomial Time Constant Approximation For Minimizing Total Weighted Flow-time. SODA (2019)
Sayan Bhattacharya, Janardhan Kulkarni
Deterministically Maintaining a (2 + ∊)-Approximate Minimum Vertex Cover in O(1/∊2) Amortized Update Time. SODA (2019)
Sayan Bhattacharya, Fabrizio Grandoni, Janardhan Kulkarni, Quanquan C. Liu, Shay Solomon
Fully Dynamic (Δ+1)-Coloring in Constant Update Time. CoRR (2019)
Sayan Bhattacharya, Janardhan Kulkarni
An Improved Algorithm for Incremental Cycle Detection and Topological Ordering in Sparse Graphs. CoRR (2018)
Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Zhiwei Steven Wu
Locally Private Gaussian Estimation. CoRR (2018)
Janardhan Kulkarni, Shi Li
Flow-time Optimization for Concurrent Open-Shop and Precedence Constrained Scheduling Models. APPROX-RANDOM (2018)
Uriel Feige, Janardhan Kulkarni, Shi Li
A Polynomial Time Constant Approximation For Minimizing Total Weighted Flow-time. CoRR (2018)
Sungjin Im, Janardhan Kulkarni, Kamesh Munagala
Competitive Algorithms from Competitive Equilibria: Non-Clairvoyant Scheduling under Polyhedral Constraints. J. ACM 65 (1) (2018)
Nikhil R. Devanur, Janardhan Kulkarni
A Unified Rounding Algorithm For Unrelated Machines Scheduling Problems. SPAA (2018)
Joshua Allen, Bolin Ding, Janardhan Kulkarni, Harsha Nori, Olga Ohrimenko, Sergey Yekhanin
An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors. CoRR (2018)
Sayan Bhattacharya, Janardhan Kulkarni
) Amortized Update Time. CoRR (2018)
Janardhan Kulkarni, Shi Li
Flow-time Optimization For Concurrent Open-Shop and Precedence Constrained Scheduling Models. CoRR (2018)
Bolin Ding, Janardhan Kulkarni, Sergey Yekhanin
Collecting Telemetry Data Privately. NIPS (2017)
Shuchi Chawla, Nikhil R. Devanur, Janardhan Kulkarni, Rad Niazadeh
Truth and Regret in Online Scheduling. CoRR (2017)
Janardhan Kulkarni, Euiwoong Lee, Mohit Singh
Minimum Birkhoff-von Neumann Decomposition. IPCO (2017)
Shuchi Chawla, Nikhil R. Devanur, Janardhan Kulkarni, Rad Niazadeh
Truth and Regret in Online Scheduling. EC (2017)
Bolin Ding, Janardhan Kulkarni, Sergey Yekhanin
Collecting Telemetry Data Privately. CoRR (2017)
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)
Sangeetha Abdu Jyothi, Carlo Curino, Ishai Menache, Shravan Matthur Narayanamurthy, Alexey Tumanov, Jonathan Yaniv, Ruslan Mavlyutov, Iñigo Goiri, Subru Krishnan, Janardhan Kulkarni, Sriram Rao
Morpheus: Towards Automated SLOs for Enterprise Clusters. OSDI (2016)
Sungjin Im, Janardhan Kulkarni
Fair Online Scheduling for Selfish Jobs on Heterogeneous Machines. SPAA (2016)
Robert Grandl, Srikanth Kandula, Sriram Rao, Aditya Akella, Janardhan Kulkarni
Do the Hard Stuff First: Scheduling Dependent Computations in Data-Analytics Clusters. CoRR (2016)
Sungjin Im, Janardhan Kulkarni, Kamesh Munagala
Competitive Analysis of Constrained Queueing Systems. ICALP (2016)
Robert Grandl, Srikanth Kandula, Sriram Rao, Aditya Akella, Janardhan Kulkarni
GRAPHENE: Packing and Dependency-Aware Scheduling for Data-Parallel Clusters. OSDI (2016)
Sungjin Im, Janardhan Kulkarni, Benjamin Moseley, Kamesh Munagala
A Competitive Flow Time Algorithm for Heterogeneous Clusters Under Polytope Constraints. APPROX-RANDOM (2016)
Sungjin Im, Janardhan Kulkarni, Benjamin Moseley
Temporal Fairness of Round Robin: Competitive Analysis for Lk-norms of Flow Time. SPAA (2015)
Janardhan Kulkarni, Vahab S. Mirrokni
Robust Price of Anarchy Bounds via LP and Fenchel Duality. SODA (2015)

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Publication Activity (10 Years)

Top Topics

Top Venues

Janardhan Kulkarni