Janardhan Kulkarni

Computer Science · Physical Sciences

16h-index1.8kcitations122works3.92yr avg
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Research Topics

Optimization and Search Problems(47), Privacy-Preserving Technologies in Data(30), Complexity and Algorithms in Graphs(28), Scheduling and Optimization Algorithms(27), Auction Theory and Applications(16)

Publications122 total

Simulating Environments with Reasoning Models for Agent Training
arXiv (Cornell University)·2025Open Access
Dyna-Mind: Learning to Simulate from Experience for Better AI Agents
arXiv (Cornell University)·2025Open Access
SAS: Simulated Attention Score
arXiv (Cornell University)·2025Open Access
Contextual Integrity in LLMs via Reasoning and Reinforcement Learning
arXiv (Cornell University)·2025Open Access
Towards Foundation Models for Mixed Integer Linear Programming
arXiv (Cornell University)·2024· 2 citedOpen Access
Differentially Private Fine-tuning of Language Models
Journal of Privacy and Confidentiality·2024· 33 citedOpen Access
Optimal Online Discrepancy Minimization
2024· 3 citedOpen Access
Workload-Aware Hardware Accelerator Mining for Distributed Deep Learning Training
arXiv (Cornell University)·2024Open Access
Online Edge Coloring via Tree Recurrences and Correlation Decay
SIAM Journal on Computing·2024· 1 cited
Differentially Private Training of Mixture of Experts Models
arXiv (Cornell University)·2024Open Access
TinyGSM: achieving >80% on GSM8k with small language models
arXiv (Cornell University)·2023· 1 citedOpen Access
Classification with Partially Private Features
arXiv (Cornell University)·2023Open Access
Privately Aligning Language Models with Reinforcement Learning
arXiv (Cornell University)·2023· 1 citedOpen Access
Assessing Privacy Risks in Language Models: A Case Study on Summarization Tasks
arXiv (Cornell University)·2023Open Access
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
arXiv (Cornell University)·2023· 8 citedOpen Access
Optimal Online Discrepancy Minimization
arXiv (Cornell University)·2023Open Access
Differentially Private Synthetic Data via Foundation Model APIs 1: Images
arXiv (Cornell University)·2023Open Access
Selective Pre-training for Private Fine-tuning
arXiv (Cornell University)·2023· 1 citedOpen Access
Assessing Privacy Risks in Language Models: A Case Study on Summarization Tasks
2023Open Access
Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping
arXiv (Cornell University)·2022· 4 citedOpen Access
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Frequent Co-authors

Sungjin Im(10), Kamesh Munagala(8), Sivakanth Gopi(7), Sergey Yekhanin(6), Gautam Kamath(5), Sayan Bhattacharya(5), Huseyin A. Inan(4), Jakub Tarnawski(4), Judy Hanwen Shen(4), Bolin Ding(3), Yin Tat Lee(3), Andre Manoel(3), Huishuai Zhang(3), Yin Tat Lee(3), Zhiwei Steven Wu(3), Harsha Nori(3), Zhiqi Bu(3), Nikhil R. Devanur(2), Sriram Rao(2), Saurabh Naik(2)