Janardhan Kulkarni
25 papers · 2016–2025 · 7 conferences · across top CS/AI conferences
Achievements
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🏃 Academic Marathon (9) 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (7) 🐝 Cross-Pollinator (8)
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(14)
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Unstoppable
(7)
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(6)
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(25)
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Conferences
NIPS (8)
ICLR (6)
ICML (4)
OSDI (3)
COLT (2)
AISTATS (1)
EMNLP (1)
Top co-authors
Research topics
Keywords
differential privacy
(12)
resource allocation
(3)
local differential privacy
(2)
lower bound
(2)
online algorithm
(2)
local privacy
(2)
large language model
(2)
neural network training
(1)
stochastic gradient descent
(1)
unsupervised learning
(1)
privacy-preserving learning
(1)
ising model
(1)
empirical risk minimization
(1)
hypothesis testing
(1)
mean estimation
(1)
text summarization
(1)
privacy attack
(1)
stochastic convex optimization
(1)
parameter learning
(1)
machine learning
(1)
Papers
Towards Foundation Models for Mixed Integer Linear Programming
ICLR 2025
DiscQuant: A Quantization Method for Neural Networks Inspired by Discrepancy Theory
COLT 2025
Differentially Private Synthetic Data via Foundation Model APIs 1: Images
ICLR 2024
Privately Aligning Language Models with Reinforcement Learning
ICLR 2024
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
ICLR 2024
Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping
ICLR 2023
Assessing Privacy Risks in Language Models: A Case Study on Summarization Tasks
EMNLP 2023
Looking Beyond GPUs for DNN Scheduling on Multi-Tenant Clusters
OSDI 2022
When Does Differentially Private Learning Not Suffer in High Dimensions?
NIPS 2022
Differentially Private Model Compression
NIPS 2022
Differentially Private Fine-tuning of Language Models
ICLR 2022
Differentially Private Correlation Clustering
ICML 2021
Consistent k-Median: Simpler, Better and Robust
AISTATS 2021
Fast and Memory Efficient Differentially Private-SGD via JL Projections
NIPS 2021
Differentially Private n-gram Extraction
NIPS 2021
Private Non-smooth ERM and SCO in Subquadratic Steps
NIPS 2021
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting
ICML 2021
Locally Private Hypothesis Selection
COLT 2020
Differentially Private Set Union
ICML 2020
Privately Learning Markov Random Fields
ICML 2020
An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors
NIPS 2019
Locally Private Gaussian Estimation
NIPS 2019
Collecting Telemetry Data Privately
NIPS 2017
GRAPHENE: Packing and Dependency-Aware Scheduling for Data-Parallel Clusters
OSDI 2016
Morpheus: Towards Automated SLOs for Enterprise Clusters
OSDI 2016