265 citations · 441 across the 39 of their papers we have counts for
9 papers · 1 filter
Online Edge Coloring via Tree Recurrences and Correlation Decay
Janardhan Kulkarni, Yang P. Liu, Ashwin Sah +2
We give an online algorithm that with high probability computes a edge coloring on a graph with maximum degree under online…
Differentially Private Fine-tuning of Language Models
Da Yu, Saurabh Naik, Arturs Backurs +9
We give simpler, sparser, and faster algorithms for differentially private fine-tuning of large-scale pre-trained language models, which achieve the state-of-the-art privacy versus…
Synergy: Resource Sensitive DNN Scheduling in Multi-Tenant Clusters
Jayashree Mohan, Amar Phanishayee, Janardhan Kulkarni +1
Training Deep Neural Networks (DNNs) is a widely popular workload in both enterprises and cloud data centers. Existing schedulers for DNN training consider GPU as the dominant reso…
Differentially Private n-gram Extraction
Kunho Kim, Sivakanth Gopi, Janardhan Kulkarni +1
We revisit the problem of -gram extraction in the differential privacy setting. In this problem, given a corpus of private text data, the goal is to release as many -grams as…
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting
Harsha Nori, Rich Caruana, Zhiqi Bu +2
We show that adding differential privacy to Explainable Boosting Machines (EBMs), a recent method for training interpretable ML models, yields state-of-the-art accuracy while prote…
On the Hardness of Scheduling With Non-Uniform Communication Delays
Sami Davies, Janardhan Kulkarni, Thomas Rothvoss +3
In the scheduling with non-uniform communication delay problem, the input is a set of jobs with precedence constraints. Associated with every precedence constraint between a pair o…
Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps
Janardhan Kulkarni, Yin Tat Lee, Daogao Liu
We study the differentially private Empirical Risk Minimization (ERM) and Stochastic Convex Optimization (SCO) problems for non-smooth convex functions. We get a (nearly) optimal b…
Differentially Private Correlation Clustering
Mark Bun, Marek Eliáš, Janardhan Kulkarni
Correlation clustering is a widely used technique in unsupervised machine learning. Motivated by applications where individual privacy is a concern, we initiate the study of differ…
Fast and Memory Efficient Differentially Private-SGD via JL Projections
Zhiqi Bu, Sivakanth Gopi, Janardhan Kulkarni +3
Differentially Private-SGD (DP-SGD) of Abadi et al. (2016) and its variations are the only known algorithms for private training of large scale neural networks. This algorithm requ…