Janardhan Kulkarni

research

∙ 09/21/2023

Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation

We study the problem of in-context learning (ICL) with large language mo...

0 Xinyu Tang, et al. ∙

research

∙ 08/02/2023

Optimal Online Discrepancy Minimization

We prove that there exists an online algorithm that for any sequence of ...

0 Janardhan Kulkarni, et al. ∙

research

∙ 05/24/2023

Differentially Private Synthetic Data via Foundation Model APIs 1: Images

Generating differentially private (DP) synthetic data that closely resem...

0 Zinan Lin, et al. ∙

research

∙ 05/23/2023

Selective Pre-training for Private Fine-tuning

Suppose we want to train text prediction models in email clients or word...

0 Da Yu, et al. ∙

research

∙ 07/01/2022

When Does Differentially Private Learning Not Suffer in High Dimensions?

Large pretrained models can be privately fine-tuned to achieve performan...

0 Xuechen Li, et al. ∙

research

∙ 06/06/2022

Per-Instance Privacy Accounting for Differentially Private Stochastic Gradient Descent

Differentially private stochastic gradient descent (DP-SGD) is the workh...

12 Da Yu, et al. ∙

research

∙ 06/03/2022

Differentially Private Model Compression

Recent papers have shown that large pre-trained language models (LLMs) s...

0 Fatemehsadat Mireshghallah, et al. ∙

research

∙ 11/01/2021

Online Edge Coloring via Tree Recurrences and Correlation Decay

We give an online algorithm that with high probability computes a (e/e-1...

0 Janardhan Kulkarni, et al. ∙

research

∙ 10/13/2021

Differentially Private Fine-tuning of Language Models

We give simpler, sparser, and faster algorithms for differentially priva...

0 Da Yu, et al. ∙

research

∙ 10/12/2021

Synergy: Resource Sensitive DNN Scheduling in Multi-Tenant Clusters

Training Deep Neural Networks (DNNs) is a widely popular workload in bot...

0 Jayashree Mohan, et al. ∙

research

∙ 08/05/2021

Differentially Private n-gram Extraction

We revisit the problem of n-gram extraction in the differential privacy ...

0 Kunho Kim, et al. ∙

research

∙ 06/17/2021

Accuracy, Interpretability, and Differential Privacy via Explainable Boosting

We show that adding differential privacy to Explainable Boosting Machine...

0 Harsha Nori, et al. ∙

research

∙ 04/30/2021

On the Hardness of Scheduling With Non-Uniform Communication Delays

In the scheduling with non-uniform communication delay problem, the inpu...

0 Sami Davies, et al. ∙

research

∙ 03/29/2021

Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps

We study the differentially private Empirical Risk Minimization (ERM) an...

0 Janardhan Kulkarni, et al. ∙

research

∙ 02/17/2021

Differentially Private Correlation Clustering

Correlation clustering is a widely used technique in unsupervised machin...

0 Mark Bun, et al. ∙

research

∙ 02/05/2021

Fast and Memory Efficient Differentially Private-SGD via JL Projections

Differentially Private-SGD (DP-SGD) of Abadi et al. (2016) and its varia...

0 Zhiqi Bu, et al. ∙

research

∙ 10/15/2020

Scheduling Opportunistic Links in Two-Tiered Reconfigurable Datacenters

Reconfigurable optical topologies are emerging as a promising technology...

0 Janardhan Kulkarni, et al. ∙

research

∙ 08/13/2020

Consistent k-Median: Simpler, Better and Robust

In this paper we introduce and study the online consistent k-clustering ...

0 Xiangyu Guo, et al. ∙

research

∙ 04/28/2020

Hierarchy-Based Algorithms for Minimizing Makespan under Precedence and Communication Constraints

We consider the classic problem of scheduling jobs with precedence const...

0 Janardhan Kulkarni, et al. ∙

research

∙ 04/21/2020

Scheduling with Communication Delays via LP Hierarchies and Clustering

We consider the classic problem of scheduling jobs with precedence const...

0 Sami Davies, et al. ∙

research

∙ 02/25/2020

The Power of Recourse: Better Algorithms for Facility Location in Online and Dynamic Models

In this paper we study the facility location problem in the online with ...

0 Xiangyu Guo, et al. ∙

research

∙ 02/22/2020

Differentially Private Set Union

We study the basic operation of set union in the global model of differe...

0 Sivakanth Gopi, et al. ∙

research

∙ 02/21/2020

Locally Private Hypothesis Selection

We initiate the study of hypothesis selection under local differential p...

0 Sivakanth Gopi, et al. ∙

research

∙ 02/21/2020

Privately Learning Markov Random Fields

We consider the problem of learning Markov Random Fields (including the ...

0 Huanyu Zhang, et al. ∙

research

∙ 10/04/2019

Fully Dynamic (Δ+1)-Coloring in Constant Update Time

The problem of (vertex) (Δ+1)-coloring a graph of maximum degree Δ has b...

0 Sayan Bhattacharya, et al. ∙

research

∙ 10/02/2019

Online Geometric Discrepancy for Stochastic Arrivals with Applications to Envy Minimization

Consider a unit interval [0,1] in which n points arrive one-by-one indep...

0 Haotian Jiang, et al. ∙

research

∙ 08/06/2019

Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds

In this paper we study the problem of dynamically maintaining graph prop...

0 David Durfee, et al. ∙

research

∙ 11/20/2018

Locally Private Gaussian Estimation

We study a basic private estimation problem: each of n users draws a sin...

0 Matthew Joseph, et al. ∙

research

∙ 10/08/2018

An Improved Algorithm for Incremental Cycle Detection and Topological Ordering in Sparse Graphs

We consider the problem of incremental cycle detection and topological o...

0 Sayan Bhattacharya, et al. ∙

research

∙ 07/25/2018

A Polynomial Time Constant Approximation For Minimizing Total Weighted Flow-time

We consider the classic scheduling problem of minimizing the total weigh...

0 Uriel Feige, et al. ∙

research

∙ 07/06/2018

Flow-time Optimization For Concurrent Open-Shop and Precedence Constrained Scheduling Models

Scheduling a set of jobs over a collection of machines is a fundamental ...

0 Janardhan Kulkarni, et al. ∙

research

∙ 07/02/2018

An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors

Differential privacy has emerged as the main definition for private data...

0 Joshua Allen, et al. ∙

research

∙ 05/09/2018

Deterministically Maintaining a (2+ε)-Approximate Minimum Vertex Cover in O(1/ε^2) Amortized Update Time

We consider the problem of maintaining an (approximately) minimum vertex...

0 Sayan Bhattacharya, et al. ∙

research

∙ 12/05/2017

Collecting Telemetry Data Privately

The collection and analysis of telemetry data from users' devices is rou...

0 Bolin Ding, et al. ∙

research

∙ 04/25/2016

Do the Hard Stuff First: Scheduling Dependent Computations in Data-Analytics Clusters

We present a scheduler that improves cluster utilization and job complet...

0 Robert Grandl, et al. ∙

Janardhan Kulkarni

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