Kunal Talwar
I am a Theoretical Computer Scientist, working in the areas of Differential Privacy, Machine Learning, Algorithms, and Data Structures. I am a Research Scientist at Apple. I got my PhD from UC Berkeley in 2004 and was at Microsoft Research 2004-2014, and at Google Brain 2015-2024.
Former Interns
ios全局伕理软件
Jingcheng Liu
ios全局伕理软件
苹果手机全局伕理软件下载
Ravishankar Krishnaswamy
Aditya Bhaskara
Moritz Hardt
Anna Blasiak
Kamalika Chaudhuri
Michael Dinitz
Recent and Selected Papers
Differential Privacy
Private Stochastic Convex Optimization: Optimal Rates in Linear Time
(with Vitaly Feldman and Tomer Koren)
Nov 2024 [pdf]
Private Stochastic Convex Optimization with Optimal Rates
(with Raef Bassily, Vitaly Feldman and Abhradeep Thakurta)
NeurIPS 2024 [arxiv]
东北网2021年06月01日新闻汇总:苹果iPad上市两月销量已达200万台 2021-06-01 14:05 [568][ 东北网黑龙江 ] 哈尔滨市一户居民家中起火 七旬老太被浓烟熏死 2021-06-01 14:04
(with Jingcheng Liu)
STOC 2024 [arxiv]
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity
(with Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan and Abhradeep Thakurta)
SODA 2024 [苹果手机全局伕理软件下载]
Privacy Amplification by Iteration
(with Vitaly Feldman, Ilya Mironov, and Abhradeep Thakurta)
FOCS 2018 [arxiv]
Nearly Optimal Private LASSO
(with Abhradeep Thakurta and Li Zhang)
NIPS 2015 [pdf]
Efficient Algorithms for Privately Releasing Marginals via Convex Relaxations
(with Cynthia Dwork and Aleksandar Nikolov)
SoCG 2014 [arxiv]
Analyze Gauss: Optimal bounds for privacy-preserving Principal Component Analysis
(with Cynthia Dwork, Abhradeep Thakurta and Li Zhang)
STOC 2014 [pdf]
The Geometry of Differential Privacy: the sparse and approximate cases
(with Aleksandar Nikolov and Li Zhang)
STOC 2013 [arxiv]
On the Geometry of Differential Privacy
(with Moritz Hardt)
STOC 2010 [苹果手机全局伕理软件下载]
Mechanism Design via Differential Privacy
(with Frank McSherry)
FOCS 2007 [pdf]. Won PET Award 2009
The Price of Privacy and the Limits of LP Decoding
(with Cynthia Dwork and Frank McSherry)
STOC 2007 [pdf]
Machine Learning
Computational Separations between Sampling and Optimization
NeurIPS 2024 [arxiv]
Semi-Cyclic Stochastic Gradient Descent
(with Hubert Eichner, Tomer Koren, H. Brendan McMahan and Nathan Srebro)
ICML 2024 [arxiv]
Better Algorithms for Stochastic Bandits with Adversarial Corruptions
(with Anupam Gupta and Tomer Koren)
COLT 2024 [苹果手机全局伕理软件下载]
Adversarially Robust Generalization Requires More Data
(with Ludwig Schmidt, Shibani Santurkar, Dimitris, Tsipras, and Aleksaner Madry)
NIPS 2018 [arxiv]
2021年度北京市级行政机关和区政府绩效考评会议 - 千龙网· ...:2021年2月10日下午14:00在北京会议中心9号楼3层多功能厅召开2021年度市级行政机关和区政府绩效考评会议。
(with Alon Cohen, Avinatan Hassidim, Tomer Koren, Nevena Lazic, and Yishay Mansour)
ICML 2018 [arxiv]
Online Learning over a finite action set with limited switching
(with Jason Altschuler)
COLT 2018 [iphone手机如何上外网]
Scalable Private Learning with PATE
(with Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan and Úlfar Erlingsson)
ICLR 2018 [arxiv]
Learning Differentially Private Recurrent Language Models
(with Brendan McMahan, Daniel Ramage and Li Zhang)
ICLR 2018 [arxiv]
新闻中心_央广网 - cnr.cn:央广网新闻中心，聚焦国内外最新新闻热点，国内外最新时政社会新闻资讯;报道国内最&#