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ORCIDKareem Amin 0002
Person information
- affiliation: Google Research, New York, NY, USA
- affiliation (former): University of Michigan, USA
- affiliation (Ph.D): University of Pennsylvania, Philadelphia, PA, USA
Other persons with the same name
- Kareem Amin 0001 — Technische Universität Kaiserslautern, Kaiserslautern, Germany (and 1 more)
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2020 – today
- 2024
[c23]Kareem Amin, Alex Bie, Weiwei Kong, Alexey Kurakin, Natalia Ponomareva, Umar Syed, Andreas Terzis, Sergei Vassilvitskii:
Private prediction for large-scale synthetic text generation. EMNLP (Findings) 2024: 7244-7262- [i13]Kareem Amin, Alex Bie, Weiwei Kong, Alexey Kurakin, Natalia Ponomareva, Umar Syed, Andreas Terzis, Sergei Vassilvitskii:
Private prediction for large-scale synthetic text generation. CoRR abs/2407.12108 (2024) - [i12]Kareem Amin, Alex Kulesza, Sergei Vassilvitskii:
Practical Considerations for Differential Privacy. CoRR abs/2408.07614 (2024) - 2023
- [c22]Kareem Amin, Matthew Joseph, Mónica Ribero, Sergei Vassilvitskii:
Easy Differentially Private Linear Regression. ICLR 2023 - [c21]Mikhail Khodak, Kareem Amin, Travis Dick, Sergei Vassilvitskii:
Learning-augmented private algorithms for multiple quantile release. ICML 2023: 16344-16376 - 2022
- [i11]Kareem Amin, Jennifer Gillenwater, Matthew Joseph, Alex Kulesza, Sergei Vassilvitskii:
Plume: Differential Privacy at Scale. CoRR abs/2201.11603 (2022) - [i10]Kareem Amin, Matthew Joseph, Mónica Ribero, Sergei Vassilvitskii:
Easy Differentially Private Linear Regression. CoRR abs/2208.07353 (2022) - [i9]Kareem Amin, Travis Dick, Mikhail Khodak, Sergei Vassilvitskii:
Private Algorithms with Private Predictions. CoRR abs/2210.11222 (2022) - 2021
- [c20]Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh:
Learning with User-Level Privacy. NeurIPS 2021: 12466-12479 - [c19]Kareem Amin, Giulia DeSalvo, Afshin Rostamizadeh:
Learning with Labeling Induced Abstentions. NeurIPS 2021: 12576-12586 - [i8]Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh:
Learning with User-Level Privacy. CoRR abs/2102.11845 (2021) - 2020
- [c18]Kareem Amin, Corinna Cortes, Giulia DeSalvo, Afshin Rostamizadeh:
Understanding the Effects of Batching in Online Active Learning. AISTATS 2020: 3482-3492 - [c17]Kareem Amin, Matthew Joseph, Jieming Mao:
Pan-Private Uniformity Testing. COLT 2020: 183-218
2010 – 2019
- 2019
- [c16]Kareem Amin, Alex Kulesza, Andres Muñoz Medina, Sergei Vassilvitskii:
Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy. ICML 2019: 263-271 - [c15]Kareem Amin, Travis Dick, Alex Kulesza, Andres Muñoz Medina, Sergei Vassilvitskii:
Differentially Private Covariance Estimation. NeurIPS 2019: 14190-14199 - [i7]Kareem Amin, Matthew Joseph, Jieming Mao:
Pan-Private Uniformity Testing. CoRR abs/1911.01452 (2019) - 2017
- [c14]Kareem Amin, Nan Jiang, Satinder Singh:
Repeated Inverse Reinforcement Learning. NIPS 2017: 1815-1824 - [i6]Kareem Amin, Nan Jiang, Satinder Singh:
Repeated Inverse Reinforcement Learning. CoRR abs/1705.05427 (2017) - 2016
- [c13]Sridhar Venkatesan, Massimiliano Albanese
, Kareem Amin, Sushil Jajodia, Mason Wright:
A moving target defense approach to mitigate DDoS attacks against proxy-based architectures. CNS 2016: 198-206 - [c12]Jacob D. Abernethy, Kareem Amin, Ruihao Zhu:
Threshold Bandits, With and Without Censored Feedback. NIPS 2016: 4889-4897 - [c11]Frank Cheng, Junming Liu, Kareem Amin, Michael P. Wellman:
Strategic Payment Routing in Financial Credit Networks. EC 2016: 721-738 - [c10]Kareem Amin, Michael P. Wellman, Satinder Singh:
Gradient Methods for Stackelberg Games. UAI 2016 - [i5]Kareem Amin, Satinder Singh:
Towards Resolving Unidentifiability in Inverse Reinforcement Learning. CoRR abs/1601.06569 (2016) - 2015
- [c9]Kareem Amin, Rachel Cummings, Lili Dworkin, Michael J. Kearns, Aaron Roth:
Online Learning and Profit Maximization from Revealed Preferences. AAAI 2015: 770-776 - [c8]Kareem Amin, Satyen Kale, Gerald Tesauro, Deepak S. Turaga:
Budgeted Prediction with Expert Advice. AAAI 2015: 2490-2496 - 2014
- [c7]Kareem Amin, Hoda Heidari, Michael J. Kearns:
Learning from Contagion (Without Timestamps). ICML 2014: 1845-1853 - [c6]Kareem Amin, Afshin Rostamizadeh, Umar Syed:
Repeated Contextual Auctions with Strategic Buyers. NIPS 2014: 622-630 - [i4]Kareem Amin, Rachel Cummings, Lili Dworkin, Michael J. Kearns, Aaron Roth:
Online Learning and Profit Maximization from Revealed Preferences. CoRR abs/1407.7294 (2014) - 2013
- [c5]Jacob D. Abernethy, Kareem Amin, Michael J. Kearns, Moez Draief:
Large-Scale Bandit Problems and KWIK Learning. ICML (1) 2013: 588-596 - [c4]Kareem Amin, Afshin Rostamizadeh, Umar Syed:
Learning Prices for Repeated Auctions with Strategic Buyers. NIPS 2013: 1169-1177 - [i3]Kareem Amin, Afshin Rostamizadeh, Umar Syed:
Learning Prices for Repeated Auctions with Strategic Buyers. CoRR abs/1311.6838 (2013) - 2012
- [c3]Kareem Amin, Michael J. Kearns, Peter B. Key, Anton Schwaighofer:
Budget Optimization for Sponsored Search: Censored Learning in MDPs. UAI 2012: 54-63 - [i2]Kareem Amin, Michael J. Kearns, Umar Syed:
Graphical Models for Bandit Problems. CoRR abs/1202.3782 (2012) - [i1]Kareem Amin, Michael J. Kearns, Peter B. Key, Anton Schwaighofer:
Budget Optimization for Sponsored Search: Censored Learning in MDPs. CoRR abs/1210.4847 (2012) - 2011
- [c2]Kareem Amin, Michael J. Kearns, Umar Syed:
Graphical Models for Bandit Problems. UAI 2011: 1-10 - [c1]Kareem Amin, Michael J. Kearns, Umar Syed:
Bandits, Query Learning, and the Haystack Dimension. COLT 2011: 87-106
Coauthor Index
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last updated on 2024-11-15 20:37 CET by the dblp team
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