Kareem Amin

Publication Activity (10 Years)

Years Active: 2011-2022
Publications (10 Years): 24

Top Topics

Profit Maximization

Bayesian Nonparametric

Inverse Reinforcement Learning

Top Venues

ICCBR Workshops

Publications

Kareem Amin, Jennifer Gillenwater, Matthew Joseph, Alex Kulesza, Sergei Vassilvitskii
Plume: Differential Privacy at Scale. CoRR (2022)
Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh
Learning with User-Level Privacy. CoRR (2021)
Kareem Amin, Stelios Kapetanakis, Nikolaos Polatidis, Klaus-Dieter Althoff, Andreas Dengel
DeepKAF: A Heterogeneous CBR & Deep Learning Approach for NLP Prototyping. INISTA (2020)
Kareem Amin, Matthew Joseph, Jieming Mao
Pan-Private Uniformity Testing. COLT (2020)
Kareem Amin, Corinna Cortes, Giulia DeSalvo, Afshin Rostamizadeh
Understanding the Effects of Batching in Online Active Learning. AISTATS (2020)
Kareem Amin, George Lancaster, Stelios Kapetanakis, Klaus-Dieter Althoff, Andreas Dengel, Miltos Petridis
Advanced Similarity Measures Using Word Embeddings and Siamese Networks in CBR. IntelliSys (2) (2019)
Kareem Amin, Travis Dick, Alex Kulesza, Andres Muñoz Medina, Sergei Vassilvitskii
Differentially Private Covariance Estimation. NeurIPS (2019)
Merton Lansley, Nikolaos Polatidis, Stelios Kapetanakis, Kareem Amin, George Samakovitis, Miltos Petridis
Seen the villains: Detecting Social Engineering Attacks using Case-based Reasoning and Deep Learning. ICCBR Workshops (2019)
Kareem Amin, Alex Kulesza, Andres Muñoz Medina, Sergei Vassilvitskii
Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy. ICML (2019)
Kareem Amin, Stelios Kapetanakis, Nikolaos Polatidis, Klaus-Dieter Althoff, Andreas Dengel, Miltos Petridis
Building Knowledge Intensive Architectures for Heterogeneous NLP Workflows. SGAI Conf. (2019)
Kareem Amin
Cases without Borders: Automating Knowledge Acquisition Approach using Deep Autoencoders and Siamese Networks in Case-Based Reasoning. ICTAI (2019)
Kareem Amin, Matthew Joseph, Jieming Mao
Pan-Private Uniformity Testing. CoRR (2019)
Kyle Martin, Stelios Kapetanakis, Anjana Wijekoon, Kareem Amin, Stewart Massie
Workshop preface. ICCBR Workshops (2019)
Kareem Amin, Stelios Kapetanakis, Klaus-Dieter Althoff, Andreas Dengel, Miltos Petridis
Dynamic Process Workflow Routing Using Deep Learning. SGAI Conf. (2018)
Kareem Amin, Stelios Kapetanakis, Klaus-Dieter Althoff, Andreas Dengel, Miltos Petridis
Answering with Cases: A CBR Approach to Deep Learning. ICCBR (2018)
Kareem Amin, Nan Jiang, Satinder P. Singh
Repeated Inverse Reinforcement Learning. NIPS (2017)
Kareem Amin
Building an Integrated CBR-Big Data Oriented Architecture for Case-Based Reasoning Systems. ICCBR (Workshops) (2017)
Kareem Amin, Nan Jiang, Satinder P. Singh
Repeated Inverse Reinforcement Learning. CoRR (2017)
Frank Cheng, Junming Liu, Kareem Amin, Michael P. Wellman
Strategic Payment Routing in Financial Credit Networks. EC (2016)
Kareem Amin
Building an integrated CBR-Big Data Architecture based on SEASALT. LWDA (2016)
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)
Kareem Amin, Michael P. Wellman, Satinder P. Singh
Gradient Methods for Stackelberg Games. UAI (2016)
Jacob D. Abernethy, Kareem Amin, Ruihao Zhu
Threshold Bandits, With and Without Censored Feedback. NIPS (2016)
Kareem Amin, Satinder P. Singh
Towards Resolving Unidentifiability in Inverse Reinforcement Learning. CoRR (2016)
Kareem Amin, Rachel Cummings, Lili Dworkin, Michael J. Kearns, Aaron Roth
Online Learning and Profit Maximization from Revealed Preferences. AAAI (2015)
Kareem Amin, Satyen Kale, Gerald Tesauro, Deepak S. Turaga
Budgeted Prediction with Expert Advice. AAAI (2015)
Kareem Amin, Afshin Rostamizadeh, Umar Syed
Repeated Contextual Auctions with Strategic Buyers. NIPS (2014)
Kareem Amin, Rachel Cummings, Lili Dworkin, Michael J. Kearns, Aaron Roth
Online Learning and Profit Maximization from Revealed Preferences. CoRR (2014)
Kareem Amin, Hoda Heidari, Michael J. Kearns
Learning from Contagion (Without Timestamps). ICML (2014)
Kareem Amin, Afshin Rostamizadeh, Umar Syed
Learning Prices for Repeated Auctions with Strategic Buyers. CoRR (2013)
Kareem Amin, Afshin Rostamizadeh, Umar Syed
Learning Prices for Repeated Auctions with Strategic Buyers. NIPS (2013)
Jacob D. Abernethy, Kareem Amin, Michael J. Kearns, Moez Draief
Large-Scale Bandit Problems and KWIK Learning. ICML (1) (2013)
Kareem Amin, Michael J. Kearns, Umar Syed
Graphical Models for Bandit Problems CoRR (2012)
Kareem Amin, Michael J. Kearns, Peter B. Key, Anton Schwaighofer
Budget Optimization for Sponsored Search: Censored Learning in MDPs CoRR (2012)
Kareem Amin, Michael J. Kearns, Peter B. Key, Anton Schwaighofer
Budget Optimization for Sponsored Search: Censored Learning in MDPs. UAI (2012)
Kareem Amin, Michael J. Kearns, Umar Syed
Graphical Models for Bandit Problems. UAI (2011)
Kareem Amin, Michael J. Kearns, Umar Syed
Bandits, Query Learning, and the Haystack Dimension. COLT (2011)