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Private Reinforcement Learning with PAC and Regret Guarantees

Sep 18, 2020
Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Zhiwei Steven Wu


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Bandit Data-driven Optimization: AI for Social Good and Beyond

Aug 26, 2020
Zheyuan Ryan Shi, Zhiwei Steven Wu, Rayid Ghani, Fei Fang


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Private Post-GAN Boosting

Jul 23, 2020
Marcel Neunhoeffer, Zhiwei Steven Wu, Cynthia Dwork


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Competing Bandits: The Perils of Exploration Under Competition

Jul 20, 2020
Guy Aridor, Yishay Mansour, Aleksandrs Slivkins, Zhiwei Steven Wu

* merged and extended version of arXiv:1702.08533 and arXiv:1902.05590 

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New Oracle-Efficient Algorithms for Private Synthetic Data Release

Jul 10, 2020
Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, Zhiwei Steven Wu


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Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification

Jul 07, 2020
Yingxue Zhou, Zhiwei Steven Wu, Arindam Banerjee


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Understanding Gradient Clipping in Private SGD: A Geometric Perspective

Jun 27, 2020
Xiangyi Chen, Zhiwei Steven Wu, Mingyi Hong


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Private Stochastic Non-Convex Optimization: Adaptive Algorithms and Tighter Generalization Bounds

Jun 24, 2020
Yingxue Zhou, Xiangyi Chen, Mingyi Hong, Zhiwei Steven Wu, Arindam Banerjee


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Greedy Algorithm almost Dominates in Smoothed Contextual Bandits

May 19, 2020
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu

* Results in this paper, without any proofs, have been announced in an extended abstract (Raghavan et al., 2018a), and fleshed out in the technical report (Raghavan et al., 2018b [arXiv:1806.00543]). This manuscript covers a subset of results from Raghavan et al. (2018a,b), focusing on the greedy algorithm, and is streamlined accordingly 

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Private Query Release Assisted by Public Data

Apr 23, 2020
Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan Ullman, Zhiwei Steven Wu


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Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis

Feb 26, 2020
Vidyashankar Sivakumar, Zhiwei Steven Wu, Arindam Banerjee


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Privately Learning Markov Random Fields

Feb 21, 2020
Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni, Zhiwei Steven Wu


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Locally Private Hypothesis Selection

Feb 21, 2020
Sivakanth Gopi, Gautam Kamath, Janardhan Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang


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Metric-Free Individual Fairness in Online Learning

Feb 17, 2020
Yahav Bechavod, Christopher Jung, Zhiwei Steven Wu


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Causal Feature Discovery through Strategic Modification

Feb 17, 2020
Yahav Bechavod, Katrina Ligett, Zhiwei Steven Wu, Juba Ziani


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Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization

Feb 13, 2020
Vikas K. Garg, Adam Kalai, Katrina Ligett, Zhiwei Steven Wu


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Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond

Oct 11, 2019
Arindam Banerjee, Qilong Gu, Vidyashankar Sivakumar, Zhiwei Steven Wu


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Differentially Private Objective Perturbation: Beyond Smoothness and Convexity

Sep 03, 2019
Seth Neel, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu


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Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms

Jun 06, 2019
Xiangyi Chen, Tiancong Chen, Haoran Sun, Zhiwei Steven Wu, Mingyi Hong


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Private Hypothesis Selection

May 30, 2019
Mark Bun, Gautam Kamath, Thomas Steinke, Zhiwei Steven Wu


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Fair Regression: Quantitative Definitions and Reduction-based Algorithms

May 30, 2019
Alekh Agarwal, Miroslav Dudík, Zhiwei Steven Wu


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Eliciting and Enforcing Subjective Individual Fairness

May 25, 2019
Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu


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Bayesian Exploration with Heterogeneous Agents

Feb 19, 2019
Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, Zhiwei Steven Wu


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Competing Bandits: The Perils of Exploration under Competition

Feb 14, 2019
Guy Aridor, Kevin Liu, Aleksandrs Slivkins, Zhiwei Steven Wu


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Equal Opportunity in Online Classification with Partial Feedback

Feb 06, 2019
Yahav Bechavod, Katrina Ligett, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu

* 28 pages 

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Privacy-Preserving Distributed Deep Learning for Clinical Data

Dec 04, 2018
Brett K. Beaulieu-Jones, William Yuan, Samuel G. Finlayson, Zhiwei Steven Wu

* Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216 

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Locally Private Gaussian Estimation

Nov 20, 2018
Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Zhiwei Steven Wu


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How to Use Heuristics for Differential Privacy

Nov 19, 2018
Seth Neel, Aaron Roth, Zhiwei Steven Wu


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Incentivizing Exploration with Unbiased Histories

Nov 14, 2018
Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, Zhiwei Steven Wu


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