
Incentivizing Compliance with Algorithmic Instruments
Randomized experiments can be susceptible to selection bias due to poten...
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Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses
Machine Learning algorithms often prompt individuals to strategically mo...
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Understanding Clipping for Federated Learning: Convergence and ClientLevel Differential Privacy
Providing privacy protection has been one of the primary motivations of ...
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Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods
We study private synthetic data generation for query release, where the ...
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Stateful Strategic Regression
Automated decisionmaking tools increasingly assess individuals to deter...
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Of Moments and Matching: Tradeoffs and Treatments in Imitation Learning
We provide a unifying view of a large family of previous imitation learn...
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Information Discrepancy in Strategic Learning
We study the effects of information discrepancy across subpopulations o...
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Towards the Unification and Robustness of Perturbation and Gradient Based Explanations
As machine learning black boxes are increasingly being deployed in criti...
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Leveraging Public Data for Practical Private Query Release
In many statistical problems, incorporating priors can significantly imp...
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Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systems
Recent work in fair machine learning has proposed dozens of technical de...
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Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation
Recently, there have been increasing calls for computer science curricul...
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Private Reinforcement Learning with PAC and Regret Guarantees
Motivated by highstakes decisionmaking domains like personalized medic...
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Bandit Datadriven Optimization: AI for Social Good and Beyond
The use of machine learning (ML) systems in realworld applications enta...
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Private PostGAN Boosting
Differentially private GANs have proven to be a promising approach for g...
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New OracleEfficient Algorithms for Private Synthetic Data Release
We present three new algorithms for constructing differentially private ...
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Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification
Differentially private SGD (DPSGD) is one of the most popular methods f...
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Understanding Gradient Clipping in Private SGD: A Geometric Perspective
Deep learning models are increasingly popular in many machine learning a...
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Private Stochastic NonConvex Optimization: Adaptive Algorithms and Tighter Generalization Bounds
We study differentially private (DP) algorithms for stochastic nonconve...
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Greedy Algorithm almost Dominates in Smoothed Contextual Bandits
Online learning algorithms, widely used to power search and content opti...
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Private Query Release Assisted by Public Data
We study the problem of differentially private query release assisted by...
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Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis
Bandit learning algorithms typically involve the balance of exploration ...
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Locally Private Hypothesis Selection
We initiate the study of hypothesis selection under local differential p...
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Privately Learning Markov Random Fields
We consider the problem of learning Markov Random Fields (including the ...
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Causal Feature Discovery through Strategic Modification
We consider an online regression setting in which individuals adapt to t...
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Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization
Domain generalization is the problem of machine learning when the traini...
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MetricFree Individual Fairness in Online Learning
We study an online learning problem subject to the constraint of individ...
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Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond
Several important families of computational and statistical results in m...
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Designing Interfaces to Help Stakeholders Comprehend, Navigate, and Manage Algorithmic TradeOffs
Artificial intelligence algorithms have been applied to a wide variety o...
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Differentially Private Objective Perturbation: Beyond Smoothness and Convexity
One of the most effective algorithms for differentially private learning...
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Distributed Training with Heterogeneous Data: Bridging Median and Mean Based Algorithms
Recently, there is a growing interest in the study of medianbased algor...
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Private Hypothesis Selection
We provide a differentially private algorithm for hypothesis selection. ...
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Fair Regression: Quantitative Definitions and Reductionbased Algorithms
In this paper, we study the prediction of a realvalued target, such as ...
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Eliciting and Enforcing Subjective Individual Fairness
We revisit the notion of individual fairness first proposed by Dwork et ...
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Bayesian Exploration with Heterogeneous Agents
It is common in recommendation systems that users both consume and produ...
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The Perils of Exploration under Competition: A Computational Modeling Approach
We empirically study the interplay between exploration and competition. ...
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Competing Bandits: The Perils of Exploration under Competition
We empirically study the interplay between exploration and competition. ...
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Equal Opportunity in Online Classification with Partial Feedback
We study an online classification problem with partial feedback in which...
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PrivacyPreserving Distributed Deep Learning for Clinical Data
Deep learning with medical data often requires larger samples sizes than...
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Locally Private Gaussian Estimation
We study a basic private estimation problem: each of n users draws a sin...
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How to Use Heuristics for Differential Privacy
We develop theory for using heuristics to solve computationally hard pro...
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Incentivizing Exploration with Unbiased Histories
In a social learning setting, there is a set of actions, each of which h...
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An Empirical Study of Rich Subgroup Fairness for Machine Learning
Kearns et al. [2018] recently proposed a notion of rich subgroup fairnes...
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Orthogonal Random Forest for Heterogeneous Treatment Effect Estimation
We study the problem of estimating heterogeneous treatment effects from ...
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The Externalities of Exploration and How Data Diversity Helps Exploitation
Online learning algorithms, widely used to power search and content opti...
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Locally Private Bayesian Inference for Count Models
As more aspects of social interaction are digitally recorded, there is a...
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Semiparametric Contextual Bandits
This paper studies semiparametric contextual bandits, a generalization o...
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A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem
Bandit learning is characterized by the tension between longterm explor...
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Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
The most prevalent notions of fairness in machine learning are statistic...
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Strategic Classification from Revealed Preferences
We study an online linear classification problem, in which the data is g...
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Zhiwei Steven Wu
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