Search Results for author: Zhiwei Steven Wu

Found 69 papers, 18 papers with code

Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders

no code implementations18 May 2022 Logan Stapleton, Min Hun Lee, Diana Qing, Marya Wright, Alexandra Chouldechova, Kenneth Holstein, Zhiwei Steven Wu, Haiyi Zhu

In this work, we conducted a set of seven design workshops with 35 stakeholders who have been impacted by the child welfare system or who work in it to understand their beliefs and concerns around PRMs, and to engage them in imagining new uses of data and technologies in the child welfare system.

Decision Making

Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits

no code implementations13 May 2022 Wesley Hanwen Deng, Manish Nagireddy, Michelle Seng Ah Lee, Jatinder Singh, Zhiwei Steven Wu, Kenneth Holstein, Haiyi Zhu

Recent years have seen the development of many open-source ML fairness toolkits aimed at helping ML practitioners assess and address unfairness in their systems.

Fairness

Provably Fair Federated Learning via Bounded Group Loss

no code implementations18 Mar 2022 Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith

Our work provides a new definition for group fairness in federated learning based on the notion of Bounded Group Loss (BGL), which can be easily applied to common federated learning objectives.

Fairness Federated Learning

Fully Adaptive Composition in Differential Privacy

no code implementations10 Mar 2022 Justin Whitehouse, Aaditya Ramdas, Ryan Rogers, Zhiwei Steven Wu

We construct filters that match the tightness of advanced composition, including constants, despite allowing for adaptively chosen privacy parameters.

Locally private nonparametric confidence intervals and sequences

no code implementations17 Feb 2022 Ian Waudby-Smith, Zhiwei Steven Wu, Aaditya Ramdas

This work derives methods for performing nonparametric, nonasymptotic statistical inference for population parameters under the constraint of local differential privacy (LDP).

Improved Regret for Differentially Private Exploration in Linear MDP

no code implementations2 Feb 2022 Dung Daniel Ngo, Giuseppe Vietri, Zhiwei Steven Wu

We study privacy-preserving exploration in sequential decision-making for environments that rely on sensitive data such as medical records.

Decision Making

Causal Imitation Learning under Temporally Correlated Noise

1 code implementation2 Feb 2022 Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu

We develop algorithms for imitation learning from policy data that was corrupted by temporally correlated noise in expert actions.

Imitation Learning

Constrained Variational Policy Optimization for Safe Reinforcement Learning

no code implementations28 Jan 2022 Zuxin Liu, Zhepeng Cen, Vladislav Isenbaev, Wei Liu, Zhiwei Steven Wu, Bo Li, Ding Zhao

Safe reinforcement learning (RL) aims to learn policies that satisfy certain constraints before deploying to safety-critical applications.

reinforcement-learning Safe Reinforcement Learning

Bayesian Persuasion for Algorithmic Recourse

no code implementations12 Dec 2021 Keegan Harris, Valerie Chen, Joon Sik Kim, Ameet Talwalkar, Hoda Heidari, Zhiwei Steven Wu

While the decision maker's problem of finding the optimal Bayesian incentive-compatible (BIC) signaling policy takes the form of optimization over infinitely-many variables, we show that this optimization can be cast as a linear program over finitely-many regions of the space of possible assessment rules.

Decision Making

A Critique of Strictly Batch Imitation Learning

no code implementations5 Oct 2021 Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu

Recent work by Jarrett et al. attempts to frame the problem of offline imitation learning (IL) as one of learning a joint energy-based model, with the hope of out-performing standard behavioral cloning.

Frame Imitation Learning

Private Multi-Task Learning: Formulation and Applications to Federated Learning

no code implementations30 Aug 2021 Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith

Many problems in machine learning rely on multi-task learning (MTL), in which the goal is to solve multiple related machine learning tasks simultaneously.

Distributed Optimization Multi-Task Learning +1

Incentivizing Compliance with Algorithmic Instruments

1 code implementation21 Jul 2021 Daniel Ngo, Logan Stapleton, Vasilis Syrgkanis, Zhiwei Steven Wu

In rounds, a social planner interacts with a sequence of heterogeneous agents who arrive with their unobserved private type that determines both their prior preferences across the actions (e. g., control and treatment) and their baseline rewards without taking any treatment.

Selection bias

Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses

1 code implementation12 Jul 2021 Keegan Harris, Daniel Ngo, Logan Stapleton, Hoda Heidari, Zhiwei Steven Wu

In settings where Machine Learning (ML) algorithms automate or inform consequential decisions about people, individual decision subjects are often incentivized to strategically modify their observable attributes to receive more favorable predictions.

Decision Making Fairness

Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy

no code implementations25 Jun 2021 Xinwei Zhang, Xiangyi Chen, Mingyi Hong, Zhiwei Steven Wu, JinFeng Yi

Recently, there has been a line of work on incorporating the formal privacy notion of differential privacy with FL.

Federated Learning

Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods

1 code implementation NeurIPS 2021 Terrance Liu, Giuseppe Vietri, Zhiwei Steven Wu

We study private synthetic data generation for query release, where the goal is to construct a sanitized version of a sensitive dataset, subject to differential privacy, that approximately preserves the answers to a large collection of statistical queries.

Synthetic Data Generation

Stateful Strategic Regression

no code implementations NeurIPS 2021 Keegan Harris, Hoda Heidari, Zhiwei Steven Wu

In particular, we consider settings in which the agent's effort investment today can accumulate over time in the form of an internal state - impacting both his future rewards and that of the principal.

Decision Making

Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap

1 code implementation4 Mar 2021 Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu

We provide a unifying view of a large family of previous imitation learning algorithms through the lens of moment matching.

Imitation Learning

Information Discrepancy in Strategic Learning

no code implementations1 Mar 2021 Yahav Bechavod, Chara Podimata, Zhiwei Steven Wu, Juba Ziani

We initiate the study of the effects of non-transparency in decision rules on individuals' ability to improve in strategic learning settings.

Decision Making

Towards the Unification and Robustness of Perturbation and Gradient Based Explanations

no code implementations21 Feb 2021 Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Zhiwei Steven Wu, Himabindu Lakkaraju

As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniques for explaining these black boxes in a post hoc manner.

Leveraging Public Data for Practical Private Query Release

1 code implementation17 Feb 2021 Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan Ullman, Zhiwei Steven Wu

In many statistical problems, incorporating priors can significantly improve performance.

Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation

no code implementations22 Oct 2020 Hong Shen, Hanwen Wesley Deng, Aditi Chattopadhyay, Zhiwei Steven Wu, Xu Wang, Haiyi Zhu

In this paper, we present Value Card, an educational toolkit to inform students and practitioners of the social impacts of different machine learning models via deliberation.

Fairness

Private Reinforcement Learning with PAC and Regret Guarantees

no code implementations18 Sep 2020 Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Zhiwei Steven Wu

Motivated by high-stakes decision-making domains like personalized medicine where user information is inherently sensitive, we design privacy preserving exploration policies for episodic reinforcement learning (RL).

Decision Making reinforcement-learning

Bandit Data-Driven Optimization

1 code implementation26 Aug 2020 Zheyuan Ryan Shi, Zhiwei Steven Wu, Rayid Ghani, Fei Fang

In this paper, we introduce bandit data-driven optimization, the first iterative prediction-prescription framework to address these pain points.

Private Post-GAN Boosting

1 code implementation ICLR 2021 Marcel Neunhoeffer, Zhiwei Steven Wu, Cynthia Dwork

We also provide a non-private variant of PGB that improves the data quality of standard GAN training.

Competing Bandits: The Perils of Exploration Under Competition

no code implementations20 Jul 2020 Guy Aridor, Yishay Mansour, Aleksandrs Slivkins, Zhiwei Steven Wu

Users arrive one by one and choose between the two firms, so that each firm makes progress on its bandit problem only if it is chosen.

Multi-Armed Bandits

New Oracle-Efficient Algorithms for Private Synthetic Data Release

1 code implementation ICML 2020 Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, Zhiwei Steven Wu

We present three new algorithms for constructing differentially private synthetic data---a sanitized version of a sensitive dataset that approximately preserves the answers to a large collection of statistical queries.

Understanding Gradient Clipping in Private SGD: A Geometric Perspective

no code implementations NeurIPS 2020 Xiangyi Chen, Zhiwei Steven Wu, Mingyi Hong

Deep learning models are increasingly popular in many machine learning applications where the training data may contain sensitive information.

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

no code implementations24 Jun 2020 Yingxue Zhou, Xiangyi Chen, Mingyi Hong, Zhiwei Steven Wu, Arindam Banerjee

We obtain this rate by providing the first analyses on a collection of private gradient-based methods, including adaptive algorithms DP RMSProp and DP Adam.

Generalization Bounds

Greedy Algorithm almost Dominates in Smoothed Contextual Bandits

no code implementations19 May 2020 Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu

Online learning algorithms, widely used to power search and content optimization on the web, must balance exploration and exploitation, potentially sacrificing the experience of current users in order to gain information that will lead to better decisions in the future.

Multi-Armed Bandits online learning

Private Query Release Assisted by Public Data

no code implementations ICML 2020 Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan Ullman, Zhiwei Steven Wu

In comparison, with only private samples, this problem cannot be solved even for simple query classes with VC-dimension one, and without any private samples, a larger public sample of size $d/\alpha^2$ is needed.

Privately Learning Markov Random Fields

no code implementations ICML 2020 Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni, Zhiwei Steven Wu

We consider the problem of learning Markov Random Fields (including the prototypical example, the Ising model) under the constraint of differential privacy.

Locally Private Hypothesis Selection

no code implementations21 Feb 2020 Sivakanth Gopi, Gautam Kamath, Janardhan Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang

Absent privacy constraints, this problem requires $O(\log k)$ samples from $p$, and it was recently shown that the same complexity is achievable under (central) differential privacy.

Two-sample testing

Gaming Helps! Learning from Strategic Interactions in Natural Dynamics

no code implementations17 Feb 2020 Yahav Bechavod, Katrina Ligett, Zhiwei Steven Wu, Juba Ziani

We consider an online regression setting in which individuals adapt to the regression model: arriving individuals are aware of the current model, and invest strategically in modifying their own features so as to improve the predicted score that the current model assigns to them.

Causal Discovery

Metric-Free Individual Fairness in Online Learning

no code implementations NeurIPS 2020 Yahav Bechavod, Christopher Jung, Zhiwei Steven Wu

We study an online learning problem subject to the constraint of individual fairness, which requires that similar individuals are treated similarly.

Fairness General Classification +1

Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization

no code implementations13 Feb 2020 Vikas K. Garg, Adam Kalai, Katrina Ligett, Zhiwei Steven Wu

Domain generalization is the problem of machine learning when the training data and the test data come from different data domains.

Domain Generalization feature selection

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

no code implementations NeurIPS 2019 Arindam Banerjee, Qilong Gu, Vidyashankar Sivakumar, Zhiwei Steven Wu

We also discuss stochastic process based forms of J-L, RIP, and sketching, to illustrate the generality of the results.

Oracle Efficient Private Non-Convex Optimization

1 code implementation ICML 2020 Seth Neel, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu

We find that for the problem of learning linear classifiers, directly optimizing for 0/1 loss using our approach can out-perform the more standard approach of privately optimizing a convex-surrogate loss function on the Adult dataset.

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

no code implementations NeurIPS 2020 Xiangyi Chen, Tiancong Chen, Haoran Sun, Zhiwei Steven Wu, Mingyi Hong

We show that these algorithms are non-convergent whenever there is some disparity between the expected median and mean over the local gradients.

Federated Learning

Fair Regression: Quantitative Definitions and Reduction-based Algorithms

3 code implementations30 May 2019 Alekh Agarwal, Miroslav Dudík, Zhiwei Steven Wu

Our schemes only require access to standard risk minimization algorithms (such as standard classification or least-squares regression) while providing theoretical guarantees on the optimality and fairness of the obtained solutions.

Fairness

Private Hypothesis Selection

no code implementations NeurIPS 2019 Mark Bun, Gautam Kamath, Thomas Steinke, Zhiwei Steven Wu

The sample complexity of our basic algorithm is $O\left(\frac{\log m}{\alpha^2} + \frac{\log m}{\alpha \varepsilon}\right)$, representing a minimal cost for privacy when compared to the non-private algorithm.

An Algorithmic Framework for Fairness Elicitation

1 code implementation25 May 2019 Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu

We consider settings in which the right notion of fairness is not captured by simple mathematical definitions (such as equality of error rates across groups), but might be more complex and nuanced and thus require elicitation from individual or collective stakeholders.

Fairness Generalization Bounds

Bayesian Exploration with Heterogeneous Agents

no code implementations19 Feb 2019 Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, Zhiwei Steven Wu

We consider Bayesian Exploration: a simple model in which the recommendation system (the "principal") controls the information flow to the users (the "agents") and strives to incentivize exploration via information asymmetry.

Recommendation Systems

Equal Opportunity in Online Classification with Partial Feedback

1 code implementation NeurIPS 2019 Yahav Bechavod, Katrina Ligett, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu

We study an online classification problem with partial feedback in which individuals arrive one at a time from a fixed but unknown distribution, and must be classified as positive or negative.

Classification Decision Making Under Uncertainty +3

Privacy-Preserving Distributed Deep Learning for Clinical Data

no code implementations4 Dec 2018 Brett K. Beaulieu-Jones, William Yuan, Samuel G. Finlayson, Zhiwei Steven Wu

Deep learning with medical data often requires larger samples sizes than are available at single providers.

Locally Private Gaussian Estimation

no code implementations NeurIPS 2019 Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Zhiwei Steven Wu

We study a basic private estimation problem: each of $n$ users draws a single i. i. d.

How to Use Heuristics for Differential Privacy

no code implementations19 Nov 2018 Seth Neel, Aaron Roth, Zhiwei Steven Wu

We show that there is an efficient algorithm for privately constructing synthetic data for any such class, given a non-private learning oracle.

Incentivizing Exploration with Selective Data Disclosure

no code implementations14 Nov 2018 Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, Zhiwei Steven Wu

We design a policy with optimal regret in the worst case over reward distributions.

An Empirical Study of Rich Subgroup Fairness for Machine Learning

5 code implementations24 Aug 2018 Michael Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu

In this paper, we undertake an extensive empirical evaluation of the algorithm of Kearns et al. On four real datasets for which fairness is a concern, we investigate the basic convergence of the algorithm when instantiated with fast heuristics in place of learning oracles, measure the tradeoffs between fairness and accuracy, and compare this approach with the recent algorithm of Agarwal et al. [2018], which implements weaker and more traditional marginal fairness constraints defined by individual protected attributes.

Fairness

Orthogonal Random Forest for Causal Inference

1 code implementation9 Jun 2018 Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu

We provide a consistency rate and establish asymptotic normality for our estimator.

Causal Inference

The Externalities of Exploration and How Data Diversity Helps Exploitation

no code implementations1 Jun 2018 Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu

Returning to group-level effects, we show that under the same conditions, negative group externalities essentially vanish under the greedy algorithm.

Multi-Armed Bandits online learning

Locally Private Bayesian Inference for Count Models

1 code implementation22 Mar 2018 Aaron Schein, Zhiwei Steven Wu, Alexandra Schofield, Mingyuan Zhou, Hanna Wallach

We present a general method for privacy-preserving Bayesian inference in Poisson factorization, a broad class of models that includes some of the most widely used models in the social sciences.

Bayesian Inference Link Prediction

Semiparametric Contextual Bandits

2 code implementations ICML 2018 Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis

This paper studies semiparametric contextual bandits, a generalization of the linear stochastic bandit problem where the reward for an action is modeled as a linear function of known action features confounded by an non-linear action-independent term.

Multi-Armed Bandits

Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness

5 code implementations ICML 2018 Michael Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu

We prove that the computational problem of auditing subgroup fairness for both equality of false positive rates and statistical parity is equivalent to the problem of weak agnostic learning, which means it is computationally hard in the worst case, even for simple structured subclasses.

Fairness

Strategic Classification from Revealed Preferences

no code implementations22 Oct 2017 Jinshuo Dong, Aaron Roth, Zachary Schutzman, Bo Waggoner, Zhiwei Steven Wu

We study an online linear classification problem, in which the data is generated by strategic agents who manipulate their features in an effort to change the classification outcome.

Classification General Classification

Meritocratic Fairness for Cross-Population Selection

no code implementations ICML 2017 Michael Kearns, Aaron Roth, Zhiwei Steven Wu

We consider the problem of selecting a strong pool of individuals from several populations with incomparable skills (e. g. soccer players, mathematicians, and singers) in a fair manner.

Fairness

Competing Bandits: Learning under Competition

no code implementations27 Feb 2017 Yishay Mansour, Aleksandrs Slivkins, Zhiwei Steven Wu

Most modern systems strive to learn from interactions with users, and many engage in exploration: making potentially suboptimal choices for the sake of acquiring new information.

Multidimensional Dynamic Pricing for Welfare Maximization

no code implementations19 Jul 2016 Aaron Roth, Aleksandrs Slivkins, Jonathan Ullman, Zhiwei Steven Wu

We are able to apply this technique to the setting of unit demand buyers despite the fact that in that setting the goods are not divisible, and the natural fractional relaxation of a unit demand valuation is not strongly concave.

Predicting with Distributions

no code implementations3 Jun 2016 Michael Kearns, Zhiwei Steven Wu

We consider a new learning model in which a joint distribution over vector pairs $(x, y)$ is determined by an unknown function $c(x)$ that maps input vectors $x$ not to individual outputs, but to entire {\em distributions\/} over output vectors $y$.

General Classification

Adaptive Learning with Robust Generalization Guarantees

no code implementations24 Feb 2016 Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, Zhiwei Steven Wu

We also show that perfect generalization is a strictly stronger guarantee than differential privacy, but that, nevertheless, many learning tasks can be carried out subject to the guarantees of perfect generalization.

Bayesian Exploration: Incentivizing Exploration in Bayesian Games

no code implementations24 Feb 2016 Yishay Mansour, Aleksandrs Slivkins, Vasilis Syrgkanis, Zhiwei Steven Wu

As a key technical tool, we introduce the concept of explorable actions, the actions which some incentive-compatible policy can recommend with non-zero probability.

Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs

no code implementations NeurIPS 2016 Shahin Jabbari, Ryan Rogers, Aaron Roth, Zhiwei Steven Wu

This models the problem of predicting the behavior of a rational agent whose goals are known, but whose resources are unknown.

Watch and Learn: Optimizing from Revealed Preferences Feedback

no code implementations4 Apr 2015 Aaron Roth, Jonathan Ullman, Zhiwei Steven Wu

In this paper we present an approach to solving for the leader's optimal strategy in certain Stackelberg games where the follower's utility function (and thus the subsequent best response of the follower) is unknown.

Dual Query: Practical Private Query Release for High Dimensional Data

no code implementations6 Feb 2014 Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu

We present a practical, differentially private algorithm for answering a large number of queries on high dimensional datasets.

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