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Found 62 papers, 19 papers with code
Individually Fair Learning with One-Sided Feedback
We consider an online learning problem with one-sided feedback, in which the learner is able to observe the true label only for positively predicted instances.
Practical Adversarial Multivalid Conformal Prediction
It is computationally lightweight -- comparable to split conformal prediction -- but does not require having a held-out validation set, and so all data can be used for training models from which to derive a conformal score.
Mixed Differential Privacy in Computer Vision
AdaMix incorporates few-shot training, or cross-modal zero-shot learning, on public data prior to private fine-tuning, to improve the trade-off.
An Algorithmic Framework for Bias Bounties
We propose and analyze an algorithmic framework for "bias bounties": events in which external participants are invited to propose improvements to a trained model, akin to bug bounty events in software and security.
Online Multiobjective Minimax Optimization and Applications
The learner and the adversary then play in this game.
Multiaccurate Proxies for Downstream Fairness
The goal of the proxy is to allow a general "downstream" learner -- with minimal assumptions on their prediction task -- to be able to use the proxy to train a model that is fair with respect to the true sensitive features.
Adaptive Machine Unlearning
In this paper, we give a general reduction from deletion guarantees against adaptive sequences to deletion guarantees against non-adaptive sequences, using differential privacy and its connection to max information.
Rejoinder: Gaussian Differential Privacy
In this rejoinder, we aim to address two broad issues that cover most comments made in the discussion.
Differentially Private Query Release Through Adaptive Projection
We propose, implement, and evaluate a new algorithm for releasing answers to very large numbers of statistical queries like $k$-way marginals, subject to differential privacy.
Lexicographically Fair Learning: Algorithms and Generalization
We extend the notion of minimax fairness in supervised learning problems to its natural conclusion: lexicographic minimax fairness (or lexifairness for short).
Online Multivalid Learning: Means, Moments, and Prediction Intervals
We present a general, efficient technique for providing contextual predictions that are "multivalid" in various senses, against an online sequence of adversarially chosen examples $(x, y)$.
Minimax Group Fairness: Algorithms and Experiments
We consider a recently introduced framework in which fairness is measured by worst-case outcomes across groups, rather than by the more standard differences between group outcomes.
Moment Multicalibration for Uncertainty Estimation
We show how to achieve the notion of "multicalibration" from H\'ebert-Johnson et al. [2018] not just for means, but also for variances and other higher moments.
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning
We study the data deletion problem for convex models.
Algorithms and Learning for Fair Portfolio Design
We consider a variation on the classical finance problem of optimal portfolio design.
Fair Prediction with Endogenous Behavior
There is increasing regulatory interest in whether machine learning algorithms deployed in consequential domains (e. g. in criminal justice) treat different demographic groups "fairly."
Pipeline Interventions
We consider two objectives: social welfare maximization, and a fairness-motivated maximin objective that seeks to maximize the value to the population (starting node) with the \emph{least} expected value.
Optimal, Truthful, and Private Securities Lending
We consider a fundamental dynamic allocation problem motivated by the problem of $\textit{securities lending}$ in financial markets, the mechanism underlying the short selling of stocks.
A New Analysis of Differential Privacy's Generalization Guarantees
This second claim follows from a thought experiment in which we imagine that the dataset is resampled from the posterior distribution after the mechanism has committed to its answers.
Oracle Efficient Private Non-Convex Optimization
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.
Exponential Separations in Local Differential Privacy
We prove a general connection between the communication complexity of two-player games and the sample complexity of their multi-player locally private analogues.
Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis
We design a general framework for answering adaptive statistical queries that focuses on providing explicit confidence intervals along with point estimates.
Fuzzi: A Three-Level Logic for Differential Privacy
Curators of sensitive datasets sometimes need to know whether queries against the data are differentially private [Dwork et al. 2006].
Programming Languages Logic in Computer Science
Average Individual Fairness: Algorithms, Generalization and Experiments
Given a sample of individuals and classification problems, we design an oracle-efficient algorithm (i. e. one that is given access to any standard, fairness-free learning heuristic) for the fair empirical risk minimization task.
An Algorithmic Framework for Fairness Elicitation
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.
Gaussian Differential Privacy
More precisely, the privacy guarantees of \emph{any} hypothesis testing based definition of privacy (including original DP) converges to GDP in the limit under composition.
The Role of Interactivity in Local Differential Privacy
Next, we show that our reduction is tight by exhibiting a family of problems such that for any $k$, there is a fully interactive $k$-compositional protocol which solves the problem, while no sequentially interactive protocol can solve the problem without at least an $\tilde \Omega(k)$ factor more examples.
Equal Opportunity in Online Classification with Partial Feedback
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.
Differentially Private Fair Learning
This algorithm is appealingly simple, but must be able to use protected group membership explicitly at test time, which can be viewed as a form of 'disparate treatment'.
How to Use Heuristics for Differential Privacy
We show that there is an efficient algorithm for privately constructing synthetic data for any such class, given a non-private learning oracle.
The Frontiers of Fairness in Machine Learning
The last few years have seen an explosion of academic and popular interest in algorithmic fairness.
Fair Algorithms for Learning in Allocation Problems
We formalize this fairness notion for allocation problems and investigate its algorithmic consequences.
Downstream Effects of Affirmative Action
We show that both goals can be achieved when the college does not report grades.
An Empirical Study of Rich Subgroup Fairness for Machine Learning
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.
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
Data that is gathered adaptively --- via bandit algorithms, for example --- exhibits bias.
Online Learning with an Unknown Fairness Metric
We consider the problem of online learning in the linear contextual bandits setting, but in which there are also strong individual fairness constraints governed by an unknown similarity metric.
Local Differential Privacy for Evolving Data
Moreover, existing techniques to mitigate this effect do not apply in the "local model" of differential privacy that these systems use.
A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem
Bandit learning is characterized by the tension between long-term exploration and short-term exploitation.
Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM
Traditional approaches to differential privacy assume a fixed privacy requirement ε for a computation, and attempt to maximize the accuracy of the computation subject to the privacy constraint.
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
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.
Strategic Classification from Revealed Preferences
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.
Meritocratic Fairness for Cross-Population Selection
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.
A Convex Framework for Fair Regression
We introduce a flexible family of fairness regularizers for (linear and logistic) regression problems.
Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM
Traditional approaches to differential privacy assume a fixed privacy requirement $\epsilon$ for a computation, and attempt to maximize the accuracy of the computation subject to the privacy constraint.
Fairness in Criminal Justice Risk Assessments: The State of the Art
Methods: We draw on the existing literatures in criminology, computer science and statistics to provide an integrated examination of fairness and accuracy in criminal justice risk assessments.
Fairness in Reinforcement Learning
We initiate the study of fairness in reinforcement learning, where the actions of a learning algorithm may affect its environment and future rewards.
Multidimensional Dynamic Pricing for Welfare Maximization
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.
Fairness in Learning: Classic and Contextual Bandits
This tight connection allows us to provide a provably fair algorithm for the linear contextual bandit problem with a polynomial dependence on the dimension, and to show (for a different class of functions) a worst-case exponential gap in regret between fair and non-fair learning algorithms
Max-Information, Differential Privacy, and Post-Selection Hypothesis Testing
In this paper, we initiate a principled study of how the generalization properties of approximate differential privacy can be used to perform adaptive hypothesis testing, while giving statistically valid $p$-value corrections.
Adaptive Learning with Robust Generalization Guarantees
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.
Do Prices Coordinate Markets?
Second, we provide learning-theoretic results that show that such prices are robust to changing the buyers in the market, so long as all buyers are sampled from the same (unknown) distribution.
Generalization in Adaptive Data Analysis and Holdout Reuse
We also formalize and address the general problem of data reuse in adaptive data analysis.
Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs
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
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.
Computer-aided verification in mechanism design
To address both concerns, we explore techniques from computer-aided verification to construct formal proofs of incentive properties.
Computer Science and Game Theory Logic in Computer Science
Preserving Statistical Validity in Adaptive Data Analysis
We show that, surprisingly, there is a way to estimate an exponential in $n$ number of expectations accurately even if the functions are chosen adaptively.
Online Learning and Profit Maximization from Revealed Preferences
We consider the problem of learning from revealed preferences in an online setting.
Higher-Order Approximate Relational Refinement Types for Mechanism Design and Differential Privacy
Unlike typical programmatic properties, it is not sufficient for algorithms to merely satisfy the property---incentive properties are only useful if the strategic agents also believe this fact.
Programming Languages Computer Science and Game Theory
Privately Solving Linear Programs
In this paper, we initiate the systematic study of solving linear programs under differential privacy.
Dual Query: Practical Private Query Release for High Dimensional Data
We present a practical, differentially private algorithm for answering a large number of queries on high dimensional datasets.
Differentially Private Combinatorial Optimization
Is it even possible to design good algorithms for this problem that preserve the privacy of the clients?
Data Structures and Algorithms Cryptography and Security Computer Science and Game Theory
