Search Results for author:
Found 36 papers, 11 papers with code
Diversified Ensembling: An Experiment in Crowdsourced Machine Learning
There, unlike in classical crowdsourced ML, participants deliberately specialize their efforts by working on subproblems, such as demographic subgroups in the service of fairness.
Membership Inference Attacks on Diffusion Models via Quantile Regression
Our proposed MI attack learns quantile regression models that predict (a quantile of) the distribution of reconstruction loss on examples not used in training.
Balanced Filtering via Non-Disclosive Proxies
Importantly, we require that the proxy classification itself not reveal significant information about the sensitive group membership of any individual sample (i. e., it should be sufficiently non-disclosive).
AI Model Disgorgement: Methods and Choices
One potential fix for training corpus data defects is model disgorgement -- the elimination of not just the improperly used data, but also the effects of improperly used data on any component of an ML model.
Improved Differentially Private Regression via Gradient Boosting
We revisit the problem of differentially private squared error linear regression.
Multicalibration as Boosting for Regression
Using this characterization, we give an exceedingly simple algorithm that can be analyzed both as a boosting algorithm for regression and as a multicalibration algorithm for a class H that makes use only of a standard squared error regression oracle for H. We give a weak learning assumption on H that ensures convergence to Bayes optimality without the need to make any realizability assumptions -- giving us an agnostic boosting algorithm for regression.
Confidence-Ranked Reconstruction of Census Microdata from Published Statistics
Our attacks significantly outperform those that are based only on access to a public distribution or population from which the private dataset $D$ was sampled, demonstrating that they are exploiting information in the aggregate statistics $Q(D)$, and not simply the overall structure of the distribution.
Multicalibrated Regression for Downstream Fairness
We show how to take a regression function $\hat{f}$ that is appropriately ``multicalibrated'' and efficiently post-process it into an approximately error minimizing classifier satisfying a large variety of fairness constraints.
Private Synthetic Data for Multitask Learning and Marginal Queries
A key innovation in our algorithm is the ability to directly handle numerical features, in contrast to a number of related prior approaches which require numerical features to be first converted into {high cardinality} categorical features via {a binning strategy}.
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.
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.
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).
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.
Algorithms and Learning for Fair Portfolio Design
We consider a variation on the classical finance problem of optimal portfolio design.
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.
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.
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.
Equilibrium Characterization for Data Acquisition Games
We demonstrate a reduction from this potentially complicated action space to a one-shot, two-action game in which each firm only decides whether or not to buy the data.
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'.
Fair Algorithms for Learning in Allocation Problems
We formalize this fairness notion for allocation problems and investigate its algorithmic consequences.
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.
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.
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.
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.
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.
Predicting with Distributions
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 $y$.
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
Online Learning and Profit Maximization from Revealed Preferences
We consider the problem of learning from revealed preferences in an online setting.
Marginals-to-Models Reducibility
We consider a number of classical and new computational problems regarding marginal distributions, and inference in models specifying a full joint distribution.
Graphical Models for Game Theory
The interpretation is that the payoff to player i is determined entirely by the actions of player i and his neighbors in the graph, and thus the payoff matrix to player i is indexed only by these players.
Competitive Contagion in Networks
We also show that if this property is violated the Price of Anarchy can be unbounded, thus yielding sharp threshold behavior for a broad class of dynamics.
Computer Science and Game Theory
