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Found 29 papers, 13 papers with code
Initializing Services in Interactive ML Systems for Diverse Users
(ii) Suboptimal Local Solutions: The total loss (sum of loss functions across all users and all services) landscape is not convex even if the individual losses on a single service are convex, making it likely for the learning dynamics to get stuck in local minima.
Fair Active Learning in Low-Data Regimes
In this work, we address the challenges of reducing bias and improving accuracy in data-scarce environments, where the cost of collecting labeled data prohibits the use of large, labeled datasets.
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.
Individual Preference Stability for Clustering
In this paper, we propose a natural notion of individual preference (IP) stability for clustering, which asks that every data point, on average, is closer to the points in its own cluster than to the points in any other cluster.
Active Learning with Safety Constraints
To our knowledge, our results are the first on best-arm identification in linear bandits with safety constraints.
Emergent segmentation from participation dynamics and multi-learner retraining
We study the participation and retraining dynamics that arise when both the learners and sub-populations of users are \emph{risk-reducing}, which cover a broad class of updates including gradient descent, multiplicative weights, etc.
Preference Dynamics Under Personalized Recommendations
We use a similar model of preference dynamics, where an individual's preferences move towards content the consume and enjoy, and away from content they consume and dislike.
Distributionally Robust Data Join
Suppose we are given two datasets: a labeled dataset and unlabeled dataset which also has additional auxiliary features not present in the first dataset.
Optimal Spend Rate Estimation and Pacing for Ad Campaigns with Budgets
We present four main results: 1) for the episodic setting we give sample complexity bounds for the spend rate prediction problem: given $n$ samples from each episode, with high probability we have $|\widehat{\rho}_e - \rho_e| \leq \tilde{O}(\frac{1}{n^{1/3}})$ where $\rho_e$ is the optimal spend rate for the episode, $\widehat{\rho}_e$ is the estimate from our algorithm, 2) we extend the algorithm of Balseiro and Gur (2017) to operate on varying, approximate spend rates and show that the resulting combined system of optimal spend rate estimation and online pacing algorithm for episodic settings has regret that vanishes in number of historic samples $n$ and the number of rounds $T$, 3) for non-episodic but slowly-changing distributions we show that the same approach approximates the optimal bidding strategy up to a factor dependent on the rate-of-change of the distributions and 4) we provide experiments showing that our algorithm outperforms both static spend plans and non-pacing across a wide variety of settings.
Auctions and Peer Prediction for Academic Peer Review
The revenue raised in the submission stage auction is used to pay reviewers based on the quality of their reviews in the reviewing stage.
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information
An alternate approach that is commonly used is to separately train an attribute classifier on data with sensitive attribute information, and then use it later in the ML pipeline to evaluate the bias of a given classifier.
Active Sampling for Min-Max Fairness
We propose simple active sampling and reweighting strategies for optimizing min-max fairness that can be applied to any classification or regression model learned via loss minimization.
A Notion of Individual Fairness for Clustering
A common distinction in fair machine learning, in particular in fair classification, is between group fairness and individual fairness.
Diversity and Inclusion Metrics in Subset Selection
The ethical concept of fairness has recently been applied in machine learning (ML) settings to describe a wide range of constraints and objectives.
FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning
We present FairVis, a mixed-initiative visual analytics system that integrates a novel subgroup discovery technique for users to audit the fairness of machine learning models.
Multi-Criteria Dimensionality Reduction with Applications to Fairness
Our main result is an exact polynomial-time algorithm for the two-criterion dimensionality reduction problem when the two criteria are increasing concave functions.
Predictive Inequity in Object Detection
In this work, we investigate whether state-of-the-art object detection systems have equitable predictive performance on pedestrians with different skin tones.
Guarantees for Spectral Clustering with Fairness Constraints
Given the widespread popularity of spectral clustering (SC) for partitioning graph data, we study a version of constrained SC in which we try to incorporate the fairness notion proposed by Chierichetti et al. (2017).
Fair k-Center Clustering for Data Summarization
In data summarization we want to choose $k$ prototypes in order to summarize a data set.
The Price of Fair PCA: One Extra Dimension
This motivates our study of dimensionality reduction techniques which maintain similar fidelity for A and B.
Datasheets for Datasets
The machine learning community currently has no standardized process for documenting datasets, which can lead to severe consequences in high-stakes domains.
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.
A Convex Framework for Fair Regression
We introduce a flexible family of fairness regularizers for (linear and logistic) regression problems.
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.
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
Learning Simple Auctions
We present a general framework for proving polynomial sample complexity bounds for the problem of learning from samples the best auction in a class of "simple" auctions.
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.
