Search Results for author:
Found 21 papers, 3 papers with code
(Individual) Fairness for k-Clustering
Intuitively, if a set of $k$ random points are chosen from $P$ as centers, every point $x\in P$ expects to have a center within radius $r(x)$.
Scalable Algorithms for Individual Preference Stable Clustering
In this paper, we study the natural local search algorithm for IP stable clustering.
Learning-Based Algorithms for Graph Searching Problems
Finally, we provide alternative simpler performance bounds on the algorithms of Banerjee et al. (2022) for the case of searching on a known graph, and establish new lower bounds for this setting.
Bayesian Strategic Classification
We initiate the study of partial information release by the learner in strategic classification.
Improved Frequency Estimation Algorithms with and without Predictions
In particular, their learning-augmented frequency estimation algorithm uses a learned heavy-hitter oracle which predicts which elements will appear many times in the stream.
Learning the Positions in CountSketch
We fix this issue and propose approaches for learning a sketching matrix for both low-rank approximation and Hessian approximation for second order optimization.
Approximation Algorithms for Fair Range Clustering
This paper studies the fair range clustering problem in which the data points are from different demographic groups and the goal is to pick $k$ centers with the minimum clustering cost such that each group is at least minimally represented in the centers set and no group dominates the centers set.
Sequential Strategic Screening
We initiate the study of strategic behavior in screening processes with multiple classifiers.
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.
Multi Stage Screening: Enforcing Fairness and Maximizing Efficiency in a Pre-Existing Pipeline
Even if the firm is required to interview all of those who pass the final round, the tests themselves could have the property that qualified individuals from some groups pass more easily than qualified individuals from others.
Fair Representation Clustering with Several Protected Classes
For this special case, we present an $O(\log k)$-approximation algorithm that runs in $(kf)^{O(\ell)}\log n + poly(n)$ time.
Approximating Fair Clustering with Cascaded Norm Objectives
We utilize convex programming techniques to approximate the $(p, q)$-Fair Clustering problem for different values of $p$ and $q$.
Improved Approximation Algorithms for Individually Fair Clustering
Moreover, for $p=1$ ($k$-median) and $p=\infty$ ($k$-center), we present improved cost-approximation factors $7. 081+\varepsilon$ and $3+\varepsilon$ respectively.
Approximation Algorithms for Socially Fair Clustering
In order to obtain our result, we introduce a strengthened LP relaxation and show that it has an integrality gap of $\Theta(\frac{\log \ell}{\log\log\ell})$ for a fixed $p$.
A framework for learned sparse sketches
In this work, we consider the problem of optimizing sketches to obtain low approximation error over a data distribution.
Learning the Positions in CountSketch
Despite the growing body of work on this paradigm, a noticeable omission is that the locations of the non-zero entries of previous algorithms were fixed, and only their values were learned.
Individual Fairness for $k$-Clustering
Intuitively, if a set of $k$ random points are chosen from $P$ as centers, every point $x\in P$ expects to have a center within radius $r(x)$.
Learning-Based Low-Rank Approximations
Our experiments show that, for multiple types of data sets, a learned sketch matrix can substantially reduce the approximation loss compared to a random matrix $S$, sometimes by one order of magnitude.
Sample-Optimal Low-Rank Approximation of Distance Matrices
Recent work by Bakshi and Woodruff (NeurIPS 2018) showed it is possible to compute a rank-$k$ approximation of a distance matrix in time $O((n+m)^{1+\gamma}) \cdot \mathrm{poly}(k, 1/\epsilon)$, where $\epsilon>0$ is an error parameter and $\gamma>0$ is an arbitrarily small constant.
Learning-Based Frequency Estimation Algorithms
Estimating the frequencies of elements in a data stream is a fundamental task in data analysis and machine learning.
Scalable Fair Clustering
In the fair variant of $k$-median, the points are colored, and the goal is to minimize the same average distance objective while ensuring that all clusters have an "approximately equal" number of points of each color.
