Search Results for author:
Found 16 papers, 10 papers with code
SVM via Saddle Point Optimization: New Bounds and Distributed Algorithms
Our algorithms achieve $(1-\epsilon)$-approximations with running time $\tilde{O}(nd+n\sqrt{d / \epsilon})$ for both variants, where $n$ is the number of points and $d$ is the dimensionality.
Multiwinner Voting with Fairness Constraints
Multiwinner voting rules are used to select a small representative subset of candidates or items from a larger set given the preferences of voters.
Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees
The main contribution of this paper is a new meta-algorithm for classification that takes as input a large class of fairness constraints, with respect to multiple non-disjoint sensitive attributes, and which comes with provable guarantees.
Stable and Fair Classification
Theoretically, we prove a stability guarantee, that was lacking in fair classification algorithms, and also provide an accuracy guarantee for our extended framework.
Coresets for Clustering with Fairness Constraints
Our approach is based on novel constructions of coresets: for the $k$-median objective, we construct an $\varepsilon$-coreset of size $O(\Gamma k^2 \varepsilon^{-d})$ where $\Gamma$ is the number of distinct collections of groups that a point may belong to, and for the $k$-means objective, we show how to construct an $\varepsilon$-coreset of size $O(\Gamma k^3\varepsilon^{-d-1})$.
Fair Classification with Noisy Protected Attributes: A Framework with Provable Guarantees
We present an optimization framework for learning a fair classifier in the presence of noisy perturbations in the protected attributes.
Coresets for Regressions with Panel Data
We first define coresets for several variants of regression problems with panel data and then present efficient algorithms to construct coresets of size that depend polynomially on 1/$\varepsilon$ (where $\varepsilon$ is the error parameter) and the number of regression parameters - independent of the number of individuals in the panel data or the time units each individual is observed for.
Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching
As such, the agent can finish inlier matching timely when the affinity score stops growing, for which otherwise an additional parameter i. e. the number of inliers is needed to avoid matching outliers.
Clustering Aware Classification for Risk Prediction and Subtyping in Clinical Data
In data containing heterogeneous subpopulations, classification performance benefits from incorporating the knowledge of cluster structure in the classifier.
$m$-mix: Generating hard negatives via multiple samples mixing for contrastive learning
Negative pairs are essential in contrastive learning, which plays the role of avoiding degenerate solutions.
Coresets for Time Series Clustering
In particular, we consider the setting where the time series data on $N$ entities is generated from a Gaussian mixture model with autocorrelations over $k$ clusters in $\mathbb{R}^d$.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
Coresets for Vertical Federated Learning: Regularized Linear Regression and $K$-Means Clustering
Vertical federated learning (VFL), where data features are stored in multiple parties distributively, is an important area in machine learning.
Subset Selection Based On Multiple Rankings in the Presence of Bias: Effectiveness of Fairness Constraints for Multiwinner Voting Score Functions
We consider the problem of subset selection where one is given multiple rankings of items and the goal is to select the highest ``quality'' subset.
A Hierarchical Destroy and Repair Approach for Solving Very Large-Scale Travelling Salesman Problem
Fair comparisons based on nineteen famous large-scale instances (with 10, 000 to 10, 000, 000 cities) show that HDR is highly competitive against existing state-of-the-art TSP algorithms, in terms of both efficiency and solution quality.
MuraNet: Multi-task Floor Plan Recognition with Relation Attention
The recognition of information in floor plan data requires the use of detection and segmentation models.
