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Found 26 papers, 4 papers with code
Autoencoder-based Graph Construction for Semi-supervised Learning
We consider graph-based semi-supervised learning that leverages a similarity graph across data points to better exploit data structure exposed in unlabeled data.
Improving Fair Training under Correlation Shifts
First, we analytically show that existing in-processing fair algorithms have fundamental limits in accuracy and group fairness.
Equal Experience in Recommender Systems
We explore the fairness issue that arises in recommender systems.
Matrix Completion with Hierarchical Graph Side Information
We consider a matrix completion problem that exploits social or item similarity graphs as side information.
Sample Selection for Fair and Robust Training
In this work, we propose a sample selection-based algorithm for fair and robust training.
A Fair Generative Model Using Total Variation Distance
We explore a fairness-related challenge that arises in generative models.
On the Fundamental Limits of Matrix Completion: Leveraging Hierarchical Similarity Graphs
In the achievability proof, we demonstrate that probability of error of the maximum likelihood estimator vanishes for sufficiently large number of users and items, if all sufficient conditions are satisfied.
FairBatch: Batch Selection for Model Fairness
We address this problem via the lens of bilevel optimization.
A Fair Classifier Using Kernel Density Estimation
As machine learning becomes prevalent in a widening array of sensitive applications such as job hiring and criminal justice, one critical aspect that machine learning classifiers should respect is to ensure fairness: guaranteeing the irrelevancy of a prediction output to sensitive attributes such as gender and race.
MC2G: An Efficient Algorithm for Matrix Completion with Social and Item Similarity Graphs
In this paper, we design and analyze MC2G (Matrix Completion with 2 Graphs), an algorithm that performs matrix completion in the presence of social and item similarity graphs.
FR-Train: A Mutual Information-Based Approach to Fair and Robust Training
Trustworthy AI is a critical issue in machine learning where, in addition to training a model that is accurate, one must consider both fair and robust training in the presence of data bias and poisoning.
Community Detection and Matrix Completion with Social and Item Similarity Graphs
We consider the problem of recovering a binary rating matrix as well as clusters of users and items based on a partially observed matrix together with side-information in the form of social and item similarity graphs.
FR-GAN: Fair and Robust Training
We consider the problem of fair and robust model training in the presence of data poisoning.
Match prediction from group comparison data using neural networks
As existing state-of-the-art algorithms are tailored to certain statistical models, we have different best algorithms across distinct scenarios.
Wasserstein GAN Can Perform PCA
Generative Adversarial Networks (GANs) have become a powerful framework to learn generative models that arise across a wide variety of domains.
Binary Rating Estimation with Graph Side Information
Considering a simple correlation model between a rating matrix and a graph, we characterize the sharp threshold on the number of observed entries required to recover the rating matrix (called the optimal sample complexity) as a function of the quality of graph side information (to be detailed).
Hypergraph Spectral Clustering in the Weighted Stochastic Block Model
Our main contribution lies in performance analysis of the poly-time algorithms under a random hypergraph model, which we name the weighted stochastic block model, in which objects and multi-way measures are modeled as nodes and weights of hyperedges, respectively.
Simulated+Unsupervised Learning With Adaptive Data Generation and Bidirectional Mappings
Recently, Shrivastava et al. (2017) propose Simulated+Unsupervised (S+U) learning: It first learns a mapping from synthetic data to real data, translates a large amount of labeled synthetic data to the ones that resemble real data, and then trains a learning model on the translated data.
Optimal Sample Complexity of M-wise Data for Top-K Ranking
As our result, we characterize the minimax optimality on the sample size for top-K ranking.
Community Recovery in Hypergraphs
The objective of the problem is to cluster data points into distinct communities based on a set of measurements, each of which is associated with the values of a certain number of data points.
Active Learning for Top-$K$ Rank Aggregation from Noisy Comparisons
We explore an active top-$K$ ranking problem based on pairwise comparisons that are collected possibly in a sequential manner as per our design choice.
Top-$K$ Ranking from Pairwise Comparisons: When Spectral Ranking is Optimal
First, in a general comparison model where item pairs to compare are given a priori, we attain an upper and lower bound on the sample size for reliable recovery of the top-$K$ ranked items.
Adversarial Top-$K$ Ranking
We study the top-$K$ ranking problem where the goal is to recover the set of top-$K$ ranked items out of a large collection of items based on partially revealed preferences.
Community Recovery in Graphs with Locality
Motivated by applications in domains such as social networks and computational biology, we study the problem of community recovery in graphs with locality.
Spectral MLE: Top-$K$ Rank Aggregation from Pairwise Comparisons
To approach this minimax limit, we propose a nearly linear-time ranking scheme, called \emph{Spectral MLE}, that returns the indices of the top-$K$ items in accordance to a careful score estimate.
Information Recovery from Pairwise Measurements
In particular, our results isolate a family of \emph{minimum} \emph{channel divergence measures} to characterize the degree of measurement corruption, which together with the size of the minimum cut of $\mathcal{G}$ dictates the feasibility of exact information recovery.
