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Found 14 papers, 6 papers with code
Optimizing Group-Fair Plackett-Luce Ranking Models for Relevance and Ex-Post Fairness
Previous works have proposed efficient algorithms to train stochastic ranking models that achieve fairness of exposure to the groups ex-ante (or, in expectation), which may not guarantee representation fairness to the groups ex-post, that is, after realizing a ranking from the stochastic ranking model.
Sampling Individually-Fair Rankings that are Always Group Fair
Fair ranking tasks, which ask to rank a set of items to maximize utility subject to satisfying group-fairness constraints, have gained significant interest in the Algorithmic Fairness, Information Retrieval, and Machine Learning literature.
Socially Fair Center-based and Linear Subspace Clustering
Center-based clustering (e. g., $k$-means, $k$-medians) and clustering using linear subspaces are two most popular techniques to partition real-world data into smaller clusters.
Individual Fairness under Varied Notions of Group Fairness in Bipartite Matching - One Framework to Approximate Them All
Our first result is a polynomial-time algorithm that computes a distribution over `group-fair' matchings such that the individual fairness constraints are approximately satisfied and the expected size of a matching is close to OPT.
Sampling Ex-Post Group-Fair Rankings
Our second random walk-based algorithm samples ex-post group-fair rankings from a distribution $\delta$-close to $D$ in total variation distance and has expected running time $O^*(k^2\ell^2)$, when there is a sufficient gap between the given upper and lower bounds on the group-wise representation.
On the Problem of Underranking in Group-Fair Ranking
We give a fair ranking algorithm that takes any given ranking and outputs another ranking with simultaneous underranking and group fairness guarantees comparable to the lower bound we prove.
Robust Identifiability in Linear Structural Equation Models of Causal Inference
First, for a large and well-studied class of LSEMs, namely ``bow free'' models, we provide a sufficient condition on model parameters under which robust identifiability holds, thereby removing the restriction of paths required by prior work.
HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs
In many real-world network datasets such as co-authorship, co-citation, email communication, etc., relationships are complex and go beyond pairwise.
Biologically Plausible Neural Networks via Evolutionary Dynamics and Dopaminergic Plasticity
Artificial neural networks (ANNs) lack in biological plausibility, chiefly because backpropagation requires a variant of plasticity (precise changes of the synaptic weights informed by neural events that occur downstream in the neural circuit) that is profoundly incompatible with the current understanding of the animal brain.
Graph Neural Networks for Soft Semi-Supervised Learning on Hypergraphs
In this paper, we explore GNNs for graph-based SSL of histograms.
Stability of Linear Structural Equation Models of Causal Inference
First we prove that under a sufficient condition, for a certain sub-class of $\LSEM$ that are \emph{bow-free} (Brito and Pearl (2002)), the parameter recovery is stable.
Link Prediction in Hypergraphs using Graph Convolutional Networks
Additionally, there is need to represent the direction from reactants to products.
HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs
In many real-world network datasets such as co-authorship, co-citation, email communication, etc., relationships are complex and go beyond pairwise.
On Euclidean $k$-Means Clustering with $α$-Center Proximity
On the hardness side we show that for any $\alpha' > 1$, there exists an $\alpha \leq \alpha'$, $(\alpha >1)$, and an $\varepsilon_0 > 0$ such that minimizing the $k$-means objective over clusterings that satisfy $\alpha$-center proximity is NP-hard to approximate within a multiplicative $(1+\varepsilon_0)$ factor.
