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Found 9 papers, 4 papers with code
CASCADE: Contextual Sarcasm Detection in Online Discussion Forums
The literature in automated sarcasm detection has mainly focused on lexical, syntactic and semantic-level analysis of text.
Ranked #1 on
Sarcasm Detection
on SARC (all-bal)
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.
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.
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.
Aspect-Sentiment Embeddings for Company Profiling and Employee Opinion Mining
With the multitude of companies and organizations abound today, ranking them and choosing one out of the many is a difficult and cumbersome task.
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.
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.
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.
Fair Active Ranking from Pairwise Preferences
Our proposed objective function asks to minimize the $\ell_q$ norm of the error of the groups, where the error of a group is the $\ell_p$ norm of the error of all the items within that group, for $p, q \geq 1$.
