no code implementations • 5 Feb 2024 • Sruthi Gorantla, Sara Ahmadian
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 $p, q \geq 1$.
1 code implementation • 25 Aug 2023 • Sruthi Gorantla, Eshaan Bhansali, Amit Deshpande, Anand Louis
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.
no code implementations • 21 Jun 2023 • Sruthi Gorantla, Anay Mehrotra, Amit Deshpande, Anand Louis
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.
no code implementations • 22 Aug 2022 • Sruthi Gorantla, Kishen N. Gowda, Amit Deshpande, Anand Louis
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.
2 code implementations • 2 Mar 2022 • Sruthi Gorantla, Amit Deshpande, Anand Louis
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.
2 code implementations • 24 Sep 2020 • Sruthi Gorantla, Amit Deshpande, Anand Louis
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.
no code implementations • NeurIPS Workshop Neuro_AI 2019 • Sruthi Gorantla, Anand Louis, Christos H. Papadimitriou, Santosh Vempala, Naganand Yadati
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.
no code implementations • 22 Feb 2019 • Rajiv Bajpai, Devamanyu Hazarika, Kunal Singh, Sruthi Gorantla, Erik Cambria, Roger Zimmerman
With the multitude of companies and organizations abound today, ranking them and choosing one out of the many is a difficult and cumbersome task.
1 code implementation • COLING 2018 • Devamanyu Hazarika, Soujanya Poria, Sruthi Gorantla, Erik Cambria, Roger Zimmermann, Rada Mihalcea
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)