Search Results for author: Anand Louis

Found 10 papers, 3 papers with code

Sampling Random Group Fair Rankings

no code implementations2 Mar 2022 Sruthi Gorantla, Amit Deshpande, Anand Louis

We experimentally validate the above guarantees of our algorithms for group fairness in top ranks and representation in every rank on real-world data sets.

Fairness

On the Problem of Underranking in Group-Fair Ranking

2 code implementations24 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.

Fairness Learning-To-Rank +1

Robust Identifiability in Linear Structural Equation Models of Causal Inference

no code implementations14 Jul 2020 Karthik Abinav Sankararaman, Anand Louis, Navin Goyal

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.

Causal Inference

HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs

1 code implementation NeurIPS 2019 Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, Partha Talukdar

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

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.

Stability of Linear Structural Equation Models of Causal Inference

no code implementations16 May 2019 Karthik Abinav Sankararaman, Anand Louis, Navin Goyal

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.

Causal Inference

HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs

1 code implementation7 Sep 2018 Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, Partha Talukdar

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

no code implementations28 Apr 2018 Amit Deshpande, Anand Louis, Apoorv Vikram Singh

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.

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