Search Results for author: Sainyam Galhotra

Found 16 papers, 1 papers with code

A Vision for Semantically Enriched Data Science

no code implementations2 Mar 2023 Udayan Khurana, Kavitha Srinivas, Sainyam Galhotra, Horst Samulowitz

The recent efforts in automation of machine learning or data science has achieved success in various tasks such as hyper-parameter optimization or model selection.

Common Sense Reasoning Data Augmentation +1

Consistent Range Approximation for Fair Predictive Modeling

no code implementations21 Dec 2022 Jiongli Zhu, Sainyam Galhotra, Nazanin Sabri, Babak Salimi

This paper proposes a novel framework for certifying the fairness of predictive models trained on biased data.

Fairness Selection bias

Community Recovery in the Geometric Block Model

no code implementations22 Jun 2022 Sainyam Galhotra, Arya Mazumdar, Soumyabrata Pal, Barna Saha

We show that a simple triangle-counting algorithm to detect communities in the geometric block model is near-optimal.

Community Detection Stochastic Block Model

How to Design Robust Algorithms using Noisy Comparison Oracle

no code implementations12 May 2021 Raghavendra Addanki, Sainyam Galhotra, Barna Saha

Metric based comparison operations such as finding maximum, nearest and farthest neighbor are fundamental to studying various clustering techniques such as $k$-center clustering and agglomerative hierarchical clustering.

Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals

no code implementations22 Mar 2021 Sainyam Galhotra, Romila Pradhan, Babak Salimi

There has been a recent resurgence of interest in explainable artificial intelligence (XAI) that aims to reduce the opaqueness of AI-based decision-making systems, allowing humans to scrutinize and trust them.

Decision Making Explainable artificial intelligence

Learning to Generate Fair Clusters from Demonstrations

no code implementations8 Feb 2021 Sainyam Galhotra, Sandhya Saisubramanian, Shlomo Zilberstein

Empirical evaluation on three real-world datasets demonstrates the effectiveness of our approach in quickly identifying the underlying fairness and interpretability constraints, which are then used to generate fair and interpretable clusters.


Semantic Annotation for Tabular Data

no code implementations15 Dec 2020 Udayan Khurana, Sainyam Galhotra

We propose $C^2$, a column to concept mapper that is based on a maximum likelihood estimation approach through ensembles.

Data Integration Feature Engineering +1

Causal Feature Selection for Algorithmic Fairness

no code implementations10 Jun 2020 Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri, Kush R. Varshney

In this work, we consider fairness in the integration component of data management, aiming to identify features that improve prediction without adding any bias to the dataset.

Data Integration Fairness +1

Adaptive Rule Discovery for Labeling Text Data

no code implementations13 May 2020 Sainyam Galhotra, Behzad Golshan, Wang-Chiew Tan

At the same time, creating a labeled subset of the data can be costly and even infeasible in imbalanced settings.

Fair Correlation Clustering

no code implementations10 Feb 2020 Saba Ahmadi, Sainyam Galhotra, Barna Saha, Roy Schwartz

We consider two variations of fairness constraint for the problem of correlation clustering where each node has a color, and the goal is to form clusters that do not over-represent vertices of any color.


Balancing the Tradeoff Between Clustering Value and Interpretability

1 code implementation17 Dec 2019 Sandhya Saisubramanian, Sainyam Galhotra, Shlomo Zilberstein

The interpretability of the clusters is complemented by generating simple explanations denoting the feature values of the nodes in the clusters, using frequent pattern mining.

Graph Clustering

Lexicographically Ordered Multi-Objective Clustering

no code implementations2 Mar 2019 Sainyam Galhotra, Sandhya Saisubramanian, Shlomo Zilberstein

We introduce a rich model for multi-objective clustering with lexicographic ordering over objectives and a slack.

Connectivity in Random Annulus Graphs and the Geometric Block Model

no code implementations12 Apr 2018 Sainyam Galhotra, Arya Mazumdar, Soumyabrata Pal, Barna Saha

Our next contribution is in using the connectivity of random annulus graphs to provide necessary and sufficient conditions for efficient recovery of communities for {\em the geometric block model} (GBM).

Community Detection Stochastic Block Model

The Geometric Block Model

no code implementations16 Sep 2017 Sainyam Galhotra, Arya Mazumdar, Soumyabrata Pal, Barna Saha

To capture the inherent geometric features of many community detection problems, we propose to use a new random graph model of communities that we call a Geometric Block Model.

Community Detection Stochastic Block Model

Fairness Testing: Testing Software for Discrimination

no code implementations11 Sep 2017 Sainyam Galhotra, Yuriy Brun, Alexandra Meliou

This paper defines software fairness and discrimination and develops a testing-based method for measuring if and how much software discriminates, focusing on causality in discriminatory behavior.


Holistic Influence Maximization: Combining Scalability and Efficiency with Opinion-Aware Models

no code implementations9 Feb 2016 Sainyam Galhotra, Akhil Arora, Shourya Roy

In this paper, we propose a holistic solution to the influence maximization (IM) problem.

Social and Information Networks Databases H.2.8

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