21 citations · 32 across the 11 of their papers we have counts for
12 papers
Realizing LLMs' Causal Potential Requires Science-Grounded, Novel Benchmarks
Ashutosh Srivastava, Lokesh Nagalapatti, Gautam Jajoo +3
Recent claims of strong performance by Large Language Models (LLMs) on causal discovery are undermined by a key flaw: many evaluations rely on benchmarks likely included in pretrai…
From Search To Sampling: Generative Models For Robust Algorithmic Recourse
Prateek Garg, Lokesh Nagalapatti, Sunita Sarawagi
Algorithmic Recourse provides recommendations to individuals who are adversely impacted by automated model decisions, on how to alter their profiles to achieve a favorable outcome.…
Robust Root Cause Diagnosis using In-Distribution Interventions
Lokesh Nagalapatti, Ashutosh Srivastava, Sunita Sarawagi +1
Diagnosing the root cause of an anomaly in a complex interconnected system is a pressing problem in today's cloud services and industrial operations. We propose In-Distribution Int…
Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE
Lokesh Nagalapatti, Pranava Singhal, Avishek Ghosh +1
Treatment effect estimation involves assessing the impact of different treatments on individual outcomes. Current methods estimate Conditional Average Treatment Effect (CATE) using…
Tab-Shapley: Identifying Top-k Tabular Data Quality Insights
Manisha Padala, Lokesh Nagalapatti, Atharv Tyagi +2
We present an unsupervised method for aggregating anomalies in tabular datasets by identifying the top-k tabular data quality insights. Each insight consists of a set of anomalous…
PairNet: Training with Observed Pairs to Estimate Individual Treatment Effect
Lokesh Nagalapatti, Pranava Singhal, Avishek Ghosh +1
Given a dataset of individuals each described by a covariate vector, a treatment, and an observed outcome on the treatment, the goal of the individual treatment effect (ITE) estima…
Continuous Treatment Effect Estimation Using Gradient Interpolation and Kernel Smoothing
Lokesh Nagalapatti, Akshay Iyer, Abir De +1
We address the Individualized continuous treatment effect (ICTE) estimation problem where we predict the effect of any continuous-valued treatment on an individual using observatio…
Gradient Coreset for Federated Learning
Durga Sivasubramanian, Lokesh Nagalapatti, Rishabh Iyer +1
Federated Learning (FL) is used to learn machine learning models with data that is partitioned across multiple clients, including resource-constrained edge devices. It is therefore…
Game of Gradients: Mitigating Irrelevant Clients in Federated Learning
Lokesh Nagalapatti, Ramasuri Narayanam
The paradigm of Federated learning (FL) deals with multiple clients participating in collaborative training of a machine learning model under the orchestration of a central server.…
Data Quality Toolkit: Automatic assessment of data quality and remediation for machine learning datasets
Nitin Gupta, Hima Patel, Shazia Afzal +10
The quality of training data has a huge impact on the efficiency, accuracy and complexity of machine learning tasks. Various tools and techniques are available that assess data qua…
Data Augmentation for Personal Knowledge Base Population
Lingraj S Vannur, Balaji Ganesan, Lokesh Nagalapatti +2
Cold start knowledge base population (KBP) is the problem of populating a knowledge base from unstructured documents. While artificial neural networks have led to significant impro…
Outlier Aware Network Embedding for Attributed Networks
Sambaran Bandyopadhyay, Lokesh N, M. N. Murty
Attributed network embedding has received much interest from the research community as most of the networks come with some content in each node, which is also known as node attribu…