b'Amit Sharma'

research

∙ 06/27/2023

Large Language Models as Annotators: Enhancing Generalization of NLP Models at Minimal Cost

State-of-the-art supervised NLP models achieve high accuracy but are als...

0 Parikshit Bansal, et al. ∙

research

∙ 05/29/2023

Rethinking Counterfactual Data Augmentation Under Confounding

Counterfactual data augmentation has recently emerged as a method to mit...

0 Abbavaram Gowtham Reddy, et al. ∙

research

∙ 05/26/2023

Controlling Learned Effects to Reduce Spurious Correlations in Text Classifiers

To address the problem of NLP classifiers learning spurious correlations...

0 Parikshit Bansal, et al. ∙

research

∙ 04/28/2023

Causal Reasoning and Large Language Models: Opening a New Frontier for Causality

The causal capabilities of large language models (LLMs) is a matter of s...

0 Emre Kiciman, et al. ∙

research

∙ 10/22/2022

Counterfactual Generation Under Confounding

A machine learning model, under the influence of observed or unobserved ...

0 Abbavaram Gowtham Reddy, et al. ∙

research

∙ 10/07/2022

Using Interventions to Improve Out-of-Distribution Generalization of Text-Matching Recommendation Systems

Given a user's input text, text-matching recommender systems output rele...

0 Parikshit Bansal, et al. ∙

research

∙ 08/17/2022

The Counterfactual-Shapley Value: Attributing Change in System Metrics

Given an unexpected change in the output metric of a large-scale system,...

0 Amit Sharma, et al. ∙

research

∙ 07/08/2022

Probing Classifiers are Unreliable for Concept Removal and Detection

Neural network models trained on text data have been found to encode und...

8 Chenhao Tan, et al. ∙

research

∙ 06/15/2022

Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization

Real-world data collected from multiple domains can have multiple, disti...

0 Jivat Neet Kaur, et al. ∙

research

∙ 02/08/2022

Machine Explanations and Human Understanding

Explanations are hypothesized to improve human understanding of machine ...

0 Chacha Chen, et al. ∙

research

∙ 12/26/2021

Omitted Variable Bias in Machine Learned Causal Models

We derive general, yet simple, sharp bounds on the size of the omitted v...

4 Victor Chernozhukov, et al. ∙

research

∙ 11/24/2021

Causal Regularization Using Domain Priors

Neural networks leverage both causal and correlation-based relationships...

0 Abbavaram Gowtham Reddy, et al. ∙

research

∙ 10/28/2021

Sayer: Using Implicit Feedback to Optimize System Policies

We observe that many system policies that make threshold decisions invol...

0 Mathias Lecuyer, et al. ∙

research

∙ 10/07/2021

The Connection between Out-of-Distribution Generalization and Privacy of ML Models

With the goal of generalizing to out-of-distribution (OOD) data, recent ...

0 Divyat Mahajan, et al. ∙

research

∙ 12/21/2020

The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective

Training datasets for machine learning often have some form of missingne...

0 Naman Goel, et al. ∙

research

∙ 11/11/2020

Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions

For many kinds of interventions, such as a new advertisement, marketing ...

0 Yanbo Xu, et al. ∙

research

∙ 11/10/2020

Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End

To explain a machine learning model, there are two main approaches: feat...

0 Ramaravind K. Mothilal, et al. ∙

research

∙ 11/09/2020

DoWhy: An End-to-End Library for Causal Inference

In addition to efficient statistical estimators of a treatment's effect,...

0 Amit Sharma, et al. ∙

research

∙ 09/17/2020

Counterfactual Generation and Fairness Evaluation Using Adversarially Learned Inference

Recent studies have reported biases in machine learning image classifier...

12 Saloni Dash, et al. ∙

research

∙ 06/12/2020

Domain Generalization using Causal Matching

Learning invariant representations has been proposed as a key technique ...

0 Divyat Mahajan, et al. ∙

research

∙ 04/21/2020

Bursts of Activity: Temporal Patterns of Help-Seeking and Support in Online Mental Health Forums

Recent years have seen a rise in social media platforms that provide pee...

0 Taisa Kushner, et al. ∙

research

∙ 04/21/2020

Learnings from Technological Interventions in a Low Resource Language: A Case-Study on Gondi

The primary obstacle to developing technologies for low-resource languag...

0 Devansh Mehta, et al. ∙

research

∙ 12/06/2019

Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers

Explaining the output of a complex machine learning (ML) model often req...

0 Divyat Mahajan, et al. ∙

research

∙ 09/27/2019

Alleviating Privacy Attacks via Causal Learning

Machine learning models, especially deep neural networks have been shown...

0 Shruti Tople, et al. ∙

research

∙ 09/03/2019

Quantifying Infra-Marginality and Its Trade-off with Group Fairness

In critical decision-making scenarios, optimizing accuracy can lead to a...

0 Arpita Biswas, et al. ∙

research

∙ 07/10/2019

Quantifying Error in the Presence of Confounders for Causal Inference

Estimating average causal effect (ACE) is useful whenever we want to kno...

0 Rathin Desai, et al. ∙

research

∙ 05/19/2019

Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations

Post-hoc explanations of machine learning models are crucial for people ...

0 Ramaravind Kommiya Mothilal, et al. ∙

research

∙ 02/05/2019

Learning to Prescribe Interventions for Tuberculosis Patients using Digital Adherence Data

Digital Adherence Technologies (DATs) are an increasingly popular method...

14 Jackson A. Killian, et al. ∙

research

∙ 12/04/2018

Necessary and Probably Sufficient Test for Finding Valid Instrumental Variables

Can instrumental variables be found from data? While instrumental variab...

0 Amit Sharma, et al. ∙

research

∙ 11/28/2016

Split-door criterion for causal identification: Automatic search for natural experiments

Unobserved or unknown confounders complicate even the simplest attempts ...

0 Amit Sharma, et al. ∙

research

∙ 01/23/2014

Algorithms for Generating Ordered Solutions for Explicit AND/OR Structures

We present algorithms for generating alternative solutions for explicit ...

0 Priyankar Ghosh, et al. ∙

research

∙ 09/19/2012

Modeling and Verification of a Multi-Agent Argumentation System using NuSMV

Autonomous intelligent agent research is a domain situated at the forefr...

0 Supriya D'Souza, et al. ∙

Amit Sharma

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