270 citations · 835 across the 84 of their papers we have counts for
8 papers · 1 filter
Differentially Private Distributed Data Summarization under Covariate Shift
Kanthi Sarpatwar, Karthikeyan Shanmugam, Venkata Sitaramagiridharganesh Ganapavarapu +2
We envision AI marketplaces to be platforms where consumers, with very less data for a target task, can obtain a relevant model by accessing many private data sources with vast num…
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen +17
As artificial intelligence and machine learning algorithms make further inroads into society, calls are increasing from multiple stakeholders for these algorithms to explain their…
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions
Matthew Faw, Rajat Sen, Karthikeyan Shanmugam +2
We consider a covariate shift problem where one has access to several different training datasets for the same learning problem and a small validation set which possibly differs fr…
Model Agnostic Contrastive Explanations for Structured Data
Amit Dhurandhar, Tejaswini Pedapati, Avinash Balakrishnan +3
Recently, a method [7] was proposed to generate contrastive explanations for differentiable models such as deep neural networks, where one has complete access to the model. In this…
Enhancing Simple Models by Exploiting What They Already Know
Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss
There has been recent interest in improving performance of simple models for multiple reasons such as interpretability, robust learning from small data, deployment in memory constr…
Leveraging Latent Features for Local Explanations
Ronny Luss, Pin-Yu Chen, Amit Dhurandhar +4
As the application of deep neural networks proliferates in numerous areas such as medical imaging, video surveillance, and self driving cars, the need for explaining the decisions…
Size of Interventional Markov Equivalence Classes in Random DAG Models
Dmitriy Katz, Karthikeyan Shanmugam, Chandler Squires +1
Directed acyclic graph (DAG) models are popular for capturing causal relationships. From observational and interventional data, a DAG model can only be determined up to its \emph{i…
ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery
Raj Agrawal, Chandler Squires, Karren Yang +2
Determining the causal structure of a set of variables is critical for both scientific inquiry and decision-making. However, this is often challenging in practice due to limited in…