Amit Dhurandhar

research

∙ 09/03/2023

Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation

Domain shift is a common problem in clinical applications, where the tra...

0 Jiajin Zhang, et al. ∙

research

∙ 12/01/2022

When Neural Networks Fail to Generalize? A Model Sensitivity Perspective

Domain generalization (DG) aims to train a model to perform well in unse...

16 Jiajin Zhang, et al. ∙

research

∙ 11/02/2022

On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach

Interpretable and explainable machine learning has seen a recent surge o...

0 Dennis Wei, et al. ∙

research

∙ 09/14/2022

PainPoints: A Framework for Language-based Detection of Chronic Pain and Expert-Collaborative Text-Summarization

Chronic pain is a pervasive disorder which is often very disabling and i...

0 Shreyas Fadnavis, et al. ∙

research

∙ 08/23/2022

Anomaly Attribution with Likelihood Compensation

This paper addresses the task of explaining anomalous predictions of a b...

0 Tsuyoshi Idé, et al. ∙

research

∙ 08/19/2022

Atomist or Holist? A Diagnosis and Vision for More Productive Interdisciplinary AI Ethics Dialogue

In response to growing recognition of the social, legal, and ethical imp...

0 Travis Greene, et al. ∙

research

∙ 06/22/2022

Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI

Recent years have seen a surge of interest in the field of explainable A...

15 Q Vera Liao, et al. ∙

research

∙ 02/08/2022

Local Explanations for Reinforcement Learning

Many works in explainable AI have focused on explaining black-box classi...

0 Ronny Luss, et al. ∙

research

∙ 02/02/2022

Analogies and Feature Attributions for Model Agnostic Explanation of Similarity Learners

Post-hoc explanations for black box models have been studied extensively...

0 Karthikeyan Natesan Ramamurthy, et al. ∙

research

∙ 02/02/2022

Auto-Transfer: Learning to Route Transferrable Representations

Knowledge transfer between heterogeneous source and target networks and ...

7 Keerthiram Murugesan, et al. ∙

research

∙ 01/28/2022

Locally Invariant Explanations: Towards Stable and Unidirectional Explanations through Local Invariant Learning

Locally interpretable model agnostic explanations (LIME) method is one o...

0 Amit Dhurandhar, et al. ∙

research

∙ 09/24/2021

AI Explainability 360: Impact and Design

As artificial intelligence and machine learning algorithms become increa...

46 Vijay Arya, et al. ∙

research

∙ 09/16/2021

Let the CAT out of the bag: Contrastive Attributed explanations for Text

Contrastive explanations for understanding the behavior of black box mod...

5 Saneem Chemmengath, et al. ∙

research

∙ 09/14/2021

Building Accurate Simple Models with Multihop

Knowledge transfer from a complex high performing model to a simpler and...

0 Amit Dhurandhar, et al. ∙

research

∙ 09/13/2021

Towards Better Model Understanding with Path-Sufficient Explanations

Feature based local attribution methods are amongst the most prevalent i...

0 Ronny Luss, et al. ∙

research

∙ 03/13/2021

Treatment Effect Estimation using Invariant Risk Minimization

Inferring causal individual treatment effect (ITE) from observational da...

19 Abhin Shah, et al. ∙

research

∙ 12/22/2020

Learning to Initialize Gradient Descent Using Gradient Descent

Non-convex optimization problems are challenging to solve; the success a...

0 Kartik Ahuja, et al. ∙

research

∙ 10/30/2020

Empirical or Invariant Risk Minimization? A Sample Complexity Perspective

Recently, invariant risk minimization (IRM) was proposed as a promising ...

5 Kartik Ahuja, et al. ∙

research

∙ 10/28/2020

Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions

Recently, invariant risk minimization (IRM) (Arjovsky et al.) was propos...

0 Kartik Ahuja, et al. ∙

research

∙ 10/15/2020

Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making

Several strands of research have aimed to bridge the gap between artific...

0 Charvi Rastogi, et al. ∙

research

∙ 03/12/2020

Model Agnostic Multilevel Explanations

In recent years, post-hoc local instance-level and global dataset-level ...

0 Karthikeyan Natesan Ramamurthy, et al. ∙

research

∙ 02/19/2020

Learning Global Transparent Models from Local Contrastive Explanations

There is a rich and growing literature on producing local point wise con...

0 Tejaswini Pedapati, et al. ∙

research

∙ 02/11/2020

Invariant Risk Minimization Games

The standard risk minimization paradigm of machine learning is brittle w...

0 Kartik Ahuja, et al. ∙

research

∙ 09/06/2019

One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques

As artificial intelligence and machine learning algorithms make further ...

36 Vijay Arya, et al. ∙

research

∙ 06/05/2019

Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning

Using machine learning in high-stakes applications often requires predic...

4 Noel C. F. Codella, et al. ∙

research

∙ 05/31/2019

Model Agnostic Contrastive Explanations for Structured Data

Recently, a method [7] was proposed to generate contrastive explanations...

11 Amit Dhurandhar, et al. ∙

research

∙ 05/30/2019

Leveraging Simple Model Predictions for Enhancing its Performance

There has been recent interest in improving performance of simple models...

0 Amit Dhurandhar, et al. ∙

research

∙ 05/29/2019

Generating Contrastive Explanations with Monotonic Attribute Functions

Explaining decisions of deep neural networks is a hot research topic wit...

9 Ronny Luss, et al. ∙

research

∙ 11/12/2018

TED: Teaching AI to Explain its Decisions

Artificial intelligence systems are being increasingly deployed due to t...

12 Noel C. F. Codella, et al. ∙

research

∙ 07/21/2018

Streaming Methods for Restricted Strongly Convex Functions with Applications to Prototype Selection

In this paper, we show that if the optimization function is restricted-s...

0 Karthik S. Gurumoorthy, et al. ∙

research

∙ 07/19/2018

Improving Simple Models with Confidence Profiles

In this paper, we propose a new method called ProfWeight for transferrin...

0 Amit Dhurandhar, et al. ∙

research

∙ 05/29/2018

Teaching Meaningful Explanations

The adoption of machine learning in high-stakes applications such as hea...

0 Noel C. F. Codella, et al. ∙

research

∙ 02/21/2018

Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives

In this paper we propose a novel method that provides contrastive explan...

0 Amit Dhurandhar, et al. ∙

research

∙ 07/12/2017

A Formal Framework to Characterize Interpretability of Procedures

We provide a novel notion of what it means to be interpretable, looking ...

0 Amit Dhurandhar, et al. ∙

research

∙ 07/05/2017

ProtoDash: Fast Interpretable Prototype Selection

In this paper we propose an efficient algorithm ProtoDash for selecting ...

0 Karthik S. Gurumoorthy, et al. ∙

research

∙ 06/09/2017

TIP: Typifying the Interpretability of Procedures

We provide a novel notion of what it means to be interpretable, looking ...

0 Amit Dhurandhar, et al. ∙

research

∙ 04/29/2017

Learning with Changing Features

In this paper we study the setting where features are added or change in...

0 Amit Dhurandhar, et al. ∙

research

∙ 06/19/2016

Building an Interpretable Recommender via Loss-Preserving Transformation

We propose a method for building an interpretable recommender system for...

0 Amit Dhurandhar, et al. ∙

Amit Dhurandhar

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