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09/03/2023
Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation
Domain shift is a common problem in clinical applications, where the tra...
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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...
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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...
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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...
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08/23/2022
Anomaly Attribution with Likelihood Compensation
This paper addresses the task of explaining anomalous predictions of a b...
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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...
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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...
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02/08/2022
Local Explanations for Reinforcement Learning
Many works in explainable AI have focused on explaining black-box classi...
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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...
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02/02/2022
Auto-Transfer: Learning to Route Transferrable Representations
Knowledge transfer between heterogeneous source and target networks and ...
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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...
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09/24/2021
AI Explainability 360: Impact and Design
As artificial intelligence and machine learning algorithms become increa...
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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...
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09/14/2021
Building Accurate Simple Models with Multihop
Knowledge transfer from a complex high performing model to a simpler and...
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09/13/2021
Towards Better Model Understanding with Path-Sufficient Explanations
Feature based local attribution methods are amongst the most prevalent i...
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03/13/2021
Treatment Effect Estimation using Invariant Risk Minimization
Inferring causal individual treatment effect (ITE) from observational da...
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12/22/2020
Learning to Initialize Gradient Descent Using Gradient Descent
Non-convex optimization problems are challenging to solve; the success a...
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10/30/2020
Empirical or Invariant Risk Minimization? A Sample Complexity Perspective
Recently, invariant risk minimization (IRM) was proposed as a promising ...
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10/28/2020
Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions
Recently, invariant risk minimization (IRM) (Arjovsky et al.) was propos...
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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...
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03/12/2020
Model Agnostic Multilevel Explanations
In recent years, post-hoc local instance-level and global dataset-level ...
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02/19/2020
Learning Global Transparent Models from Local Contrastive Explanations
There is a rich and growing literature on producing local point wise con...
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02/11/2020
Invariant Risk Minimization Games
The standard risk minimization paradigm of machine learning is brittle w...
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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 ...
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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...
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05/31/2019
Model Agnostic Contrastive Explanations for Structured Data
Recently, a method [7] was proposed to generate contrastive explanations...
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05/30/2019
Leveraging Simple Model Predictions for Enhancing its Performance
There has been recent interest in improving performance of simple models...
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05/29/2019
Generating Contrastive Explanations with Monotonic Attribute Functions
Explaining decisions of deep neural networks is a hot research topic wit...
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11/12/2018
TED: Teaching AI to Explain its Decisions
Artificial intelligence systems are being increasingly deployed due to t...
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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...
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07/19/2018
Improving Simple Models with Confidence Profiles
In this paper, we propose a new method called ProfWeight for transferrin...
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05/29/2018
Teaching Meaningful Explanations
The adoption of machine learning in high-stakes applications such as hea...
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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...
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07/12/2017
A Formal Framework to Characterize Interpretability of Procedures
We provide a novel notion of what it means to be interpretable, looking ...
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07/05/2017
ProtoDash: Fast Interpretable Prototype Selection
In this paper we propose an efficient algorithm ProtoDash for selecting ...
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06/09/2017
TIP: Typifying the Interpretability of Procedures
We provide a novel notion of what it means to be interpretable, looking ...
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04/29/2017
Learning with Changing Features
In this paper we study the setting where features are added or change in...
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06/19/2016
