18 citations · 62 across the 12 of their papers we have counts for
13 papers
Progressive Inference: Explaining Decoder-Only Sequence Classification Models Using Intermediate Predictions
Sanjay Kariyappa, Freddy Lécué, Saumitra Mishra +3
This paper proposes Progressive Inference - a framework to compute input attributions to explain the predictions of decoder-only sequence classification models. Our work is based o…
Counterfactual Metarules for Local and Global Recourse
Tom Bewley, Salim I. Amoukou, Saumitra Mishra +2
We introduce T-CREx, a novel model-agnostic method for local and global counterfactual explanation (CE), which summarises recourse options for both individuals and groups in the fo…
On the Connection between Game-Theoretic Feature Attributions and Counterfactual Explanations
Emanuele Albini, Shubham Sharma, Saumitra Mishra +2
Explainable Artificial Intelligence (XAI) has received widespread interest in recent years, and two of the most popular types of explanations are feature attributions, and counterf…
GLOBE-CE: A Translation-Based Approach for Global Counterfactual Explanations
Dan Ley, Saumitra Mishra, Daniele Magazzeni
Counterfactual explanations have been widely studied in explainability, with a range of application dependent methods prominent in fairness, recourse and model understanding. The m…
Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees
Faisal Hamman, Erfaun Noorani, Saumitra Mishra +2
There is an emerging interest in generating robust counterfactual explanations that would remain valid if the model is updated or changed even slightly. Towards finding robust coun…
Symbolic Metamodels for Interpreting Black-boxes Using Primitive Functions
Mahed Abroshan, Saumitra Mishra, Mohammad Mahdi Khalili
One approach for interpreting black-box machine learning models is to find a global approximation of the model using simple interpretable functions, which is called a metamodel (a…
CLEAR: Generative Counterfactual Explanations on Graphs
Jing Ma, Ruocheng Guo, Saumitra Mishra +2
Counterfactual explanations promote explainability in machine learning models by answering the question "how should an input instance be perturbed to obtain a desired predicted lab…
Robust Counterfactual Explanations for Tree-Based Ensembles
Sanghamitra Dutta, Jason Long, Saumitra Mishra +2
Counterfactual explanations inform ways to achieve a desired outcome from a machine learning model. However, such explanations are not robust to certain real-world changes in the u…
Global Counterfactual Explanations: Investigations, Implementations and Improvements
Dan Ley, Saumitra Mishra, Daniele Magazzeni
Counterfactual explanations have been widely studied in explainability, with a range of application dependent methods emerging in fairness, recourse and model understanding. Howeve…
A Survey on the Robustness of Feature Importance and Counterfactual Explanations
Saumitra Mishra, Sanghamitra Dutta, Jason Long +1
There exist several methods that aim to address the crucial task of understanding the behaviour of AI/ML models. Arguably, the most popular among them are local explanations that f…
Reliable Local Explanations for Machine Listening
Saumitra Mishra, Emmanouil Benetos, Bob L. Sturm +1
One way to analyse the behaviour of machine learning models is through local explanations that highlight input features that maximally influence model predictions. Sensitivity anal…
GAN-based Generation and Automatic Selection of Explanations for Neural Networks
Saumitra Mishra, Daniel Stoller, Emmanouil Benetos +2
One way to interpret trained deep neural networks (DNNs) is by inspecting characteristics that neurons in the model respond to, such as by iteratively optimising the model input (e…
A Study On Convolutional Neural Network Based End-To-End Replay Anti-Spoofing
Bhusan Chettri, Saumitra Mishra, Bob L. Sturm +1
The second Automatic Speaker Verification Spoofing and Countermeasures challenge (ASVspoof 2017) focused on "replay attack" detection. The best deep-learning systems to compete in…