18 citations · 62 across the 12 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…