Sampling the "Inverse Set" of a Neuron: An Approach to Understanding Neural Nets. - Suryabhan Singh Hada; Miguel Á. Carreira-Perpiñán (CoRR, 2019)

An Experimental Comparison of Old and New Decision Tree Algorithms. - Arman Zharmagambetov; Suryabhan Singh Hada; Miguel Á. Carreira-Perpiñán (CoRR, 2019)

Counterfactual Explanations for Oblique Decision Trees: Exact, Efficient Algorithms. - Miguel Á. Carreira-Perpiñán; Suryabhan Singh Hada (AAAI, 2021)

Sampling The "Inverse Set" of a Neuron. - Suryabhan Singh Hada; Miguel Á. Carreira-Perpiñán (ICIP, 2021)

Understanding And Manipulating Neural Net Features Using Sparse Oblique Classification Trees. - Suryabhan Singh Hada; Miguel Á. Carreira-Perpiñán; Arman Zharmagambetov (ICIP, 2021)

Non-Greedy Algorithms for Decision Tree Optimization: An Experimental Comparison. - Arman Zharmagambetov; Suryabhan Singh Hada; Magzhan Gabidolla; Miguel Á. Carreira-Perpiñán (IJCNN, 2021)

Exploring Counterfactual Explanations for Classification and Regression Trees. - Suryabhan Singh Hada; Miguel Á. Carreira-Perpiñán (PKDD/ECML Workshops (1), 2021)

Style Transfer by Rigid Alignment in Neural Net Feature Space. - Suryabhan Singh Hada; Miguel Á. Carreira-Perpiñán (WACV, 2021)

Interpretable Image Classification Using Sparse Oblique Decision Trees. - Suryabhan Singh Hada; Miguel Á. Carreira-Perpiñán (ICASSP, 2022)

Sparse Oblique Decision Trees: A Tool to Interpret Natural Language Processing Datasets. - Suryabhan Singh Hada; Miguel Á. Carreira-Perpiñán (IJCNN, 2022)

Approaches to Interpret Deep Neural Networks - Suryabhan Singh Hada (2022)

Very Fast, Approximate Counterfactual Explanations for Decision Forests. - Miguel Á. Carreira-Perpiñán; Suryabhan Singh Hada (AAAI, 2023)

Inverse classification with logistic and softmax classifiers: efficient optimization. - Miguel Á. Carreira-Perpiñán; Suryabhan Singh Hada (CoRR, 2023)

Sparse oblique decision trees: a tool to understand and manipulate neural net features. - Suryabhan Singh Hada; Miguel Á. Carreira-Perpiñán; Arman Zharmagambetov (Data Min. Knowl. Discov., 2024)