Demo Publications

@inproceedings{soton454952,

title = {Model agnostic interpretability for multiple instance learning},

author = {Joseph Early and Christine Evers and Sarvapali Ramchurn},

url = {https://eprints.soton.ac.uk/454952/},

year  = {2022},

date = {2022-01-01},

booktitle = {International Conference on Learning Representations 2022 (25/04/22 - 29/04/22)},

abstract = {In Multiple Instance Learning (MIL), models are trained using bags of instances, where only a single label is provided for each bag. A bag label is often only determined by a handful of key instances within a bag, making it difficult to interpret what information a classifier is using to make decisions. In this work, we establish the key requirements for interpreting MIL models. We then go on to develop several model-agnostic approaches that meet these requirements. Our methods are compared against existing inherently interpretable MIL models on several datasets, and achieve an increase in interpretability accuracy of up to 30%. We also examine the ability of the methods to identify interactions between instances and scale to larger datasets, improving their applicability to real-world problems.},

note = {25 pages (9 content, 2 acknowledgement + references, 14 appendix). 16 figures (3 main content, 13 appendix). Submitted and accepted to ICLR 22, see http://openreview.net/forum?id=KSSfF5lMIAg . Revision: added additional acknowledgements},

keywords = {cs.AI, cs.LG},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{soton454952,

title = {Model agnostic interpretability for multiple instance learning},

author = {Joseph Early and Christine Evers and Sarvapali Ramchurn},

url = {https://eprints.soton.ac.uk/454952/},

year  = {2022},

date = {2022-01-01},

booktitle = {International Conference on Learning Representations 2022 (25/04/22 - 29/04/22)},

abstract = {In Multiple Instance Learning (MIL), models are trained using bags of instances, where only a single label is provided for each bag. A bag label is often only determined by a handful of key instances within a bag, making it difficult to interpret what information a classifier is using to make decisions. In this work, we establish the key requirements for interpreting MIL models. We then go on to develop several model-agnostic approaches that meet these requirements. Our methods are compared against existing inherently interpretable MIL models on several datasets, and achieve an increase in interpretability accuracy of up to 30%. We also examine the ability of the methods to identify interactions between instances and scale to larger datasets, improving their applicability to real-world problems.},

note = {25 pages (9 content, 2 acknowledgement + references, 14 appendix). 16 figures (3 main content, 13 appendix). Submitted and accepted to ICLR 22, see http://openreview.net/forum?id=KSSfF5lMIAg . Revision: added additional acknowledgements},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}