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110 Results
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Poster
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Tue 14:00 |
Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology Syed Ashar Javed · Dinkar Juyal · Harshith Padigela · Amaro Taylor-Weiner · Limin Yu · Aaditya Prakash |
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Poster
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Thu 9:00 |
Learning to Scaffold: Optimizing Model Explanations for Teaching Patrick Fernandes · Marcos Treviso · Danish Pruthi · André Martins · Graham Neubig |
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Poster
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Tue 14:00 |
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations Tessa Han · Suraj Srinivas · Himabindu Lakkaraju |
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Poster
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Wed 9:00 |
Explaining Preferences with Shapley Values Robert Hu · Siu Lun Chau · Jaime Ferrando Huertas · Dino Sejdinovic |
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Poster
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Thu 9:00 |
GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games Shichang Zhang · Yozen Liu · Neil Shah · Yizhou Sun |
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Poster
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Wed 14:00 |
ProtoX: Explaining a Reinforcement Learning Agent via Prototyping Ronilo Ragodos · Tong Wang · Qihang Lin · Xun Zhou |
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Poster
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Tue 9:00 |
Text Classification with Born's Rule Emanuele Guidotti · Alfio Ferrara |
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Poster
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Wed 9:00 |
Robust Feature-Level Adversaries are Interpretability Tools Stephen Casper · Max Nadeau · Dylan Hadfield-Menell · Gabriel Kreiman |
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Poster
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Tue 14:00 |
Harmonizing the object recognition strategies of deep neural networks with humans Thomas FEL · Ivan F Rodriguez Rodriguez · Drew Linsley · Thomas Serre |
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Poster
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Wed 14:00 |
Decision Trees with Short Explainable Rules Victor Feitosa Souza · Ferdinando Cicalese · Eduardo Laber · Marco Molinaro |
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Poster
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Thu 14:00 |
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability Roman Levin · Manli Shu · Eitan Borgnia · Furong Huang · Micah Goldblum · Tom Goldstein |
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Poster
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Tue 14:00 |
Consistent Sufficient Explanations and Minimal Local Rules for explaining the decision of any classifier or regressor Salim I. Amoukou · Nicolas Brunel |