35 citations · 39 across the 3 of their papers we have counts for
6 papers
Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions
Harrie Oosterhuis, Lijun Lyu, Avishek Anand
Local feature selection in machine learning provides instance-specific explanations by focusing on the most relevant features for each prediction, enhancing the interpretability of…
Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank?
Lijun Lyu, Nirmal Roy, Harrie Oosterhuis +1
Neural ranking models have become increasingly popular for real-world search and recommendation systems in recent years. Unlike their tree-based counterparts, neural models are muc…
Explainable Information Retrieval: A Survey
Avishek Anand, Lijun Lyu, Maximilian Idahl +3
Explainable information retrieval is an emerging research area aiming to make transparent and trustworthy information retrieval systems. Given the increasing use of complex machine…
BERT Rankers are Brittle: a Study using Adversarial Document Perturbations
Yumeng Wang, Lijun Lyu, Avishek Anand
Contextual ranking models based on BERT are now well established for a wide range of passage and document ranking tasks. However, the robustness of BERT-based ranking models under…
Towards Benchmarking the Utility of Explanations for Model Debugging
Maximilian Idahl, Lijun Lyu, Ujwal Gadiraju +1
Post-hoc explanation methods are an important class of approaches that help understand the rationale underlying a trained model's decision. But how useful are they for an end-user…
Neural OCR Post-Hoc Correction of Historical Corpora
Lijun Lyu, Maria Koutraki, Martin Krickl +1
Optical character recognition (OCR) is crucial for a deeper access to historical collections. OCR needs to account for orthographic variations, typefaces, or language evolution (i.…