27 citations · 28 across the 8 of their papers we have counts for
6 papers · 1 filter
Best-of-Evidence: Best-of-N Selection under Partial Verification
Cenwei Zhang, Teng Fang, Yuxia Wang +3
BoN improves model outputs by sampling several candidates and selecting one with a proxy score, but it assumes that complete candidates can be evaluated reliably. Many vision-langu…
DISCOVER: A Solver for Distributional Counterfactual Explanations
Yikai Gu, Lele Cao, Bo Zhao +2
Counterfactual explanations (CE) explain model decisions by identifying input modifications that lead to different predictions. Most existing methods operate at the instance level.…
From Baselines to Transport Geodesics: Axiomatic Attribution via Optimal Generative Flows
Cenwei Zhang, Lin Zhu, Manxi Lin +1
Feature attributions often hide a critical modeling choice: they explain a prediction along a counterfactual path from a reference state to an input. Different baselines, interpola…
xai-cola: A Python library for sparsifying counterfactual explanations
Lin Zhu, Lei You
Counterfactual explanation (CE) is an important domain within post-hoc explainability. However, the explanations generated by most CE generators are often highly redundant. This wo…
Joint Distribution-Informed Shapley Values for Sparse Counterfactual Explanations
Lei You, Yijun Bian, Lele Cao
Counterfactual explanations (CE) aim to reveal how small input changes flip a model's prediction, yet many methods modify more features than necessary, reducing clarity and actiona…
Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask
Zineb Senane, Lele Cao, Valentin Leonhard Buchner +6
Time Series Representation Learning (TSRL) focuses on generating informative representations for various Time Series (TS) modeling tasks. Traditional Self-Supervised Learning (SSL)…