27 citations · 28 across the 8 of their papers we have counts for
9 papers
Decomposition of Evidence, Contradiction, and Fragility in Perturbation Responses
Lei You
Perturbation methods explain model decisions by measuring prediction changes under altered inputs, but response magnitude tells us only how much a model reacts, not what that react…
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…
Preventing the Collapse of Peer Review Requires Verification-First AI
Lei You, Lele Cao, Iryna Gurevych
This paper argues that AI-assisted peer review should be verification-first rather than review-mimicking. We propose truth-coupling, i.e. how tightly venue scores track latent scie…
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)…
Distributional Counterfactual Explanations With Optimal Transport
Lei You, Lele Cao, Mattias Nilsson +2
Counterfactual explanations (CE) are the de facto method for providing insights into black-box decision-making models by identifying alternative inputs that lead to different outco…