Probabilistic Explanations for Linear Models
Authors: Bernardo Subercaseaux, Marcelo Arenas, Kuldeep S. Meel
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Formal XAI is an emerging field that focuses on providing explanations with mathematical guarantees for the decisions made by machine learning models. [...] We propose the notion of pδ, ϵq SR, a simple relaxation of δ-SRs, and show that this kind of explanations can be computed efficiently over linear models. [...] Theorem 2. Given a linear model L and an input x, we can compute a pδ, εq-min-SR successfully with probability 1 γ in time r O d ε2γ2 ; that is, polynomial in d, 1{ε, and 1{γ. |
| Researcher Affiliation | Collaboration | Bernardo Subercaseaux1, Marcelo Arenas2, 3, 4, Kuldeep S. Meel5, 6 1Carnegie Mellon University 2Pontificia Universidad Cat olica de Chile 3IMFD Chile 4Relational AI 5Georgia Institute of Technology 6University of Toronto |
| Pseudocode | Yes | Algorithm 1: Linear Monte Carlo Explainer Input: Linear model L, instance x, parameters δ P p0, 1q Parameter: ε P p0, 1q, γ P p0, 1q Output: A value δ P rδ ε, δ εs together with a minimum δ -SR explanation for x. [...] Algorithm 2: Monte Carlo Estimation Input: Linear model L, a partial instance y, an instance x, and a number of samples M Output: An estimate pv of Prz Upyqr Lpzq Lpxqs. |
| Open Source Code | No | The paper does not provide an explicit statement about the release of open-source code, a link to a repository, or mention of code in supplementary materials. |
| Open Datasets | No | The paper uses illustrative examples with synthetic linear models and instances (e.g., Example 1: 'Consider a binary classifier M defined as Mpxq px1 _ x3q px2 _ x1q px4 _ x3q , and the input instance x p1, 1, 0, 1q.'; Example 2: 'Consider a linear model L of dimension d 1000 with parameters t 1250 and w p1000, 1, 1, 1, 1, . . . , 1q.'). It does not refer to any publicly available or open datasets. |
| Dataset Splits | No | The paper does not use any external datasets, therefore, there is no mention of dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper discusses the theoretical runtime complexity of its algorithms but does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for conducting experiments. |
| Software Dependencies | No | The paper does not specify any particular software, libraries, or their version numbers that were used for implementation or experiments. |
| Experiment Setup | No | As a theoretical paper focusing on mathematical proofs and algorithm design, it does not describe an experimental setup including specific hyperparameter values, training configurations, or system-level settings. |