Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Consistent Adversarially Robust Linear Classification: Non-Parametric Setting
Authors: Elvis Dohmatob
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present some empirical verification of our theoretical results. All experiments were run on a single modern CPU laptop. |
| Researcher Affiliation | Industry | Elvis Dohmatob 1 1Meta FAIR. Correspondence to: Elvis Dohmatob <EMAIL>. |
| Pseudocode | No | The paper describes the algorithm in text but does not provide a formal pseudocode block or a clearly labeled 'Algorithm' section with structured steps. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | No | The paper describes synthetic data generation processes (e.g., 'Consider the distribution P(y = 1) = 1/2, x | y N(yµ, Σ)' and 'z | y N(yµ, Id), x = max(z, 0)') rather than referencing or providing concrete access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper mentions generating training data ('Dn = {(x1, y1), . . . , (xn, yn)} of n iid samples') and evaluating adversarial risk, but it does not specify explicit train/validation/test splits, percentages, or a cross-validation methodology. |
| Hardware Specification | No | All experiments were run on a single modern CPU laptop. |
| Software Dependencies | No | We use trust-region-based methods (Absil et al., 2007) implemented in the Manopt library (Boumal et al., 2014). |
| Experiment Setup | Yes | We set the input-dimension to d = 20 for this experiment. For each value of sample size n {100, 200, . . . , 1000, 2000, 3000, . . . , 10000}, we generate a dataset Dn = {(x1, y1), . . . , (xn, yn)} of n iid samples, and then compute the estimator bfn,ϵ,hn described in Section 3.3, where hn is the bandwidth parameter, taken as hn = p (d/n) log n, in accordance with the choice in Corollary 4.1. We consider Euclidean-norm attacks of strength ϵ ranging in {0.1, 0.2, . . . , 0.9, 1}. |