Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Cross-regularization: Adaptive Model Complexity through Validation Gradients
Authors: Carlos Stein Brito
ICML 2025 | Venue PDF | LLM Run Details | Input Tokens: 19,023 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 1,633 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The empirical results in Figure 1(A-C) show crossregularization converging to optimally tuned ridge regression through direct gradient descent. ... Figure 2. Noise dynamics in VGG-16 on CIFAR-10 reveal architectural regularization patterns. ... Figures 10, 11 and 12 show the detailed plots for the systematic method analyses. |
| Researcher Affiliation | Industry | 1Night City Labs, Lisbon, Portugal. Correspondence to: Carlos Stein Brito <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Cross-regularization Training |
| Open Source Code | No | The paper does not provide concrete access to source code. It does not contain a specific repository link, an explicit code release statement, or code in supplementary materials. |
| Open Datasets | Yes | Figure 2. Noise dynamics in VGG-16 on CIFAR-10 reveal architectural regularization patterns. ... We evaluate L1 cross-regularization on the diabetes regression dataset (Efron et al., 2004). ... Figure 4. Dataset growth adaptation and adaptive augmentation. ... A: Performance evolution shows successful knowledge transfer at epoch 100 transition from partial to full dataset. B: Total regularization strength automatically adapts stronger regularization compensates for limited initial data, then decreases as full dataset provides natural regularization. Vertical line marks dataset transition. C: Evolution of learned augmentation parameters on SVHN. |
| Dataset Splits | Yes | We evaluate L1 cross-regularization on the diabetes regression dataset (Efron et al., 2004). The dataset consists of 442 patients with 10 physiological features. Data is standardized and split 80/20 into train/validation sets. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper mentions 'Adam optimizer' but does not specify version numbers for any programming languages, libraries, or frameworks used in the implementation. |
| Experiment Setup | Yes | C.3. Training Protocol. Optimization settings: Adam optimizer, Learning rates: 10-4 (model), 10-1 (noise), Initialization: log σ = 3, Batch size: 512, Training epochs: 100. |