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..
Cautious Regret Minimization: Online Optimization with Long-Term Budget Constraints
Authors: Nikolaos Liakopoulos, Apostolos Destounis, Georgios Paschos, Thrasyvoulos Spyropoulos, Panayotis Mertikopoulos
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theoretical ๏ฌndings are also validated by a series of numerical experiments which suggest that increasing K that is, enlarging the window over which the budget must be balanced makes the K-benchmark guarantee tighter. and 5. Numerical results |
| Researcher Affiliation | Collaboration | 1Paris Research Center, Huawei Technologies, Paris, France 2EURECOM, Sophia-Antipolis, France 3Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LIG, Grenoble, France. |
| Pseudocode | Yes | Cautious Online Lagrangian Descent (COLD) For t = 1, . . . , T: xt 1 V f t 1(xt 1) + Q(t)g t 1(xt 1) 2 Q(t + 1) = [Q(t) + ฬgt(xt)]+. |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing the code for the described methodology, nor does it include any links to a code repository. |
| Open Datasets | No | We simulate a scenario with one website, where xt [0, ), ft(xt) = wtxt, and gt(xt) = ptxt b T /T, where wt, pt are generated by exponential distributions wt Exp(11) and pt Exp(10). This is a simulated environment, not a named public dataset with access info. |
| Dataset Splits | No | The paper describes a simulated scenario where data is generated. It does not mention explicit train/validation/test dataset splits. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., CPU, GPU models) used for running the experiments or simulations. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers used for the experiments. |
| Experiment Setup | Yes | We run the experiment for different horizons T = {2000, 4000, . . . , 10000}, budget b T = 300T, and parameters set to = max{T, V } and V = T 0.99 for each of the experiments. and we choose K = T 3/4 and V = {T 1/2, T 3/4, T 0.99, T 5/4} |