The Primal-Dual method for Learning Augmented Algorithms
Authors: Etienne Bamas, Andreas Maggiori, Ola Svensson
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experimental results that confirm the theoretical analysis of Algorithm 6 for the TCP acknowledgement problem. The code is publicly available at https://github.com/etienne4/ PDLA. We experiment on various types of distribution for packet arrival inputs. |
| Researcher Affiliation | Academia | Etienne Bamas EPFL, Lausanne, Switzerland etienne.bamas@epfl.ch Andreas Maggiori EPFL, Lausanne, Switzerland andreas.maggiori@epfl.ch Ola Svensson EPFL, Lausanne, Switzerland ola.svensson@epfl.ch |
| Pseudocode | Yes | Algorithm 1 PRIMAL DUAL METHOD FOR ONLINE WEIGHTED SET COVER [1]. (...) Algorithm 2 PDLA FOR ONLINE WEIGHTED SET COVER. (...) |
| Open Source Code | Yes | The code is publicly available at https://github.com/etienne4/ PDLA. |
| Open Datasets | No | In all our instances, we set the subdivision parameter d to 100 which means that every second is split into 100 time units. Then we define an array of length 1000 where the i-th entry defines how many requests arrive at the i-th time step. Each entry in the array is drawn independently from the others from a distribution D. In the case of a Poisson distribution, we set D = P(1) (the Poisson distribution of mean 1). For the Pareto distribution, we choose D to be the Lomax distribution (which is a special case of Pareto distribution) with shape parameter set to 2 ([29]). |
| Dataset Splits | No | The paper describes input distributions and a method for generating noisy predictions but does not specify traditional training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for its experiments. |
| Software Dependencies | No | The paper mentions that code is publicly available but does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | In all our instances, we set the subdivision parameter d to 100 which means that every second is split into 100 time units. Then we define an array of length 1000 where the i-th entry defines how many requests arrive at the i-th time step. Each entry in the array is drawn independently from the others from a distribution D. (...) We introduce a replacement rate p [0, 1]. Then we go through the instance generated according to some distribution D and for each each entry at index 1 i 1000, with probability p we set this entry to 0 (i.e. we delete this entry) and with probability p we add to this entry a random variable Y D. (...) We then test our algorithm with 4 different values of robustness parameter λ {1, 0.8, 0.6, 0.4}. |