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..
Online Learning for Load Balancing of Unknown Monotone Resource Allocation Games
Authors: Ilai Bistritz, Nicholas Bambos
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we provide numerical simulations that include the two applications discussed in Section 5. In the first two experiments, to simulate a vanishing estimation error (Definition 3), at iteration t, Gaussian noise with mean δt = 0.25 (t+1)0.4 and variance σ2 = 1 4 was added to the gradients of each player. To quantify the effectiveness of our algorithm, we compare it to the uncontrolled system where αk = 0 for all k. For all experiments, we used the step-size sequence ηt = η0 (t+1)p and the control step-size sequence εt = ε0 (t+1)q with different values of η0, ε0. We ran 100 realizations for each experiment and plotted the average result along with the standard deviation region, which was always small. |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering, Stanford University. Correspondence to: Ilai Bistritz <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Online Load Balancing with Bandit Feedback Initialization: Let x0 X and α0 RK + . Let {ηt} , {εt} satisfy the conditions of Theorem 1. Input: Target total load vector l . For each turn t 1 do 1. Each player n updates its action using gn,t 1 and αt 1 to approximate xnun (x; α): xn,t = ΠXn xn,t 1 + ηt 1 gn,t 1 αt 1 (5) where ΠXn is the Euclidean projection into Xn. 2. The manager observes PN n=1 xk n,t for each k. 3. The manager updates the pricing coefficients using αt 1 + εt 1 PN n=1 xn,t l !#+ where [x]+ = max {x, 0} (element-wise). |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the methodology, nor does it include links to a code repository. |
| Open Datasets | No | The paper describes setting up simulations with randomly generated parameters and conditions (e.g., “The location y1 n of transmitter n was chosen uniformly at random on a 2D square of area 2N.”), rather than utilizing or providing access information for a publicly available dataset. |
| Dataset Splits | No | The paper performs numerical simulations and refers to “100 realizations” but does not specify any training, validation, or test dataset splits or percentages. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the simulations or experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers, such as programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | Yes | For all experiments, we used the step-size sequence ηt = η0 (t+1)p and the control step-size sequence εt = ε0 (t+1)q with different values of η0, ε0. |