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..
Dealing With Unbounded Gradients in Stochastic Saddle-point Optimization
Authors: Gergely Neu, Nneka Okolo
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Besides providing a set of general results, we also apply our algorithm to a specific problem in reinforcement learning, where it leads to performance guarantees for finding near-optimal policies in an average-reward MDP without prior knowledge of the bias span. |
| Researcher Affiliation | Academia | 1Universitat Pompeu Fabra, Barcelona, Spain. Correspondence to: Gergely Neu <EMAIL>, Nneka Okolo <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 COMIDA-MDP |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-sourcing the code for the described methodology. |
| Open Datasets | No | The paper discusses applying the method to Average-Reward Markov Decision Processes (AMDPs) and mentions using 'a simulator (or generative model) of the transition function P', but it does not specify or provide access information for a public dataset for training. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts, or citations to predefined splits) needed to reproduce data partitioning for validation, as it is a theoretical paper. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers. |
| Experiment Setup | No | While the paper defines parameters for its theoretical algorithms and bounds (e.g., ϱx, ϱy, ηx, ηy), it does not describe a concrete experimental setup with hyperparameters or system-level training settings for an empirical evaluation. |