Interaction-Grounded Learning with Action-Inclusive Feedback
Authors: Tengyang Xie, Akanksha Saran, Dylan J Foster, Lekan Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford
NeurIPS 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide theoretical guarantees and large-scale experiments based on supervised datasets to demonstrate the effectiveness of the new approach. |
| Researcher Affiliation | Collaboration | Tengyang Xie UIUC EMAIL Akanksha Saran Microsoft Research, NYC EMAIL Dylan J. Foster Microsoft Research, New England EMAIL Lekan Molu Microsoft Research, NYC EMAIL Ida Momennejad Microsoft Research, NYC EMAIL Nan Jiang UIUC EMAIL Paul Mineiro Microsoft Research, NYC EMAIL John Langford Microsoft Research, NYC EMAIL |
| Pseudocode | Yes | Algorithm 1 Action-inclusive IGL (AI-IGL) |
| Open Source Code | No | The paper does not contain an explicit statement about the release of its own source code, nor does it provide a link to a repository for the described methodology. It only mentions using existing open-source datasets and simulators. |
| Open Datasets | Yes | To verify that our proposed algorithm scales to a variety of tasks, we evaluate performance on more than 200 datasets from the publicly available Open ML Curated Classification Benchmarking Suite [Vanschoren et al., 2015; Casalicchio et al., 2019; Feurer et al., 2021; Bischl et al., 2021]. Open ML CC-18 datasets are licensed under CC-BY license2 and the platform and library are licensed under the BSD (3-Clause) license3. |
| Dataset Splits | Yes | We use 90% of the data for training and the remaining 10% for evaluation. |
| Hardware Specification | No | The paper does not specify any particular hardware details such as GPU models, CPU types, or memory amounts used for running the experiments. It only mentions general simulation environments. |
| Software Dependencies | No | The paper mentions using 'logistic regression with a linear representation' and the OpenML platform, but it does not provide specific version numbers for any software dependencies, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | All methods use logistic regression with a linear representation. At test time, each method takes the argmax of the policy. |