Online learning with dynamics: A minimax perspective
Authors: Kush Bhatia, Karthik Sridharan
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main results provide sufficient conditions for online learnability for this setup with corresponding rates. The rates are characterized by: 1) a complexity term capturing the expressiveness of the underlying policy class under the dynamics of state change, and 2) a dynamic stability term measuring the deviation of the instantaneous loss from a certain counterfactual loss. Further, we provide matching lower bounds which show that both the complexity terms are indeed necessary. |
| Researcher Affiliation | Academia | Kush Bhatia EECS, UC Berkeley kush@cs.berkeley.edu Karthik Sridharan CS, Cornell University sridharan@cs.cornell.edu |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention providing open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not report on experiments using datasets for training. The examples section applies the theoretical framework to existing problem setups, but does not involve computational experiments with data. |
| Dataset Splits | No | The paper does not describe experimental validation or dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe a computational experimental setup with hyperparameters or system-level settings. |