Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization
Authors: Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli, Prateek Jain
ICML 2021 | Venue PDF | LLM Run Details | Input Tokens: 17,739 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,569 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present simulations in Section 5 that demonstrate the regret bounds on simple synthetic problems. |
| Researcher Affiliation | Industry | 1Microsoft Research, New York City 2Microsoft Research, India 3The authors are currently at Google Research, India. |
| Pseudocode | Yes | Algorithm 1 OPTPBCO |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | We present synthetic experiments that showcase the regret bounds established in Section 4. We work with a linear gt for all the experiments. We fix W = Bd(1), context vectors from { xt 2 1}, and the two loss functions (a) ft(w) = ( w, xt y t )2 where y t = w , xt , for a fixed w Bd(1), and (b) ft(w) = | w, xt y t |. |
| Dataset Splits | No | The paper describes generating synthetic data and averaging over '50 problem instances' but does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper mentions running simulations but does not provide specific hardware details such as GPU/CPU models or memory. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers used for the experiments. |
| Experiment Setup | Yes | We work with a linear gt for all the experiments. We fix W = Bd(1), context vectors from { xt 2 1}, and the two loss functions (a) ft(w) = ( w, xt y t )2 where y t = w , xt , for a fixed w Bd(1), and (b) ft(w) = | w, xt y t |. Additionally, Algorithm 1 specifies parameters: 'Run Kernelized Exponential Weights for PBCO (Algorithm 2) with η = d log(L T) / BT' and 'Run Online Gradient Descent for PBCO (Algorithm 3) with η = W δ / DC T , δ = W DC / 3L 1/2 , α = δ, and T'. |