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..
Real-Time Bidding with Side Information
Authors: arthur flajolet, Patrick Jaillet
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We develop UCB-type algorithms that combine two streams of literature: the confidence-set approach to linear contextual MABs and the probabilistic bisection search method for stochastic root-finding. Under mild assumptions on the underlying unknown distribution, we establish distributionindependent regret bounds of order O(d T) when either B = or when B scales linearly with T. |
| Researcher Affiliation | Academia | Arthur Flajolet MIT, ORC EMAIL Patrick Jaillet MIT, EECS, LIDS, ORC EMAIL |
| Pseudocode | Yes | Algorithm 1: Interval updating procedure at the end of phase k |
| Open Source Code | No | The paper does not provide any statements about open-sourcing code or links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not describe the use of any datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not describe the use of any datasets or their splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe a concrete experimental setup with hyperparameters or training settings. |