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..
Constant Regret, Generalized Mixability, and Mirror Descent
Authors: Zakaria Mhammedi, Robert C. Williamson
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrated the performance of this algorithm on football game predictions (see Appendix J). |
| Researcher Affiliation | Collaboration | Zakaria Mhammedi Research School of Computer Science Australian National University and DATA61 EMAIL Robert C. Williamson Research School of Computer Science Australian National University and DATA61 EMAIL |
| Pseudocode | Yes | Algorithm 1: Aggregating Algorithm; Algorithm 2: Generalized Aggregating Algorithm; Algorithm 3: Adaptive Generalized Aggregating Algorithm (AGAA) |
| Open Source Code | No | The paper mentions empirical demonstration and provides an appendix for details, but does not provide concrete access to source code or explicitly state it's open-source. |
| Open Datasets | Yes | We have tested the aggregating algorithms on real data as studied by Vovk [11]. |
| Dataset Splits | No | The paper mentions using real data and two datasets but does not provide specific details on training, validation, or test splits. |
| 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 software dependencies with version numbers. |
| Experiment Setup | Yes | Consider the Brier game G2 ℓBrier( 2, 2); a probability game with 2 experts {θ1, θ2}, 2 outcomes {0, 1}, and where the loss ℓBrier is the Brier loss [11] (which is 1-mixable). Assume that; expert θ1 consistently predicts Pr(x = 0) = 1/2; expert θ2 predicts Pr(x = 0) = 1/4 during the first 50 rounds, then switches to predicting Pr(x = 0) = 3/4 thereafter; the outcome is always x = 0. A straightforward simulation using the AGAA with the Shannon entropy, Vovk s substitution function for the Brier loss [11], βt as in Theorem 19 with vt := 1 8t Pt s=1 ℓBrier(xs, As), yields RΦ ℓBrier + Rθ (T) 5, T 150, where in this case θ = θ2 is the best expert for T 150. |