Constant Regret, Generalized Mixability, and Mirror Descent
Authors: Zakaria Mhammedi, Robert C. Williamson
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | 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 zak.mhammedi@anu.edu.au Robert C. Williamson Research School of Computer Science Australian National University and DATA61 bob.williamson@anu.edu.au |
| 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. |