Spectral Bandits for Smooth Graph Functions
Authors: Michal Valko, Remi Munos, Branislav Kveton, Tomáš Kocák
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on real-world content recommendation problem show that a good estimator of user preferences for thousands of items can be learned from just tens of nodes evaluations. (Abstract) and 6. Experiments (Section Title) |
| Researcher Affiliation | Collaboration | Michal Valko MICHAL.VALKO@INRIA.FR INRIA Lille Nord Europe, Seque L team, 40 avenue Halley 59650, Villeneuve d Ascq, France Remi Munos REMI.MUNOS@INRIA.FR INRIA Lille Nord Europe, Seque L team, France; Microsoft Research New England, Cambridge, MA, USA Branislav Kveton BRANISLAV.KVETON@TECHNICOLOR.COM Technicolor Research Center, 735 Emerson St, Palo Alto, CA 94301, USA Tom aˇs Koc ak TOMAS.KOCAK@INRIA.FR INRIA Lille Nord Europe, Seque L team, 40 avenue Halley 59650, Villeneuve d Ascq, France |
| Pseudocode | Yes | Algorithm 1 SPECTRALUCB and Algorithm 2 SPECTRALELIMINATOR |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | Movie Lens dataset (Lam & Herlocker, 2012), a dataset of 6k users who rated one million movies. (Section 6.2) and The social network of the users was crawled by Jamali & Ester (2010) (Section 6.3) |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or explicit mention of training, validation, and test sets) needed to reproduce the data partitioning in a standard ML context. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'fast SDD solvers as CMG by Koutis et al. (2011)' but does not provide a specific version number for CMG or any other software dependency. |
| Experiment Setup | Yes | In all experiments we set δ to 0.001 and R to 0.01. For SPECTRALUCB and SPECTRALELIMINATOR we set Λ to ΛL + λI with λ = 0.01. For Lin UCB we regularized with λI with λ = 1. (Section 6. Experiments) and We ran the algorithms in the desired T < N regime, with N = 500 (N = 54 for the lattice), T = 250, and k = 5. (Section 6.1 Random graph models) |