Efficient Learning in Large-Scale Combinatorial Semi-Bandits
Authors: Zheng Wen, Branislav Kveton, Azin Ashkan
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also evaluate Comb Lin TS on a variety of problems with thousands of items. Our experiment results demonstrate that Comb Lin TS is scalable, robust to the choice of algorithm parameters, and significantly outperforms the best of our baselines. (Abstract) and 6. Experiments In this section, we evaluate Comb Lin TS on three problems. |
| Researcher Affiliation | Industry | Zheng Wen ZHENGWEN@YAHOO-INC.COM Yahoo Labs, Sunnyvale, CA Branislav Kveton KVETON@ADOBE.COM Adobe Research, San Jose, CA Azin Ashkan AZIN.ASHKAN@TECHNICOLOR.COM Technicolor Research, Los Altos, CA |
| Pseudocode | Yes | Algorithm 1 Compute θt+1 and Σt+1 based on Kalman Filtering, Algorithm 2 Combinatorial Linear Thompson Sampling, Algorithm 3 Combinatorial Linear UCB |
| Open Source Code | No | The paper does not provide any concrete access to source code (no specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | Yes | Adult dataset (Asuncion & Newman, 2007) and Last.fm music recommendation dataset (Cantador et al., 2011). Full citations are in the references: Asuncion, A. and Newman, D.J. UCI machine learning repository, 2007. URL http://www.ics.uci.edu/$\\$~mlearn/{MLR}epository.html. and Cantador, Iv an, Brusilovsky, Peter, and Kuflik, Tsvi. Second workshop on information heterogeneity and fusion in recommender systems (hetrec 2011). In Proceedings of the 5th ACM conference on Recommender systems, Rec Sys 2011. ACM, 2011. (The Last.fm dataset also mentions a URL in the text: http://www.lastfm.com) |
| Dataset Splits | No | The paper describes the datasets used (Adult dataset, Last.fm dataset) but does not provide specific details on training, validation, and test splits (e.g., percentages, sample counts, or citations to predefined splits). |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Our experiments are parameterized by a sextuple (m, d, λtrue, σtrue, λ, σ), where m, d, λ, and σ are defined before and λtrue and σtrue are respectively the true standard deviations of θ and the observation noises. In each round of simulation, we first construct a problem instance as follows: (1) generate Φ by sampling each component of Φ i.i.d. from N(0, 1); (2) sample θ independently from N(0, λ2 true I) and set w = Φθ ; and (3) (t, e), the observation noise ηt(e) = wt(e) w(e) is i.i.d. sampled from N(0, σ2 true). Then we apply Comb Lin TS with parameter (λ, σ) to the constructed instance for n episodes. Notice that in general (λ, σ) = (λtrue, σtrue). We average the experiment results over 200 simulations to estimate the Bayes cumulative regret RBayes(n). We start with a default case with m = 30, d = 200, λtrue = λ = 10 and σtrue = σ = 1. Notice in this case L = 1860 and |A| 1.18 1017. We choose n = 150 since in the default case, the Bayes per-episode regret of Comb Lin TS vanishes far before period 150. |