Set-Valued Approachability and Online Learning with Partial Monitoring
Authors: Shie Mannor, Vianney Perchet, Gilles Stoltz
JMLR 2014 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We develop a variant of approachability for games where there is ambiguity in the obtained reward: it belongs to a set rather than being a single vector. Using this variant we tackle the problem of approachability in games with partial monitoring and develop a simple and generally efficient strategy (i.e., with constant per-step complexity) for this setup. As an important example, we instantiate our general strategy to the case when external regret or internal regret is to be minimized under partial monitoring. The paper contains proofs, lemmas, and pseudocode for theoretical algorithms, but no empirical evaluation on datasets or performance metrics. |
| Researcher Affiliation | Academia | Shie Mannor Israel Institute of Technology (Technion) Faculty of Electrical Engineering 32000 Haifa, Israel; Vianney Perchet Universit e Paris Diderot, LPMA 8 place FM/13 75013 Paris, France; Gilles Stoltz GREGHEC: HEC Paris CNRS 1 rue de la Lib eration 78351 Jouy-en-Josas, France |
| Pseudocode | Yes | Approaching Strategy in Games with Partial Monitoring Parameters: an integer block length L 1, an exploration parameter γ [0, 1], a strategy Ψ for m approachability of C Notation: u (I) is the uniform distribution over I, PF denotes the projection operator in ℓ2 norm of RH I onto F Initialization: compute the finite set B and the mapping Φ : F (B) of Lemma 19, compute the finite set A and the mapping Θ : (I) (A) defined based on Assumption 1, pick an arbitrary θ1 (A) For all blocks n = 1, 2, . . ., |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and introduces game settings and approachability concepts. It does not use or provide any specific datasets for empirical evaluation. Example 1 lists abstract game types like "apple tasting problem" but these are illustrative scenarios, not concrete datasets. |
| Dataset Splits | No | This paper is theoretical and does not perform experiments using datasets, therefore, no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and describes algorithms conceptually rather than providing implementation details or specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and proofs; it does not present an experimental setup, hyperparameters, or training configurations. |