Comments (3)
david-cortes
commented on August 19, 2024
It is a hyperparameter that should be tuned. The formula from the reference is calculated with information that you don't have for an online policy like this, and which includes probabilistic terms. The formula that you are thinking about is not the same thing LinTS is doing (see LogisticTS).
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douglasmason
commented on August 19, 2024
Can you please elaborate? There are two formulas I mentioned and it’s not clear which you’re referring to. The first comes from residuals (empirical) and is standard statistics requiring the entire training dataset, and the second comes from the Agarwal paper. Are you saying the Agarwal algorithm cannot be implemented in an online fashion?
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david-cortes
commented on August 19, 2024
LinTS is based on the reference from Agrawal, and has a hyperparameter v_sq. It is meant for an online scenario with incremental updates. LogisticTS's formula comes from standard statistics (for a logistic model), and does not support incremental updates.
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Related Issues (20)
- Question regarding using contextual bandits for Learning-To-Rank HOT 1
- Different predictions of the same model.dill file in different CPUs for the LinUCB algorithm HOT 1
- Support for continuous rewards HOT 1
- ParametricTS fails with: '_OneVsRest' object has no attribute 'beta_counters' HOT 2
- XGBClassifier becomes un-serializable after being used as a base_model HOT 2
- Need more understanding for the method beta_prior HOT 1
- Any good strategies to run Simulation other then one in Online policy example HOT 1
- How to deal with this Scenario while applying CB techniques HOT 2
- Getting topN arm features in Offpolicy method HOT 4
- Type error if beta_prior == "auto" and nchoices is list HOT 1
- Possibly unexpected behaviour of decision function HOT 1
- _BasePolicy.add_arm(); NameError: name 'base_algorithm' is not defined HOT 1
- AssertionError: online_contextual_bandits.ipynb HOT 1
- TypeError: contextual bandits with custom 'choice_names' (online.py) HOT 3
- Getting topN arm features in onlinepolicy method HOT 9
- topN inputs clarification HOT 3
- warm_start for online policy HOT 1
- Doubt in DREstimator HOT 3
- New release HOT 1
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