Online Preselection with Context Information under the Plackett-Luce Model

• URL: http://arxiv.org/abs/2002.04275v1
• Date: Tue, 11 Feb 2020 09:27:24 GMT
• Title: Online Preselection with Context Information under the Plackett-Luce Model
• Authors: Adil El Mesaoudi-Paul, Viktor Bengs, Eyke H\"ullermeier
• Abstract summary: We consider an extension of the contextual multi-armed bandit problem. Instead of selecting a single alternative (arm), a learner is supposed to make a preselection in the form of a subset of alternatives. We propose the CPPL algorithm, which is inspired by the well-known UCB algorithm.
• Score: 10.286111512725334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider an extension of the contextual multi-armed bandit problem, in which, instead of selecting a single alternative (arm), a learner is supposed to make a preselection in the form of a subset of alternatives. More specifically, in each iteration, the learner is presented a set of arms and a context, both described in terms of feature vectors. The task of the learner is to preselect $k$ of these arms, among which a final choice is made in a second step. In our setup, we assume that each arm has a latent (context-dependent) utility, and that feedback on a preselection is produced according to a Plackett-Luce model. We propose the CPPL algorithm, which is inspired by the well-known UCB algorithm, and evaluate this algorithm on synthetic and real data. In particular, we consider an online algorithm selection scenario, which served as a main motivation of our problem setting. Here, an instance (which defines the context) from a certain problem class (such as SAT) can be solved by different algorithms (the arms), but only $k$ of these algorithms can actually be run.

Related papers

• Contextual Bandits with Arm Request Costs and Delays [19.263086804406786]
  We introduce a novel extension of the contextual bandit problem, where new sets of arms can be requested with time delays and associated costs. In this setting, the learner can select multiple arms from a decision set, with each selection taking one unit of time. We design algorithms that can effectively select arms and determine the appropriate time to request new arms, thereby minimizing their regret.
  arXiv  Detail & Related papers  (2024-10-17T00:44:50Z)
• How to Choose a Reinforcement-Learning Algorithm [29.76033485145459]
  We streamline the process of choosing reinforcement-learning algorithms and action-distribution families. We provide a structured overview of existing methods and their properties, as well as guidelines for when to choose which methods.
  arXiv  Detail & Related papers  (2024-07-30T15:54:18Z)
• Stochastic Rising Bandits [40.32303434592863]
  We study a particular case of the rested and restless bandits in which the arms' expected payoff is monotonically non-decreasing. This characteristic allows designing specifically crafted algorithms that exploit the regularity of the payoffs to provide tight regret bounds. We empirically compare our algorithms with state-of-the-art methods for non-stationary MABs over several synthetically generated tasks and an online model selection problem for a real-world dataset.
  arXiv  Detail & Related papers  (2022-12-07T17:30:45Z)
• Machine Learning for Online Algorithm Selection under Censored Feedback [71.6879432974126]
  In online algorithm selection (OAS), instances of an algorithmic problem class are presented to an agent one after another, and the agent has to quickly select a presumably best algorithm from a fixed set of candidate algorithms. For decision problems such as satisfiability (SAT), quality typically refers to the algorithm's runtime. In this work, we revisit multi-armed bandit algorithms for OAS and discuss their capability of dealing with the problem. We adapt them towards runtime-oriented losses, allowing for partially censored data while keeping a space- and time-complexity independent of the time horizon.
  arXiv  Detail & Related papers  (2021-09-13T18:10:52Z)
• Max-Utility Based Arm Selection Strategy For Sequential Query Recommendations [16.986870945319293]
  We consider the query recommendation problem in closed loop interactive learning settings like online information gathering and exploratory analytics. The problem can be naturally modelled using the Multi-Armed Bandits (MAB) framework with countably many arms. We show that such a selection strategy often results in higher cumulative regret and to this end, we propose a selection strategy based on the maximum utility of the arms.
  arXiv  Detail & Related papers  (2021-08-31T13:03:30Z)
• Algorithm Selection on a Meta Level [58.720142291102135]
  We introduce the problem of meta algorithm selection, which essentially asks for the best way to combine a given set of algorithm selectors. We present a general methodological framework for meta algorithm selection as well as several concrete learning methods as instantiations of this framework.
  arXiv  Detail & Related papers  (2021-07-20T11:23:21Z)
• Determinantal Beam Search [75.84501052642361]
  Beam search is a go-to strategy for decoding neural sequence models. In use-cases that call for multiple solutions, a diverse or representative set is often desired. By posing iterations in beam search as a series of subdeterminant problems, we can turn the algorithm into a diverse subset selection process.
  arXiv  Detail & Related papers  (2021-06-14T13:01:46Z)
• Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedback [51.21673420940346]
  Combinatorial bandits generalize multi-armed bandits, where the agent chooses sets of arms and observes a noisy reward for each arm contained in the chosen set. We focus on the pure-exploration problem of identifying the best arm with fixed confidence, as well as a more general setting, where the structure of the answer set differs from the one of the action set. Based on a projection-free online learning algorithm for finite polytopes, it is the first computationally efficient algorithm which is convexally optimal and has competitive empirical performance.
  arXiv  Detail & Related papers  (2021-01-21T10:35:09Z)
• Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis [75.64261155172856]
  survival analysis (SA) naturally supports censored data and offers appropriate ways to use such data for learning distributional models of algorithm runtime. We leverage such models as a basis of a sophisticated decision-theoretic approach to algorithm selection, which we dub Run2Survive. In an extensive experimental study with the standard benchmark ASlib, our approach is shown to be highly competitive and in many cases even superior to state-of-the-art AS approaches.
  arXiv  Detail & Related papers  (2020-07-06T15:20:17Z)
• Extreme Algorithm Selection With Dyadic Feature Representation [78.13985819417974]
  We propose the setting of extreme algorithm selection (XAS) where we consider fixed sets of thousands of candidate algorithms. We assess the applicability of state-of-the-art AS techniques to the XAS setting and propose approaches leveraging a dyadic feature representation.
  arXiv  Detail & Related papers  (2020-01-29T09:40:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.