Discovering Multiple Algorithm Configurations

• URL: http://arxiv.org/abs/2303.07434v1
• Date: Mon, 13 Mar 2023 19:21:59 GMT
• Title: Discovering Multiple Algorithm Configurations
• Authors: Leonid Keselman and Martial Hebert
• Abstract summary: We extend algorithm configuration to automatically discover multiple modes in the tuning dataset. Unlike prior work, these configuration modes represent multiple dataset instances and are detected automatically during the course of optimization. Our results characterize these methods on synthetic test functions and in multiple robotics application domains: stereoscopic depth estimation, differentiable rendering, motion planning, and visual odometry.
• Score: 24.7500811470085
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many practitioners in robotics regularly depend on classic, hand-designed algorithms. Often the performance of these algorithms is tuned across a dataset of annotated examples which represent typical deployment conditions. Automatic tuning of these settings is traditionally known as algorithm configuration. In this work, we extend algorithm configuration to automatically discover multiple modes in the tuning dataset. Unlike prior work, these configuration modes represent multiple dataset instances and are detected automatically during the course of optimization. We propose three methods for mode discovery: a post hoc method, a multi-stage method, and an online algorithm using a multi-armed bandit. Our results characterize these methods on synthetic test functions and in multiple robotics application domains: stereoscopic depth estimation, differentiable rendering, motion planning, and visual odometry. We show the clear benefits of detecting multiple modes in algorithm configuration space.

Related papers

• A Framework for Guided Motion Planning [1.179253400575852]
  We formalize the notion of guided search by defining the concept of a guiding space. This new language encapsulates many seemingly distinct prior methods under the same framework. We suggest an information theoretic method to evaluate guidance, which experimentally matches intuition when tested on known algorithms.
  arXiv  Detail & Related papers  (2024-04-04T00:58:19Z)
• Iterative Linear Quadratic Optimization for Nonlinear Control: Differentiable Programming Algorithmic Templates [9.711326718689495]
  We present the implementation of nonlinear control algorithms based on linear and quadratic approximations of the objective from a functional viewpoint. We derive the computational complexities of all algorithms in a differentiable programming framework and present sufficient optimality conditions. The algorithms are coded in a differentiable programming language in a publicly available package.
  arXiv  Detail & Related papers  (2022-07-13T17:10:47Z)
• Automated Dynamic Algorithm Configuration [39.39845379026921]
  The performance of an algorithm often critically depends on its parameter configuration. It has been shown that some algorithm parameters are best adjusted dynamically during execution. A promising alternative is to automatically learn such dynamic parameter adaptation policies from data.
  arXiv  Detail & Related papers  (2022-05-27T10:30:25Z)
• Per-run Algorithm Selection with Warm-starting using Trajectory-based Features [5.073358743426584]
  Per-instance algorithm selection seeks to recommend, for a given problem instance, one or several suitable algorithms. We propose an online algorithm selection scheme which we coin per-run algorithm selection. We show that our approach outperforms static per-instance algorithm selection.
  arXiv  Detail & Related papers  (2022-04-20T14:30:42Z)
• A Metaheuristic Algorithm for Large Maximum Weight Independent Set Problems [58.348679046591265]
  Given a node-weighted graph, find a set of independent (mutually nonadjacent) nodes whose node-weight sum is maximum. Some of the graphs airsing in this application are large, having hundreds of thousands of nodes and hundreds of millions of edges. We develop a new local search algorithm, which is a metaheuristic in the greedy randomized adaptive search framework.
  arXiv  Detail & Related papers  (2022-03-28T21:34:16Z)
• 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)
• Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm Selection [0.9281671380673306]
  A key challenge in the application of evolutionary algorithms is the selection of an algorithm instance that best suits the problem at hand. We analyze in this work how such prior performance data can be used to infer informed dynamic algorithm selection schemes for the solution of pseudo-Boolean optimization problems.
  arXiv  Detail & Related papers  (2021-02-12T12:27:02Z)
• A Systematic Characterization of Sampling Algorithms for Open-ended Language Generation [71.31905141672529]
  We study the widely adopted ancestral sampling algorithms for auto-regressive language models. We identify three key properties that are shared among them: entropy reduction, order preservation, and slope preservation. We find that the set of sampling algorithms that satisfies these properties performs on par with the existing sampling algorithms.
  arXiv  Detail & Related papers  (2020-09-15T17:28:42Z)
• 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)
• CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus [62.86856923633923]
  We present a robust estimator for fitting multiple parametric models of the same form to noisy measurements. In contrast to previous works, which resorted to hand-crafted search strategies for multiple model detection, we learn the search strategy from data. For self-supervised learning of the search, we evaluate the proposed algorithm on multi-homography estimation and demonstrate an accuracy that is superior to state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-01-08T17:37:01Z)

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.