10,223 research outputs found
Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning
Hyperparameter optimization (HPO) is important to leverage the full potential
of machine learning (ML). In practice, users are often interested in
multi-objective (MO) problems, i.e., optimizing potentially conflicting
objectives, like accuracy and energy consumption. To tackle this, the vast
majority of MO-ML algorithms return a Pareto front of non-dominated machine
learning models to the user. Optimizing the hyperparameters of such algorithms
is non-trivial as evaluating a hyperparameter configuration entails evaluating
the quality of the resulting Pareto front. In literature, there are known
indicators that assess the quality of a Pareto front (e.g., hypervolume, R2) by
quantifying different properties (e.g., volume, proximity to a reference
point). However, choosing the indicator that leads to the desired Pareto front
might be a hard task for a user. In this paper, we propose a human-centered
interactive HPO approach tailored towards multi-objective ML leveraging
preference learning to extract desiderata from users that guide the
optimization. Instead of relying on the user guessing the most suitable
indicator for their needs, our approach automatically learns an appropriate
indicator. Concretely, we leverage pairwise comparisons of distinct Pareto
fronts to learn such an appropriate quality indicator. Then, we optimize the
hyperparameters of the underlying MO-ML algorithm towards this learned
indicator using a state-of-the-art HPO approach. In an experimental study
targeting the environmental impact of ML, we demonstrate that our approach
leads to substantially better Pareto fronts compared to optimizing based on a
wrong indicator pre-selected by the user, and performs comparable in the case
of an advanced user knowing which indicator to pick
Human-Machine Collaborative Optimization via Apprenticeship Scheduling
Coordinating agents to complete a set of tasks with intercoupled temporal and
resource constraints is computationally challenging, yet human domain experts
can solve these difficult scheduling problems using paradigms learned through
years of apprenticeship. A process for manually codifying this domain knowledge
within a computational framework is necessary to scale beyond the
``single-expert, single-trainee" apprenticeship model. However, human domain
experts often have difficulty describing their decision-making processes,
causing the codification of this knowledge to become laborious. We propose a
new approach for capturing domain-expert heuristics through a pairwise ranking
formulation. Our approach is model-free and does not require enumerating or
iterating through a large state space. We empirically demonstrate that this
approach accurately learns multifaceted heuristics on a synthetic data set
incorporating job-shop scheduling and vehicle routing problems, as well as on
two real-world data sets consisting of demonstrations of experts solving a
weapon-to-target assignment problem and a hospital resource allocation problem.
We also demonstrate that policies learned from human scheduling demonstration
via apprenticeship learning can substantially improve the efficiency of a
branch-and-bound search for an optimal schedule. We employ this human-machine
collaborative optimization technique on a variant of the weapon-to-target
assignment problem. We demonstrate that this technique generates solutions
substantially superior to those produced by human domain experts at a rate up
to 9.5 times faster than an optimization approach and can be applied to
optimally solve problems twice as complex as those solved by a human
demonstrator.Comment: Portions of this paper were published in the Proceedings of the
International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and
in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper
consists of 50 pages with 11 figures and 4 table
A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments
Many real-world problems are usually computationally costly and the objective
functions evolve over time. Data-driven, a.k.a. surrogate-assisted,
evolutionary optimization has been recognized as an effective approach for
tackling expensive black-box optimization problems in a static environment
whereas it has rarely been studied under dynamic environments. This paper
proposes a simple but effective transfer learning framework to empower
data-driven evolutionary optimization to solve dynamic optimization problems.
Specifically, it applies a hierarchical multi-output Gaussian process to
capture the correlation between data collected from different time steps with a
linearly increased number of hyperparameters. Furthermore, an adaptive source
task selection along with a bespoke warm staring initialization mechanisms are
proposed to better leverage the knowledge extracted from previous optimization
exercises. By doing so, the data-driven evolutionary optimization can jump
start the optimization in the new environment with a strictly limited
computational budget. Experiments on synthetic benchmark test problems and a
real-world case study demonstrate the effectiveness of our proposed algorithm
against nine state-of-the-art peer algorithms
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