Efficient Hyperparameter Optimization under Multi-Source Covariate Shift

• URL: http://arxiv.org/abs/2006.10600v2
• Date: Mon, 16 Aug 2021 10:42:39 GMT
• Title: Efficient Hyperparameter Optimization under Multi-Source Covariate Shift
• Authors: Masahiro Nomura and Yuta Saito
• Abstract summary: A typical assumption in supervised machine learning is that the train (source) and test (target) datasets follow completely the same distribution. In this work, we consider a novel hyperparameter optimization problem under the multi-source covariate shift. We construct a variance reduced estimator that unbiasedly approximates the target objective with a desirable variance property.
• Score: 13.787554178089446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A typical assumption in supervised machine learning is that the train (source) and test (target) datasets follow completely the same distribution. This assumption is, however, often violated in uncertain real-world applications, which motivates the study of learning under covariate shift. In this setting, the naive use of adaptive hyperparameter optimization methods such as Bayesian optimization does not work as desired since it does not address the distributional shift among different datasets. In this work, we consider a novel hyperparameter optimization problem under the multi-source covariate shift whose goal is to find the optimal hyperparameters for a target task of interest using only unlabeled data in a target task and labeled data in multiple source tasks. To conduct efficient hyperparameter optimization for the target task, it is essential to estimate the target objective using only the available information. To this end, we construct the variance reduced estimator that unbiasedly approximates the target objective with a desirable variance property. Building on the proposed estimator, we provide a general and tractable hyperparameter optimization procedure, which works preferably in our setting with a no-regret guarantee. The experiments demonstrate that the proposed framework broadens the applications of automated hyperparameter optimization.

Related papers

• Adaptive Preference Scaling for Reinforcement Learning with Human Feedback [103.36048042664768]
  Reinforcement learning from human feedback (RLHF) is a prevalent approach to align AI systems with human values. We propose a novel adaptive preference loss, underpinned by distributionally robust optimization (DRO) Our method is versatile and can be readily adapted to various preference optimization frameworks.
  arXiv  Detail & Related papers  (2024-06-04T20:33:22Z)
• End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty [55.04219793298687]
  The Predict-Then-Forecast (PtO) paradigm in machine learning aims to maximize downstream decision quality. This paper extends the PtO methodology to optimization problems with nondifferentiable Ordered Weighted Averaging (OWA) objectives. It shows how optimization of OWA functions can be effectively integrated with parametric prediction for fair and robust optimization under uncertainty.
  arXiv  Detail & Related papers  (2024-02-12T16:33:35Z)
• Bayesian Inverse Transfer in Evolutionary Multiobjective Optimization [29.580786235313987]
  We introduce the first Inverse Transfer Multiobjective (invTrEMO) InvTrEMO harnesses the common objective functions in many prevalent areas, even when decision spaces do not precisely align between tasks. InvTrEMO yields high-precision inverse models as a significant byproduct, enabling the generation of tailored solutions on-demand.
  arXiv  Detail & Related papers  (2023-12-22T14:12:18Z)
• Fine-Tuning Adaptive Stochastic Optimizers: Determining the Optimal Hyperparameter $ε$ via Gradient Magnitude Histogram Analysis [0.7366405857677226]
  We introduce a new framework based on the empirical probability density function of the loss's magnitude, termed the "gradient magnitude histogram" We propose a novel algorithm using gradient magnitude histograms to automatically estimate a refined and accurate search space for the optimal safeguard.
  arXiv  Detail & Related papers  (2023-11-20T04:34:19Z)
• Optimization of Annealed Importance Sampling Hyperparameters [77.34726150561087]
  Annealed Importance Sampling (AIS) is a popular algorithm used to estimates the intractable marginal likelihood of deep generative models. We present a parameteric AIS process with flexible intermediary distributions and optimize the bridging distributions to use fewer number of steps for sampling. We assess the performance of our optimized AIS for marginal likelihood estimation of deep generative models and compare it to other estimators.
  arXiv  Detail & Related papers  (2022-09-27T07:58:25Z)
• Bayesian Optimization with Informative Covariance [13.113313427848828]
  We propose novel informative covariance functions for optimization, leveraging nonstationarity to encode preferences for certain regions of the search space. We demonstrate that the proposed functions can increase the sample efficiency of Bayesian optimization in high dimensions, even under weak prior information.
  arXiv  Detail & Related papers  (2022-08-04T15:05:11Z)
• Pre-training helps Bayesian optimization too [49.28382118032923]
  We seek an alternative practice for setting functional priors. In particular, we consider the scenario where we have data from similar functions that allow us to pre-train a tighter distribution a priori. Our results show that our method is able to locate good hyper parameters at least 3 times more efficiently than the best competing methods.
  arXiv  Detail & Related papers  (2022-07-07T04:42:54Z)
• Efficient hyperparameter optimization by way of PAC-Bayes bound minimization [4.191847852775072]
  We present an alternative objective that is equivalent to a Probably Approximately Correct-Bayes (PAC-Bayes) bound on the expected out-of-sample error. We then devise an efficient gradient-based algorithm to minimize this objective.
  arXiv  Detail & Related papers  (2020-08-14T15:54:51Z)
• Automatic Setting of DNN Hyper-Parameters by Mixing Bayesian Optimization and Tuning Rules [0.6875312133832078]
  We build a new algorithm for evaluating and analyzing the results of the network on the training and validation sets. We use a set of tuning rules to add new hyper-parameters and/or to reduce the hyper- parameter search space to select a better combination.
  arXiv  Detail & Related papers  (2020-06-03T08:53:48Z)
• Implicit differentiation of Lasso-type models for hyperparameter optimization [82.73138686390514]
  We introduce an efficient implicit differentiation algorithm, without matrix inversion, tailored for Lasso-type problems. Our approach scales to high-dimensional data by leveraging the sparsity of the solutions.
  arXiv  Detail & Related papers  (2020-02-20T18:43:42Z)
• Bilevel Optimization for Differentially Private Optimization in Energy Systems [53.806512366696275]
  This paper studies how to apply differential privacy to constrained optimization problems whose inputs are sensitive. The paper shows that, under a natural assumption, a bilevel model can be solved efficiently for large-scale nonlinear optimization problems.
  arXiv  Detail & Related papers  (2020-01-26T20:15:28Z)

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.