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Adaptive Risk Minimization: Learning to Adapt to Domain Shift
[article]
2021
arXiv
pre-print
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Our primary contribution is to introduce the framework of adaptive risk minimization (ARM), in which models are directly optimized for effective adaptation to shift by learning to adapt on the training ...
Compared to prior methods for robustness, invariance, and adaptation, ARM methods provide performance gains of 1-4% test accuracy on a number of image classification problems exhibiting domain shift. ...
Adaptive Risk Minimization In this section, we formally describe the ARM framework, which defines an objective for training adaptive models to tackle domain shift. ...
arXiv:2007.02931v4
fatcat:2gjsygbev5cmhdryhrwijvfeju
Adaptation policy and community discourse: risk, vulnerability, and just transformation
2017
Environmental Politics
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Despite a discursive disconnect between governmental focus on a risk or resilience-based approach and a community concern with the vulnerability of basic needs and capabilities of everyday life, deliberative ...
articulated by local governments, environmental groups, and local residents engaged in adaptation planning in Australia. ...
This paper, along with the others in this symposium, was originally presented at a workshop on Just Adaptation hosted and funded by the Sydney Environment Institute. ...
doi:10.1080/09644016.2017.1287628
fatcat:rlr5d3qcurezngsczcll44ncpa
Discrepancy Minimization in Domain Generalization with Generative Nearest Neighbors
[article]
2020
arXiv
pre-print
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Features extracted from this source domain are learned using a generative model whose latent space is used as a sampler to retrieve the nearest neighbors for the target data points. ...
Domain generalization (DG) deals with the problem of domain shift where a machine learning model trained on multiple-source domains fail to generalize well on a target domain with different statistics. ...
Existing methods tackle the DG problem by learning domain invariant feature representations using adversarial methods (Li et al., 2018b,c) or meta-learning approaches where held-out source domains are ...
arXiv:2007.14284v1
fatcat:y5twxlfenvc35ikeu5eexrtbzi
Comparative Risk Assessment to Inform Adaptation Priorities for the Natural Environment: Observations from the First UK Climate Change Risk Assessment
2015
Climate
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This suggests that goals for risk assessment need to be more clearly explicated and assumptions on tolerable risk declared as a primer for further dialogue on expectations for managed outcomes. ...
Risk assessment can potentially provide an objective framework to synthesise and prioritise climate change risks to inform adaptation policy. ...
Acknowledgments The CCRA Advisory Group are thanked for providing very useful and informative comments on the use of evidence to inform policy requirements. ...
doi:10.3390/cli3040937
fatcat:zfa3jmggu5f7bmm5la36man374
Understanding and unlocking transformative learning as a method for enabling behaviour change for adaptation and resilience to disaster threats
2016
International Journal of Disaster Risk Reduction
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Cite this article as: Justin Sharpe, Understanding and unlocking transformative learning as a method for enabling behaviour change for adaptation and resilience to disaster threats, International Journal ...
of Disaster Risk Reduction, http://dx. ...
This includes social contexts and pathways for learning. Therefore new ways of approaching learning are required to help break-out of established ways of thinking and tackling problems. ...
doi:10.1016/j.ijdrr.2016.04.014
fatcat:znnf3idgqneq5ob5yr74j4r3qq
Risk, responsibility and reconfiguration: Penal adaptation and misadaptation
2009
Punishment & Society
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Drawing on Bourdieu, we suggest that this may be best understood not as a counter-example to accounts of penal transformation but as evidence of an incompleteness in their analyses which reflects the ' ...
In so doing, we problematize and explore what we term the 'governmentality gap'; meaning, a lacuna in the existing penological scholarship which concerns the contingent relationships between changing governmental ...
to support a variety of penal strategies -including 'correctional treatment' as a form of risk minimization. ...
doi:10.1177/1462474509341153
fatcat:74bs773xkreajjynfqcutoufaq
Uncertainty Minimization for Personalized Federated Semi-Supervised Learning
[article]
2022
arXiv
pre-print
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To tackle this problem, 1) we propose a novel personalized semi-supervised learning paradigm which allows partial-labeled or unlabeled clients to seek labeling assistance from data-related clients (helper ...
Since federated learning (FL) has been introduced as a decentralized learning technique with privacy preservation, statistical heterogeneity of distributed data stays the main obstacle to achieve robust ...
In [25] , Fallah et al. utilized the model-agnostic meta-learning (MAML) framework to find an initial global model, which can be easily adapt to clients' personal datasets by performing a few steps of ...
arXiv:2205.02438v1
fatcat:lsx2xo6tbbenjdn4prq7cs4cpy
What Is Minimally Cooperative Behavior?
[chapter]
2020
From Conventionalism to Social Authenticity
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In principle, all phenomena dealing with sociality are covered as long as they are approached from a philosophical point of view, broadly understood. ...
Covers a new and rapidly developing field that has become one of the key topics of the international philosophical world. ...
Acknowledgments This research has been supported by a grant on Minimal Cooperation, led by Acknowledgements Thanks to Lambros Malafouris and Deb Tollefsen for helpful discussions on these topics. ...
doi:10.1007/978-3-030-29783-1_2
fatcat:kilqd6dgpvepvmr6lxmhlyaatq
Giant Proliferating Pilar Tumor of the Scalp: A Minimal Risk Approach
2012
Journal of Neurological Surgery. Part B: Skull Base: an interdisciplinary approach
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The Occipital Transtentorial Approach for Superior Vermian and Superomedial Cerebellar Arteriovenous Malformations: Advantages, Limitations, and Options Nancy McLaughlin (presenter), Neil A. ...
Giant Proliferating Pilar Tumor of the Scalp: A Minimal Risk Approach Christopher H. Rassekh (presenter), Kelly M. Malloy, Tom Thomas, Jason Brandt, Xiaowei Xu, Ara A. ...
Finally, the reader will learn a systematic approach to characterizing complex lesions. ...
doi:10.1055/s-0032-1312280
fatcat:kksrjga6drfb7ojybu3lcsm55e
Software Replica of Minimal Living Processes
2010
Origins of life and evolution of the biosphere
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Minimal life begins at the intersection of a series of processes which need to be isolated, differentiated and duplicated as such in computers. ...
Only software developments and running make possible to understand the way these processes are intimately interconnected in order for life to appear at the crossroad. ...
Acknowledgements G.B. thanks the Belgian Fonds de la Recherche Scientifique-FNRS for a post-doctoral fellowship. ...
doi:10.1007/s11084-010-9190-5
pmid:20204519
fatcat:m46uvtkpnbh7bhjzmwctk4q43e
Class-Incremental Domain Adaptation
[article]
2020
arXiv
pre-print
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Existing DA methods tackle domain-shift but are unsuitable for learning novel target-domain classes. ...
We introduce a practical Domain Adaptation (DA) paradigm called Class-Incremental Domain Adaptation (CIDA). ...
B) During CIDA, we learn a targetspecific feature extractor ft (to minimize domain-shift) and classifier gt (to learn C t ). ...
arXiv:2008.01389v1
fatcat:ukx4f6ohbzgyvo3vo2rvplxjje
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
[article]
2021
arXiv
pre-print
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Following the emerging trends, we also discuss federated learning in the intersection with other learning paradigms, termed as federated x learning, where x includes multitask learning, meta-learning, ...
We conduct a focused survey of federated learning in conjunction with other learning algorithms. ...
[25] studied the federated unsupervised domain adaptation that aligns the shifted domains under federated setting with a couple of learning paradigms. ...
arXiv:2102.12920v2
fatcat:5fcwfhxibbedbcbuzrfyqdedky
Few-shot Adaptive Faster R-CNN
[article]
2019
arXiv
pre-print
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To mitigate the detection performance drop caused by domain shift, we aim to develop a novel few-shot adaptation approach that requires only a few target domain images with limited bounding box annotations ...
Third, the model suffers from over-adaptation (similar to overfitting when training with a few data example) and instability risk that may lead to degraded detection performance in the target domain. ...
Later, [19] proposed to transfer the base class feature to a new class; a recent work [10] proposed a meta learning based approach which achieves state-of-the-art. ...
arXiv:1903.09372v1
fatcat:3mxc75hstbextk2fesqx7hib7q
MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records
[article]
2019
arXiv
pre-print
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In this paper, we propose MetaPred, a meta-learning for clinical risk prediction from longitudinal patient EHRs. ...
In particular, in order to predict the target risk where there are limited data samples, we train a meta-learner from a set of related risk prediction tasks which learns how a good predictor is learned ...
Specifically, model-agnostic meta-learning [15] aims to learn a good parameter initialization for the fast adaptation of testing tasks. ...
arXiv:1905.03218v1
fatcat:nkxrocx56vfrdj3hxos7qay2xq
Generalizing to Unseen Domains: A Survey on Domain Generalization
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2103.03097v5 fatcat:nvwnepfeb5adjfghuvuqx5hopyOther Versions
We categorize recent algorithms into three classes: data manipulation, representation learning, and learning strategy, and present several popular algorithms in detail for each category. ...
Domain generalization deals with a challenging setting where one or several different but related domain(s) are given, and the goal is to learn a model that can generalize to an unseen test domain. ...
There are many generalization-related research topics such as domain adaptation, meta-learning, transfer learning, covariate shift, and so on. ...
arXiv:2103.03097v6
fatcat:gztglwp4nrdwbnddjr7t3j3ize
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