460 research outputs found

    Target Contrastive Pessimistic Discriminant Analysis

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    Domain-adaptive classifiers learn from a source domain and aim to generalize to a target domain. If the classifier's assumptions on the relationship between domains (e.g. covariate shift) are valid, then it will usually outperform a non-adaptive source classifier. Unfortunately, it can perform substantially worse when its assumptions are invalid. Validating these assumptions requires labeled target samples, which are usually not available. We argue that, in order to make domain-adaptive classifiers more practical, it is necessary to focus on robust methods; robust in the sense that the model still achieves a particular level of performance without making strong assumptions on the relationship between domains. With this objective in mind, we formulate a conservative parameter estimator that only deviates from the source classifier when a lower or equal risk is guaranteed for all possible labellings of the given target samples. We derive the corresponding estimator for a discriminant analysis model, and show that its risk is actually strictly smaller than that of the source classifier. Experiments indicate that our classifier outperforms state-of-the-art classifiers for geographically biased samples.Comment: 9 pages, no figures, 2 tables. arXiv admin note: substantial text overlap with arXiv:1706.0808

    A review of domain adaptation without target labels

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    Domain adaptation has become a prominent problem setting in machine learning and related fields. This review asks the question: how can a classifier learn from a source domain and generalize to a target domain? We present a categorization of approaches, divided into, what we refer to as, sample-based, feature-based and inference-based methods. Sample-based methods focus on weighting individual observations during training based on their importance to the target domain. Feature-based methods revolve around on mapping, projecting and representing features such that a source classifier performs well on the target domain and inference-based methods incorporate adaptation into the parameter estimation procedure, for instance through constraints on the optimization procedure. Additionally, we review a number of conditions that allow for formulating bounds on the cross-domain generalization error. Our categorization highlights recurring ideas and raises questions important to further research.Comment: 20 pages, 5 figure

    Epidemiological Models and Epistemic Perspectives: How Scientific Pluralism may be Misconstrued

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    In a scenario characterized by unpredictable developments, such as the recent COVID-19 pandemic, epidemiological models have played a leading part, having been especially widely deployed for forecasting purposes. In this paper, two real-world examples of modeling are examined in support of the proposition that science can convey inconsistent as well as genuinely perspectival representations of the world. Reciprocally inconsistent outcomes are grounded on incompatible assumptions, whereas perspectival outcomes are grounded on compatible assumptions and illuminate different aspects of the same object of interest. In both cases, models should be viewed as expressions of specific assumptions and unconstrained choices on the part of those designing them. The coexistence of a variety of models reflects a primary feature of science, namely its pluralism. It is herein proposed that recent over-exposure to science’s inner workings and disputes such as those pertaining to models, may have led the public to perceive pluralism as a flaw – or more specifically, as disunity or fragmentation, which in turn may have been interpreted as a sign of unreliability. In conclusion, given the inescapability of pluralism, suggestions are offered as to how to counteract distorted perceptions of science, and thereby enhance scientific literacy

    Behavior Prior Representation learning for Offline Reinforcement Learning

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    Offline reinforcement learning (RL) struggles in environments with rich and noisy inputs, where the agent only has access to a fixed dataset without environment interactions. Past works have proposed common workarounds based on the pre-training of state representations, followed by policy training. In this work, we introduce a simple, yet effective approach for learning state representations. Our method, Behavior Prior Representation (BPR), learns state representations with an easy-to-integrate objective based on behavior cloning of the dataset: we first learn a state representation by mimicking actions from the dataset, and then train a policy on top of the fixed representation, using any off-the-shelf Offline RL algorithm. Theoretically, we prove that BPR carries out performance guarantees when integrated into algorithms that have either policy improvement guarantees (conservative algorithms) or produce lower bounds of the policy values (pessimistic algorithms). Empirically, we show that BPR combined with existing state-of-the-art Offline RL algorithms leads to significant improvements across several offline control benchmarks

    Gradual Domain Adaptation: Theory and Algorithms

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    Unsupervised domain adaptation (UDA) adapts a model from a labeled source domain to an unlabeled target domain in a one-off way. Though widely applied, UDA faces a great challenge whenever the distribution shift between the source and the target is large. Gradual domain adaptation (GDA) mitigates this limitation by using intermediate domains to gradually adapt from the source to the target domain. In this work, we first theoretically analyze gradual self-training, a popular GDA algorithm, and provide a significantly improved generalization bound compared with Kumar et al. (2020). Our theoretical analysis leads to an interesting insight: to minimize the generalization error on the target domain, the sequence of intermediate domains should be placed uniformly along the Wasserstein geodesic between the source and target domains. The insight is particularly useful under the situation where intermediate domains are missing or scarce, which is often the case in real-world applications. Based on the insight, we propose G\textbf{G}enerative Gradual DO\textbf{O}main A\textbf{A}daptation with Optimal T\textbf{T}ransport (GOAT), an algorithmic framework that can generate intermediate domains in a data-dependent way. More concretely, we first generate intermediate domains along the Wasserstein geodesic between two given consecutive domains in a feature space, then apply gradual self-training to adapt the source-trained classifier to the target along the sequence of intermediate domains. Empirically, we demonstrate that our GOAT framework can improve the performance of standard GDA when the given intermediate domains are scarce, significantly broadening the real-world application scenarios of GDA. Our code is available at https://github.com/yifei-he/GOAT.Comment: arXiv admin note: substantial text overlap with arXiv:2204.0820

    Semi-generative modelling: learning with cause and effect features

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    We consider a case of covariate shift where prior causal inference or expert knowledge has identified some features as effects, and show how this setting, when analysed from a causal perspective, gives rise to a semi-generative modelling framework: P(Y,X_eff|Xcau)

    The malleability behind terms referring to common professional roles : the current meaning of ?boss? in British newspapers

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    L'objectiu de la present recerca és abordar la variació i ductilitat de conceptes aparentment clars i inequívocs relacionats amb els rols professionals habituals. L'estudi se centra en les estructures semàntiques, i subsegüents models cognitius, associats amb el terme 'boss', tal com són expressats i transmesos en l'actualitat a través dels grans mitjans de comunicació britànics. L'anàlisi lingüística, qualitatiu i quantitatiu, d'un corpus significatiu de textos en els quals apareix aquest terme mostra clares diferències en el seu significat, depenent de factors clau com l'orientació sociopolítica i ideològica de la plataforma de publicació
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