460 research outputs found
Target Contrastive Pessimistic Discriminant Analysis
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
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
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
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
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 enerative Gradual
Dmain daptation with Optimal 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
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
L'objectiu de la present recerca és abordar la variació i ductilitat de conceptes aparentment clars i inequÃvocs relacionats amb els rols professionals habituals. L'estudi se centra en les estructures semà ntiques, 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 brità nics. L'anà lisi lingüÃstica, 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 sociopolÃtica i ideològica de la plataforma de publicació
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