1,318 research outputs found
Towards optimally abstaining from prediction with OOD test examples
A common challenge across all areas of machine learning is that training data
is not distributed like test data, due to natural shifts, "blind spots," or
adversarial examples; such test examples are referred to as out-of-distribution
(OOD) test examples. We consider a model where one may abstain from predicting,
at a fixed cost. In particular, our transductive abstention algorithm takes
labeled training examples and unlabeled test examples as input, and provides
predictions with optimal prediction loss guarantees. The loss bounds match
standard generalization bounds when test examples are i.i.d. from the training
distribution, but add an additional term that is the cost of abstaining times
the statistical distance between the train and test distribution (or the
fraction of adversarial examples). For linear regression, we give a
polynomial-time algorithm based on Celis-Dennis-Tapia optimization algorithms.
For binary classification, we show how to efficiently implement it using a
proper agnostic learner (i.e., an Empirical Risk Minimizer) for the class of
interest. Our work builds on a recent abstention algorithm of Goldwasser,
Kalais, and Montasser (2020) for transductive binary classification.Comment: In NeurIPS 2021 (+spotlight), 24 page
Tweet your vote. How content analysis of social network can improve our knowledge of citizens’ policy preferences. An application to Italy and France
4The growing usage of social media by a wider audience of citizens sharply increases the possibility to investigate the web as a device to explore and track policy preferences. In the present paper we apply the recent method proposed in Hopkins and King (2010) to three different scenarios, by analyzing on one side the on-line popularity of Italian political leaders throughout 2011, and on the other the voting intention of French internet-users in both the 2012 Presidential ballot and in the subsequent Legislative election. Despite internet users are not necessarily representative of the whole population of country’s citizens, our analysis shows a remarkable ability of social-media to forecast electoral results as well as a noteworthy correlation between social-media and traditional mass surveys results. We also illustrate that the predictive ability of social-media analysis strengthens as the number of citizens’ expressing on-line their opinion increases, provided they act consistently on that (i.e. apart from high abstention rates).openA. Ceron; L. Curini; S.M. Iacus; G. PorroA., Ceron; L., Curini; S. M., Iacus; Porro, Giusepp
Selective Nonparametric Regression via Testing
Prediction with the possibility of abstention (or selective prediction) is an
important problem for error-critical machine learning applications. While
well-studied in the classification setup, selective approaches to regression
are much less developed. In this work, we consider the nonparametric
heteroskedastic regression problem and develop an abstention procedure via
testing the hypothesis on the value of the conditional variance at a given
point. Unlike existing methods, the proposed one allows to account not only for
the value of the variance itself but also for the uncertainty of the
corresponding variance predictor. We prove non-asymptotic bounds on the risk of
the resulting estimator and show the existence of several different convergence
regimes. Theoretical analysis is illustrated with a series of experiments on
simulated and real-world data
Online Decision Mediation
Consider learning a decision support assistant to serve as an intermediary
between (oracle) expert behavior and (imperfect) human behavior: At each time,
the algorithm observes an action chosen by a fallible agent, and decides
whether to *accept* that agent's decision, *intervene* with an alternative, or
*request* the expert's opinion. For instance, in clinical diagnosis,
fully-autonomous machine behavior is often beyond ethical affordances, thus
real-world decision support is often limited to monitoring and forecasting.
Instead, such an intermediary would strike a prudent balance between the former
(purely prescriptive) and latter (purely descriptive) approaches, while
providing an efficient interface between human mistakes and expert feedback. In
this work, we first formalize the sequential problem of *online decision
mediation* -- that is, of simultaneously learning and evaluating mediator
policies from scratch with *abstentive feedback*: In each round, deferring to
the oracle obviates the risk of error, but incurs an upfront penalty, and
reveals the otherwise hidden expert action as a new training data point.
Second, we motivate and propose a solution that seeks to trade off (immediate)
loss terms against (future) improvements in generalization error; in doing so,
we identify why conventional bandit algorithms may fail. Finally, through
experiments and sensitivities on a variety of datasets, we illustrate
consistent gains over applicable benchmarks on performance measures with
respect to the mediator policy, the learned model, and the decision-making
system as a whole
Survey on Leveraging Uncertainty Estimation Towards Trustworthy Deep Neural Networks: The Case of Reject Option and Post-training Processing
Although neural networks (especially deep neural networks) have achieved
\textit{better-than-human} performance in many fields, their real-world
deployment is still questionable due to the lack of awareness about the
limitation in their knowledge. To incorporate such awareness in the machine
learning model, prediction with reject option (also known as selective
classification or classification with abstention) has been proposed in
literature. In this paper, we present a systematic review of the prediction
with the reject option in the context of various neural networks. To the best
of our knowledge, this is the first study focusing on this aspect of neural
networks. Moreover, we discuss different novel loss functions related to the
reject option and post-training processing (if any) of network output for
generating suitable measurements for knowledge awareness of the model. Finally,
we address the application of the rejection option in reducing the prediction
time for the real-time problems and present a comprehensive summary of the
techniques related to the reject option in the context of extensive variety of
neural networks. Our code is available on GitHub:
\url{https://github.com/MehediHasanTutul/Reject_option
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