5 research outputs found
Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions
We introduce Conformal Decision Theory, a framework for producing safe
autonomous decisions despite imperfect machine learning predictions. Examples
of such decisions are ubiquitous, from robot planning algorithms that rely on
pedestrian predictions, to calibrating autonomous manufacturing to exhibit high
throughput and low error, to the choice of trusting a nominal policy versus
switching to a safe backup policy at run-time. The decisions produced by our
algorithms are safe in the sense that they come with provable statistical
guarantees of having low risk without any assumptions on the world model
whatsoever; the observations need not be I.I.D. and can even be adversarial.
The theory extends results from conformal prediction to calibrate decisions
directly, without requiring the construction of prediction sets. Experiments
demonstrate the utility of our approach in robot motion planning around humans,
automated stock trading, and robot manufacturing.Comment: 8 pages, 5 figure
Computationally efficient versions of conformal predictive distributions
Conformal predictive systems are a recent modification of conformal
predictors that output, in regression problems, probability distributions for
labels of test observations rather than set predictions. The extra information
provided by conformal predictive systems may be useful, e.g., in decision
making problems. Conformal predictive systems inherit the relative
computational inefficiency of conformal predictors. In this paper we discuss
two computationally efficient versions of conformal predictive systems, which
we call split conformal predictive systems and cross-conformal predictive
systems. The main advantage of split conformal predictive systems is their
guaranteed validity, whereas for cross-conformal predictive systems validity
only holds empirically and in the absence of excessive randomization. The main
advantage of cross-conformal predictive systems is their greater predictive
efficiency.Comment: 31 pages, 14 figures, 1 table. The conference version published in
the Proceedings of COPA 2018, and the journal version is to appear in
Neurocomputin
Online Algorithms with Uncertainty-Quantified Predictions
Online algorithms with predictions have become a trending topic in the field
of beyond worst-case analysis of algorithms. These algorithms incorporate
predictions about the future to obtain performance guarantees that are of high
quality when the predictions are good, while still maintaining bounded
worst-case guarantees when predictions are arbitrarily poor. In general, the
algorithm is assumed to be unaware of the prediction's quality. However, recent
developments in the machine learning literature have studied techniques for
providing uncertainty quantification on machine-learned predictions, which
describes how certain a model is about its quality. This paper examines the
question of how to optimally utilize uncertainty-quantified predictions in the
design of online algorithms. In particular, we consider predictions augmented
with uncertainty quantification describing the likelihood of the ground truth
falling in a certain range, designing online algorithms with these
probabilistic predictions for two classic online problems: ski rental and
online search. In each case, we demonstrate that non-trivial modifications to
algorithm design are needed to fully leverage the probabilistic predictions.
Moreover, we consider how to utilize more general forms of uncertainty
quantification, proposing a framework based on online learning that learns to
exploit uncertainty quantification to make optimal decisions in multi-instance
settings
Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control
We introduce a framework for calibrating machine learning models so that
their predictions satisfy explicit, finite-sample statistical guarantees. Our
calibration algorithm works with any underlying model and (unknown)
data-generating distribution and does not require model refitting. The
framework addresses, among other examples, false discovery rate control in
multi-label classification, intersection-over-union control in instance
segmentation, and the simultaneous control of the type-1 error of outlier
detection and confidence set coverage in classification or regression. Our main
insight is to reframe the risk-control problem as multiple hypothesis testing,
enabling techniques and mathematical arguments different from those in the
previous literature. We use our framework to provide new calibration methods
for several core machine learning tasks with detailed worked examples in
computer vision and tabular medical data.Comment: Code available at https://github.com/aangelopoulos/lt