27 research outputs found
Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach
We present a new method of training energy-based models (EBMs) for anomaly
detection that leverages low-dimensional structures within data. The proposed
algorithm, Manifold Projection-Diffusion Recovery (MPDR), first perturbs a data
point along a low-dimensional manifold that approximates the training dataset.
Then, EBM is trained to maximize the probability of recovering the original
data. The training involves the generation of negative samples via MCMC, as in
conventional EBM training, but from a different distribution concentrated near
the manifold. The resulting near-manifold negative samples are highly
informative, reflecting relevant modes of variation in data. An energy function
of MPDR effectively learns accurate boundaries of the training data
distribution and excels at detecting out-of-distribution samples. Experimental
results show that MPDR exhibits strong performance across various anomaly
detection tasks involving diverse data types, such as images, vectors, and
acoustic signals.Comment: NeurIPS 202
CRT-116 The Impact of Bifucation Lesion in Acute Myocardial Infarction Undergoing Percutaneous Coronary Intervention Two Years Clinical Outcomes
Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions
We consider local kernel metric learning for off-policy evaluation (OPE) of
deterministic policies in contextual bandits with continuous action spaces. Our
work is motivated by practical scenarios where the target policy needs to be
deterministic due to domain requirements, such as prescription of treatment
dosage and duration in medicine. Although importance sampling (IS) provides a
basic principle for OPE, it is ill-posed for the deterministic target policy
with continuous actions. Our main idea is to relax the target policy and pose
the problem as kernel-based estimation, where we learn the kernel metric in
order to minimize the overall mean squared error (MSE). We present an analytic
solution for the optimal metric, based on the analysis of bias and variance.
Whereas prior work has been limited to scalar action spaces or kernel bandwidth
selection, our work takes a step further being capable of vector action spaces
and metric optimization. We show that our estimator is consistent, and
significantly reduces the MSE compared to baseline OPE methods through
experiments on various domains
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