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Erik Learned-Miller

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6 papers

1 author row

NeurIPS Conference 2021 Conference Paper

Universal Off-Policy Evaluation

Yash Chandak
Scott Niekum
Bruno da Silva
Erik Learned-Miller
Emma Brunskill
Philip S. Thomas

When faced with sequential decision-making problems, it is often useful to be able to predict what would happen if decisions were made using a new policy. Those predictions must often be based on data collected under some previously used decision-making rule. Many previous methods enable such off-policy (or counterfactual) estimation of the expected value of a performance measure called the return. In this paper, we take the first steps towards a 'universal off-policy estimator' (UnO)---one that provides off-policy estimates and high-confidence bounds for any parameter of the return distribution. We use UnO for estimating and simultaneously bounding the mean, variance, quantiles/median, inter-quantile range, CVaR, and the entire cumulative distribution of returns. Finally, we also discuss UnO's applicability in various settings, including fully observable, partially observable (i. e. , with unobserved confounders), Markovian, non-Markovian, stationary, smoothly non-stationary, and discrete distribution shifts.

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NeurIPS Conference 2017 Conference Paper

Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples

Haw-Shiuan Chang
Erik Learned-Miller
Andrew McCallum

Self-paced learning and hard example mining re-weight training instances to improve learning accuracy. This paper presents two improved alternatives based on lightweight estimates of sample uncertainty in stochastic gradient descent (SGD): the variance in predicted probability of the correct class across iterations of mini-batch SGD, and the proximity of the correct class probability to the decision threshold. Extensive experimental results on six datasets show that our methods reliably improve accuracy in various network architectures, including additional gains on top of other popular training techniques, such as residual learning, momentum, ADAM, batch normalization, dropout, and distillation.

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JMLR Journal 2012 Journal Article

Bounding the Probability of Error for High Precision Optical Character Recognition

Gary B. Huang
Andrew Kae
Carl Doersch
Erik Learned-Miller

We consider a model for which it is important, early in processing, to estimate some variables with high precision, but perhaps at relatively low recall. If some variables can be identified with near certainty, they can be conditioned upon, allowing further inference to be done efficiently. Specifically, we consider optical character recognition (OCR) systems that can be bootstrapped by identifying a subset of correctly translated document words with very high precision. This "clean set" is subsequently used as document-specific training data. While OCR systems produce confidence measures for the identity of each letter or word, thresholding these values still produces a significant number of errors. We introduce a novel technique for identifying a set of correct words with very high precision. Rather than estimating posterior probabilities, we bound the probability that any given word is incorrect using an approximate worst case analysis. We give empirical results on a data set of difficult historical newspaper scans, demonstrating that our method for identifying correct words makes only two errors in 56 documents. Using document-specific character models generated from this data, we are able to reduce the error over properly segmented characters by 34.1% from an initial OCR system's translation. [abs] [ pdf ][ bib ] &copy JMLR 2012. ( edit, beta )

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NeurIPS Conference 2012 Conference Paper

Learning to Align from Scratch

Gary Huang
Marwan Mattar
Honglak Lee
Erik Learned-Miller

Unsupervised joint alignment of images has been demonstrated to improve performance on recognition tasks such as face verification. Such alignment reduces undesired variability due to factors such as pose, while only requiring weak supervision in the form of poorly aligned examples. However, prior work on unsupervised alignment of complex, real world images has required the careful selection of feature representation based on hand-crafted image descriptors, in order to achieve an appropriate, smooth optimization landscape. In this paper, we instead propose a novel combination of unsupervised joint alignment with unsupervised feature learning. Specifically, we incorporate deep learning into the {\em congealing} alignment framework. Through deep learning, we obtain features that can represent the image at differing resolutions based on network depth, and that are tuned to the statistics of the specific data being aligned. In addition, we modify the learning algorithm for the restricted Boltzmann machine by incorporating a group sparsity penalty, leading to a topographic organization on the learned filters and improving subsequent alignment results. We apply our method to the Labeled Faces in the Wild database (LFW). Using the aligned images produced by our proposed unsupervised algorithm, we achieve a significantly higher accuracy in face verification than obtained using the original face images, prior work in unsupervised alignment, and prior work in supervised alignment. We also match the accuracy for the best available, but unpublished method.

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NeurIPS Conference 2004 Conference Paper

Joint MRI Bias Removal Using Entropy Minimization Across Images

Erik Learned-Miller
Parvez Ahammad

The correction of bias in magnetic resonance images is an important problem in medical image processing. Most previous approaches have used a maximum likelihood method to increase the likelihood of the pix- els in a single image by adaptively estimating a correction to the unknown image bias field. The pixel likelihoods are defined either in terms of a pre-existing tissue model, or non-parametrically in terms of the image's own pixel values. In both cases, the specific location of a pixel in the im- age is not used to calculate the likelihoods. We suggest a new approach in which we simultaneously eliminate the bias from a set of images of the same anatomy, but from different patients. We use the statistics from the same location across different images, rather than within an image, to eliminate bias fields from all of the images simultaneously. The method builds a "multi-resolution" non-parametric tissue model conditioned on image location while eliminating the bias fields associated with the orig- inal image set. We present experiments on both synthetic and real MR data sets, and present comparisons with other methods.

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NeurIPS Conference 2004 Conference Paper

Learning Hyper-Features for Visual Identification

Andras Ferencz
Erik Learned-Miller
Jitendra Malik

We address the problem of identifying specific instances of a class (cars) from a set of images all belonging to that class. Although we cannot build a model for any particular instance (as we may be provided with only one "training" example of it), we can use information extracted from observ- ing other members of the class. We pose this task as a learning problem, in which the learner is given image pairs, labeled as matching or not, and must discover which image features are most consistent for matching in- stances and discriminative for mismatches. We explore a patch based representation, where we model the distributions of similarity measure- ments defined on the patches. Finally, we describe an algorithm that selects the most salient patches based on a mutual information criterion. This algorithm performs identification well for our challenging dataset of car images, after matching only a few, well chosen patches.

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