4,110 research outputs found
Bethe Projections for Non-Local Inference
Many inference problems in structured prediction are naturally solved by
augmenting a tractable dependency structure with complex, non-local auxiliary
objectives. This includes the mean field family of variational inference
algorithms, soft- or hard-constrained inference using Lagrangian relaxation or
linear programming, collective graphical models, and forms of semi-supervised
learning such as posterior regularization. We present a method to
discriminatively learn broad families of inference objectives, capturing
powerful non-local statistics of the latent variables, while maintaining
tractable and provably fast inference using non-Euclidean projected gradient
descent with a distance-generating function given by the Bethe entropy. We
demonstrate the performance and flexibility of our method by (1) extracting
structured citations from research papers by learning soft global constraints,
(2) achieving state-of-the-art results on a widely-used handwriting recognition
task using a novel learned non-convex inference procedure, and (3) providing a
fast and highly scalable algorithm for the challenging problem of inference in
a collective graphical model applied to bird migration.Comment: minor bug fix to appendix. appeared in UAI 201
Generative Adversarial Networks for Mitigating Biases in Machine Learning Systems
In this paper, we propose a new framework for mitigating biases in machine
learning systems. The problem of the existing mitigation approaches is that
they are model-oriented in the sense that they focus on tuning the training
algorithms to produce fair results, while overlooking the fact that the
training data can itself be the main reason for biased outcomes. Technically
speaking, two essential limitations can be found in such model-based
approaches: 1) the mitigation cannot be achieved without degrading the accuracy
of the machine learning models, and 2) when the data used for training are
largely biased, the training time automatically increases so as to find
suitable learning parameters that help produce fair results. To address these
shortcomings, we propose in this work a new framework that can largely mitigate
the biases and discriminations in machine learning systems while at the same
time enhancing the prediction accuracy of these systems. The proposed framework
is based on conditional Generative Adversarial Networks (cGANs), which are used
to generate new synthetic fair data with selective properties from the original
data. We also propose a framework for analyzing data biases, which is important
for understanding the amount and type of data that need to be synthetically
sampled and labeled for each population group. Experimental results show that
the proposed solution can efficiently mitigate different types of biases, while
at the same time enhancing the prediction accuracy of the underlying machine
learning model
Asset Allocation under the Basel Accord Risk Measures
Financial institutions are currently required to meet more stringent capital
requirements than they were before the recent financial crisis; in particular,
the capital requirement for a large bank's trading book under the Basel 2.5
Accord more than doubles that under the Basel II Accord. The significant
increase in capital requirements renders it necessary for banks to take into
account the constraint of capital requirement when they make asset allocation
decisions. In this paper, we propose a new asset allocation model that
incorporates the regulatory capital requirements under both the Basel 2.5
Accord, which is currently in effect, and the Basel III Accord, which was
recently proposed and is currently under discussion. We propose an unified
algorithm based on the alternating direction augmented Lagrangian method to
solve the model; we also establish the first-order optimality of the limit
points of the sequence generated by the algorithm under some mild conditions.
The algorithm is simple and easy to implement; each step of the algorithm
consists of solving convex quadratic programming or one-dimensional
subproblems. Numerical experiments on simulated and real market data show that
the algorithm compares favorably with other existing methods, especially in
cases in which the model is non-convex
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