194 research outputs found
Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation
Controlling bias in training datasets is vital for ensuring equal treatment,
or parity, between different groups in downstream applications. A naive
solution is to transform the data so that it is statistically independent of
group membership, but this may throw away too much information when a
reasonable compromise between fairness and accuracy is desired. Another common
approach is to limit the ability of a particular adversary who seeks to
maximize parity. Unfortunately, representations produced by adversarial
approaches may still retain biases as their efficacy is tied to the complexity
of the adversary used during training. To this end, we theoretically establish
that by limiting the mutual information between representations and protected
attributes, we can assuredly control the parity of any downstream classifier.
We demonstrate an effective method for controlling parity through mutual
information based on contrastive information estimators and show that they
outperform approaches that rely on variational bounds based on complex
generative models. We test our approach on UCI Adult and Heritage Health
datasets and demonstrate that our approach provides more informative
representations across a range of desired parity thresholds while providing
strong theoretical guarantees on the parity of any downstream algorithm.Comment: This version fixes an error in Theorem 2 of the original manuscript
that appeared at the Proceedings of the 35th AAAI Conference on Artificial
Intelligence (AAAI-21). Code is available at
https://github.com/umgupta/fairness-via-contrastive-estimatio
A Study of Synchronization Techniques for Optical Communication Systems
The study of synchronization techniques and related topics in the design of high data rate, deep space, optical communication systems was reported. Data cover: (1) effects of timing errors in narrow pulsed digital optical systems, (2) accuracy of microwave timing systems operating in low powered optical systems, (3) development of improved tracking systems for the optical channel and determination of their tracking performance, (4) development of usable photodetector mathematical models for application to analysis and performance design in communication receivers, and (5) study application of multi-level block encoding to optical transmission of digital data
Statistical methods for scale-invariant and multifractal stochastic processes.
This thesis focuses on stochastic modeling, and statistical methods, in finance and in climate science. Two financial markets, short-term interest rates and electricity prices, are analyzed. We find that the evidence of mean reversion in short-term interest rates is week, while the “log-returns” of electricity prices have significant anti-correlations. More importantly, empirical analyses confirm the multifractal nature of these financial markets, and we propose multifractal models that incorporate the specific conditional mean reversion and level dependence.
A second topic in the thesis is the analysis of regional (5â—¦ Ă— 5â—¦ and 2â—¦ Ă— 2â—¦ latitude- longitude) globally gridded surface temperature series for the time period 1900-2014, with respect to a linear trend and long-range dependence. We find statistically significant trends in most regions. However, we also demonstrate that the existence of a second scaling regime on decadal time scales will have an impact on trend detection.
The last main result is an approximative maximum likelihood (ML) method for the log- normal multifractal random walk. It is shown that the ML method has applications beyond parameter estimation, and can for instance be used to compute various risk measures in financial markets
Sparse Modeling for Image and Vision Processing
In recent years, a large amount of multi-disciplinary research has been
conducted on sparse models and their applications. In statistics and machine
learning, the sparsity principle is used to perform model selection---that is,
automatically selecting a simple model among a large collection of them. In
signal processing, sparse coding consists of representing data with linear
combinations of a few dictionary elements. Subsequently, the corresponding
tools have been widely adopted by several scientific communities such as
neuroscience, bioinformatics, or computer vision. The goal of this monograph is
to offer a self-contained view of sparse modeling for visual recognition and
image processing. More specifically, we focus on applications where the
dictionary is learned and adapted to data, yielding a compact representation
that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics
and Visio
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