3,819 research outputs found
Learning Representations of Emotional Speech with Deep Convolutional Generative Adversarial Networks
Automatically assessing emotional valence in human speech has historically
been a difficult task for machine learning algorithms. The subtle changes in
the voice of the speaker that are indicative of positive or negative emotional
states are often "overshadowed" by voice characteristics relating to emotional
intensity or emotional activation. In this work we explore a representation
learning approach that automatically derives discriminative representations of
emotional speech. In particular, we investigate two machine learning strategies
to improve classifier performance: (1) utilization of unlabeled data using a
deep convolutional generative adversarial network (DCGAN), and (2) multitask
learning. Within our extensive experiments we leverage a multitask annotated
emotional corpus as well as a large unlabeled meeting corpus (around 100
hours). Our speaker-independent classification experiments show that in
particular the use of unlabeled data in our investigations improves performance
of the classifiers and both fully supervised baseline approaches are
outperformed considerably. We improve the classification of emotional valence
on a discrete 5-point scale to 43.88% and on a 3-point scale to 49.80%, which
is competitive to state-of-the-art performance
Censored and Fair Universal Representations using Generative Adversarial Models
We present a data-driven framework for learning \textit{censored and fair
universal representations} (CFUR) that ensure statistical fairness guarantees
for all downstream learning tasks that may not be known \textit{a priori}. Our
framework leverages recent advancements in adversarial learning to allow a data
holder to learn censored and fair representations that decouple a set of
sensitive attributes from the rest of the dataset. The resulting problem of
finding the optimal randomizing mechanism with specific fairness/censoring
guarantees is formulated as a constrained minimax game between an encoder and
an adversary where the constraint ensures a measure of usefulness (utility) of
the representation. We show that for appropriately chosen adversarial loss
functions, our framework enables defining demographic parity for fair
representations and also clarifies {the optimal adversarial strategy against
strong information-theoretic adversaries}. We evaluate the performance of our
proposed framework on multi-dimensional Gaussian mixture models and publicly
datasets including the UCI Census, GENKI, Human Activity Recognition (HAR), and
the UTKFace. Our experimental results show that multiple sensitive features can
be effectively censored while ensuring accuracy for several \textit{a priori}
unknown downstream tasks. Finally, our results also make precise the tradeoff
between censoring and fidelity for the representation as well as the
fairness-utility tradeoffs for downstream tasks.Comment: 45 pages, 23 Figures. arXiv admin note: text overlap with
arXiv:1807.0530
Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation
Computer vision models learn to perform a task by capturing relevant
statistics from training data. It has been shown that models learn spurious
age, gender, and race correlations when trained for seemingly unrelated tasks
like activity recognition or image captioning. Various mitigation techniques
have been presented to prevent models from utilizing or learning such biases.
However, there has been little systematic comparison between these techniques.
We design a simple but surprisingly effective visual recognition benchmark for
studying bias mitigation. Using this benchmark, we provide a thorough analysis
of a wide range of techniques. We highlight the shortcomings of popular
adversarial training approaches for bias mitigation, propose a simple but
similarly effective alternative to the inference-time Reducing Bias
Amplification method of Zhao et al., and design a domain-independent training
technique that outperforms all other methods. Finally, we validate our findings
on the attribute classification task in the CelebA dataset, where attribute
presence is known to be correlated with the gender of people in the image, and
demonstrate that the proposed technique is effective at mitigating real-world
gender bias.Comment: To appear in CVPR 202
Robust and Fair Machine Learning under Distribution Shift
Machine learning algorithms have been widely used in real world applications. The development of these techniques has brought huge benefits for many AI-related tasks, such as natural language processing, image classification, video analysis, and so forth. In traditional machine learning algorithms, we usually assume that the training data and test data are independently and identically distributed (iid), indicating that the model learned from the training data can be well applied to the test data with good prediction performance. However, this assumption is quite restrictive because the distribution shift can exist from the training data to the test data in many scenarios. In addition, the goal of traditional machine learning model is to maximize the prediction performance, e.g., accuracy, based on the historical training data, which may tend to make unfair predictions for some particular individual or groups. In the literature, researchers either focus on building robust machine learning models under data distribution shift or achieving fairness separately, without considering to solve them simultaneously.
The goal of this dissertation is to solve the above challenging issues in fair machine learning under distribution shift. We start from building an agnostic fair framework in federated learning as the data distribution is more diversified and distribution shift exists from the training data to the test data. Then we build a robust framework to address the sample selection bias for fair classification. Next we solve the sample selection bias issue for fair regression. Finally, we propose an adversarial framework to build a personalized model in the distributed setting where the distribution shift exists between different users.
In this dissertation, we conduct the following research for fair machine learning under distribution shift. • We develop a fairness-aware agnostic federated learning framework (AgnosticFair) to deal with the challenge of unknown testing distribution; • We propose a framework for robust and fair learning under sample selection bias; • We develop a framework for fair regression under sample selection bias when dependent variable values of a set of samples from the training data are missing as a result of another hidden process; • We propose a learning framework that allows an individual user to build a personalized model in a distributed setting, where the distribution shift exists among different users
Analysing Fairness of Privacy-Utility Mobility Models
Preserving the individuals' privacy in sharing spatial-temporal datasets is
critical to prevent re-identification attacks based on unique trajectories.
Existing privacy techniques tend to propose ideal privacy-utility tradeoffs,
however, largely ignore the fairness implications of mobility models and
whether such techniques perform equally for different groups of users. The
quantification between fairness and privacy-aware models is still unclear and
there barely exists any defined sets of metrics for measuring fairness in the
spatial-temporal context. In this work, we define a set of fairness metrics
designed explicitly for human mobility, based on structural similarity and
entropy of the trajectories. Under these definitions, we examine the fairness
of two state-of-the-art privacy-preserving models that rely on GAN and
representation learning to reduce the re-identification rate of users for data
sharing. Our results show that while both models guarantee group fairness in
terms of demographic parity, they violate individual fairness criteria,
indicating that users with highly similar trajectories receive disparate
privacy gain. We conclude that the tension between the re-identification task
and individual fairness needs to be considered for future spatial-temporal data
analysis and modelling to achieve a privacy-preserving fairness-aware setting
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