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    <title>DALI 2019 - Data, Learning and Inference</title>
    <description>3rd-5th January, George, South Africa</description>
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        <title>Machine Learning Theory</title>
        <description>This year, the workshop brings together researchers from diverse theoretical areas of data sciences, e.g., Machine Learning Theory, Mathematical Statistics, and related fields. We have an exciting list of scheduled talks with plenty of breaks in between for one on one discussions.</description>
        <pubDate>Sat, 05 Jan 2019 00:00:00 +0000</pubDate>
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        <title>Security and Robustness</title>
        <description>In recent years, there has been a proliferation of ML algorithms in several safety-critical applications, including (amongst others), face detection, data centers, biometric identification and self driving cars. Even outside of safety critical systems, ML algorithms can lead to severely undesirable outcomes, including data leakage and racist/biased predictions. This calls for the need to have stronger checks on ML algorithms and the ability to train ML models not just to fit training data well, but also to satisfy auxiliary properties necessary for safe deployment. This workshop will discuss challenges and opportunties around developing secure ML systems, bringing together perspectives from formal verification, robust learning, robotics and autonomous systems and privacy.</description>
        <pubDate>Sat, 05 Jan 2019 00:00:00 +0000</pubDate>
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        <title>Healthcare in Machine Learning</title>
        <description>Health presents one of the most challenging and under-investigated domains of machine learning research. This offers an exciting opportunity for machine learning techniques to impact healthcare in a meaningful way. In this workshop, we will investigate and discuss different perspectives that are essential for successful research and deployment of machine learning for a positive impact in health.</description>
        <pubDate>Sat, 05 Jan 2019 00:00:00 +0000</pubDate>
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        <title>Day 3</title>
        <description></description>
        <pubDate>Sat, 05 Jan 2019 00:00:00 +0000</pubDate>
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        <title>Day 2</title>
        <description></description>
        <pubDate>Fri, 04 Jan 2019 00:00:00 +0000</pubDate>
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        <title>Optimization</title>
        <description></description>
        <pubDate>Thu, 03 Jan 2019 00:00:00 +0000</pubDate>
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        <title>Fairness and Transparency in Machine Learning</title>
        <description></description>
        <pubDate>Thu, 03 Jan 2019 00:00:00 +0000</pubDate>
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        <title>Deep Reinforcement Learning and Robotics</title>
        <description>TBA</description>
        <pubDate>Thu, 03 Jan 2019 00:00:00 +0000</pubDate>
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        <title>Day 1</title>
        <description></description>
        <pubDate>Thu, 03 Jan 2019 00:00:00 +0000</pubDate>
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        <title>Day 0</title>
        <description></description>
        <pubDate>Wed, 02 Jan 2019 00:00:00 +0000</pubDate>
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