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    <dc:date>2026-10-22T13:38:08Z</dc:date>
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    <title>Derivations of polynomial rings and isotropy groups [RESTRICTED THESIS-01 Year]</title>
    <link>https://dspace.iiti.ac.in/handle/123456789/19252</link>
    <description>Title: Derivations of polynomial rings and isotropy groups [RESTRICTED THESIS-01 Year]
Authors: Singh, Sneha
Abstract: [Abstract is restricted for 01 Year, due to IPR related issue]</description>
    <dc:date>2026-05-14T00:00:00Z</dc:date>
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    <title>Robust and efficient machine learning under data uncertainty: from loss functions to data-aware representations</title>
    <link>https://dspace.iiti.ac.in/handle/123456789/19184</link>
    <description>Title: Robust and efficient machine learning under data uncertainty: from loss functions to data-aware representations
Authors: Akhtar, Mushir
Abstract: Reliable machine learning in real-world environments is often limited not by the expressive power of a model alone, but by how the learning objective responds to corrupted labels, outliers, overlapping classes, class imbalance, ill-conditioned optimization, and arbitrary random representations. This thesis, entitled Robust and Efficient Machine Learning under Data Uncertainty: From Loss Functions to Data-Aware Representations, develops a coherent set of robust and computationally efficient learning strategies by redesigning four fundamental components of supervised learning: the loss function, the sample-weighting mechanism, the optimization procedure, and the initialization of randomized representations. The first contribution is the RoBoSS loss, a robust, bounded, sparse, and smooth classification loss that limits the influence of extreme samples while preserving useful margin behavior and optimization stability. RoBoSS is integrated into support vector machine (SVM) learning and further extended to twin support vector machine (TSVM) learning through the RoBoSS-based Twin Support Vector Machine (RoBoTS) and its inverse-free variant, demonstrating that bounded sparse loss design can improve robustness without sacrificing efficiency. The thesis then develops the Wave loss function, a bounded, smooth, and asymmetric classification loss, and embeds it into SVM and TSVM frameworks to obtain Wave-SVM and Wave-TSVM for learning under noisy and uncertain data.</description>
    <dc:date>2026-09-10T00:00:00Z</dc:date>
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    <title>Regime switching Heston log-stochastic model [RESTRICTED THESIS-01 Year]</title>
    <link>https://dspace.iiti.ac.in/handle/123456789/19151</link>
    <description>Title: Regime switching Heston log-stochastic model [RESTRICTED THESIS-01 Year]
Authors: Mittal, Khushi Anil
Abstract: [Abstract is restricted for 01 Year, due to IPR related issue]</description>
    <dc:date>2026-05-22T00:00:00Z</dc:date>
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    <title>Estimating the location parameter of the selected exponential population [RESTRICTED THESIS-01 Year]</title>
    <link>https://dspace.iiti.ac.in/handle/123456789/19142</link>
    <description>Title: Estimating the location parameter of the selected exponential population [RESTRICTED THESIS-01 Year]
Authors: Maurya, Saurabh Kumar
Abstract: [Abstract is restricted for 01 Year, due to IPR related issue]</description>
    <dc:date>2026-05-15T00:00:00Z</dc:date>
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