In this tutorial, we build a complete, production-grade synthetic data pipeline using CTGAN and the SDV ecosystem. We start from raw mixed-type tabular data and progressively move toward constrained generation, conditional sampling, statistical validation, and downstream utility testing. Rather than stopping at sample generation, we focus on understanding how well synthetic data preserves structure, distributions, and predictive signal. This tutorial demonstrates how CTGAN can be used responsibly and rigorously in real-world data science workflows. Copy CodeCopiedUse a different […]

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arXiv:2405.16594v4 Announce Type: replace Abstract: Conformal prediction methodology has recently been extended to the covariate shift setting, where the distribution of covariates differs between training and test data. While existing results ensure that the prediction sets from these methods achieve marginal coverage above a nominal level, their coverage rate conditional on the training dataset (referred to as training-conditional coverage) remains unexplored. In this paper, we address this gap by deriving upper bounds on the tail of the training-conditional […]

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It’s been a long time sine I’ve written a blog post, and I miss it a lot! Today, I’ll be talking about an experiment I did 2 months ago when I was trying to optimize Blazor.Diagrams: JS Interop Batching. Context I’ve been working on my Diagramming Library for Blazor for quite some time now, and it basically takes a model (Diagram) that contains multiple nodes, ports and links (which are also models), renders it and makes it editable […]

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Setting the learning rate for a deep learning model is a critical part of successful training, yet choosing this hyperparameter is often done empirically with trial and error. In this work, we explore a solvable model of optimal learning rate schedules for a powerlaw random feature model trained with stochastic gradient descent (SGD). We consider the optimal schedule $η_T^star(t)$ where $t$ is the current iterate and $T$ is the total training horizon. This schedule is computed both numerically […]

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arXiv:2601.00120v1 Announce Type: new Abstract: A distributed storage system stores data across multiple nodes, with the primary objective of enabling efficient data recovery even in the event of node failures. The main goal of an exact repair scheme is to recover the data from a failed node by accessing and downloading information from the rest of the nodes. In a groundbreaking paper, ~cite{GW} developed an exact repair scheme for a distributed storage system that is based on Reed-Solomon […]

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Over-the-air (OTA) updates in multi-tenant systems often face task conflicts, cache overlap, and weak fault recovery during parallel updates. This study designed a layered fault-tolerance and isolation method that combines task redundancy, cache separation, and snapshot rollback. Tests were carried out on 120 devices across six tenants with a fault rate of up to 95%. The system kept stable operation, extended the mean time between failures (MTBF) to 182 hours, and raised total availability from 98.2% to 99.7%. […]

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India’s AQI crisis sees no signs of abating. All across northern India, the winter season sees the AQI stay in the very hazardous category for months together across the big cities, small towns, and even villages of North India. AQI readings above 400 are routine, and one has come to regard a 150 AQI, which constitutes the poor category, as pretty much normal and par for the course. The fact that the high AQI levels have started getting […]

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