Posts by date. See them by tags here.
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Sep 16, 2026 || Sep 16, 2026
·math
I began my day today with this interesting question about how large factorials are in terms of digits. Which brought back another interesting question I had to answer many years ago: if you represent a number \(n\) in both decimal and binary, how much more space is writing the binary...
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Feb 3, 2026 || Feb 3, 2026
·ai-ml
When we set out to learn a function or some property of it (like its maximum), we hope it is differentiable, because that means we have at our disposal a host of well-studied, and often fast, techniques. But sometimes we are not so lucky - and then there are broadly...
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Mar 6, 2025 || Mar 6, 2025
·ai-ml
·llm
I attended NeurIPS’24 virtually, and I was happy to see that they had two tutorials on topics that I care about. One was on evaluating LLMs, and the other one was on decoding-time strategies. This post covers the former. I have been meaning to publish this for a while, but...
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Sep 25, 2024 || Sep 25, 2024
·ai-ml
I totally stole the title from a paper (Attenberg & Provost, 2011). In theory, Active Learning (AL) is a tremendous idea. You need labeled data, but your kind of labeling comes at a cost, e.g., you need to obtain them from a domain expert. Now, lets say, your goal is...
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Sep 15, 2024 || Sep 16, 2024
·math
Jensen’s inequality finds widespread application in mathematical proofs. I am fond of a particular intuitive explanation of it, which doesn’t seem to be very popular. I will try to present it in brief here. I am not sure when this argument originated, but Google does turn up a paper (Needham,...
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Nov 18, 2023 || Jan 15, 2024
·ai-ml
This post continues our discussion on BayesOpt. This is part-2 of a two-part series. Now we take a look at the other pillar BayesOpt rests on: acquisition functions. My goal is to provide a flavor by looking at a few of them. I’ll go into depth for a couple; this...
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Nov 18, 2023 || Apr 20, 2024
·ai-ml
The real reason I like Bayesian Optimization: lots of pretty pictures! If I wanted to sell you on the idea of Bayesian Optimization (BayesOpt), I’d just list some of its applications: Hyperparameter Optimization (HPO) (Turner et al., 2021). Neural Architecture Search (NAS) (White et al., 2021). Prompt Optimization for LLMs...
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Apr 22, 2022 || Nov 16, 2023
·ai-ml
Generative Models have been all the rage in AI lately, be it image generators like Stable Diffusion or text generators like ChatGPT. These are examples of fairly sophisticated generative systems. But whittled down to basics, they are a means to: (a) concisely represent patterns in data, in a way that...
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Apr 26, 2017 || Nov 16, 2023
·other
Moving to a new place can be hectic and tiresome. I am moving my blog, from here, and it’s none of those.1 /s I tend towards writing technical posts when I tend towards writing at all these days, and blogger doesn’t give me the presentation options I need. So, for...