Information Theory
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Entropy Maps: When Hashing Meets Information Theory
A conceptual introduction to entropy maps, implementing functions with hash functions and prefix-free codes.
Information Theory, Inference, and Learning Algorithms
From Mathematical Horror to Practical Horror: The Mocking Void and Echoes of the Sublime
How The Mocking Void's arguments about computational impossibility connect to Echoes of the Sublime's practical horror of exceeding cognitive bandwidth.
What I Learned by Analyzing My Own Research as Data
I asked an AI to analyze 140+ repos and 50+ papers as a dataset. The unifying thesis it found: compositional abstractions for computing under ignorance.
Bernoulli Types: A Foundation for Approximate and Oblivious Computing
A unified type-theoretic foundation for probabilistic data structures, approximate computing, and oblivious computation with information-theoretic privacy guarantees.
Encrypted Search and Oblivious Types
Oblivious types give encrypted search information-theoretic privacy against access pattern leakage. No ORAM, no computational hardness assumptions. Here's how.
Maximizing Confidentiality in Encrypted Search Through Entropy Optimization
All Induction Is the Same Induction
Solomonoff induction, MDL, speed priors, and neural networks are all special cases of one Bayesian framework with four knobs.
The Beautiful Deception: How 256 Bits Pretend to be Infinity
Cryptographic theory assumes random oracles with infinite output. We have 256 bits. This paper explores how we bridge that gap, and what it means that we can.
Entropy Maps
Entropy maps use prefix-free hash codes to approximate functions without storing the domain, achieving information-theoretic space bounds with controllable error.
Perfect Hashing: Space Bounds, Entropy, and Cryptographic Security
Space bounds, entropy requirements, and cryptographic security properties of perfect hash functions.
Packed Containers: Zero-Waste Bit-Level Storage in C++
What if containers wasted zero bits? A C++ library for packing arbitrary value types at the bit level using pluggable codecs.
Introduction to Sequential Prediction
The problem of predicting what comes next, from compression to language models