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Found 5 papers, 1 papers with code
Asymptotics of Language Model Alignment
The goal of language model alignment is to alter $p$ to a new distribution $\phi$ that results in a higher expected reward while keeping $\phi$ close to $p.$ A popular alignment method is the KL-constrained reinforcement learning (RL), which chooses a distribution $\phi_\Delta$ that maximizes $E_{\phi_{\Delta}} r(y)$ subject to a relative entropy constraint $KL(\phi_\Delta || p) \leq \Delta.$ Another simple alignment method is best-of-$N$, where $N$ samples are drawn from $p$ and one with highest reward is selected.
Correspondence Analysis Using Neural Networks
Correspondence analysis (CA) is a multivariate statistical tool used to visualize and interpret data dependencies.
Correspondence Analysis of Government Expenditure Patterns
We analyze expenditure patterns of discretionary funds by Brazilian congress members.
Generalizing Correspondence Analysis for Applications in Machine Learning
In this paper, we provide a novel interpretation of CA in terms of an information-theoretic quantity called the principal inertia components.
Generalizing Bottleneck Problems
Given a pair of random variables $(X, Y)\sim P_{XY}$ and two convex functions $f_1$ and $f_2$, we introduce two bottleneck functionals as the lower and upper boundaries of the two-dimensional convex set that consists of the pairs $\left(I_{f_1}(W; X), I_{f_2}(W; Y)\right)$, where $I_f$ denotes $f$-information and $W$ varies over the set of all discrete random variables satisfying the Markov condition $W \to X \to Y$.
