arXiv research

The study examines neural networks with random weights and biases, finding that depth-to-width ratio controls fluctuations and correlations.

Study uses reinforcement learning to optimize portfolios under recursive utility.

Proposes a method to partition univariate data into unimodal subsets.

Symmetry in neural networks affects generalization, as shown by CLT and RG transformations.

Constructs classifiers for neural networks with specific data configurations.

Recursive experts improve dynamic sequential learning systems.

We introduce Recurrent Predictive State Policy (RPSP) networks, a recurrent architecture that brings insights from predictive state representations to reinforcement learning in partially observable environments. Predictive state policy networks consist of a recursive filter, which keeps track of a belief about the stat…

We determine the variance-optimal hedge when the logarithm of the underlying price follows a process with stationary independent increments in discrete or continuous time. Although the general solution to this problem is known as backward recursion or backward stochastic differential equation, we show that for this cla…

New algorithm reduces regret in linear mixture SSPs without cost bounds.

Paper addresses global convergence of MLR estimation under weak data conditions.

Controller-Augmented Hidden Markov Models (CHMMs) are a framework for constrained sequential inference.

New algorithm identifies causal effects in latent confounding models.

Overview of risk-sensitive Markov decision processes with Optimized Certainty Equivalent.

Derives a general derivative identity for conditional mean in Gaussian noise.

This paper studies the bail-out optimal dividend problem with regime switching under the constraint that the cumulative dividend strategy is absolutely continuous. We confirm the optimality of the regime-modulated refraction-reflection strategy when the underlying risk model follows a general spectrally negative Markov…

Recursive neural networks have widely been used by researchers to handle applications with recursively or hierarchically structured data. However, embedded control flow deep learning frameworks such as TensorFlow, Theano, Caffe2, and MXNet fail to efficiently represent and execute such neural networks, due to lack of s…

Evaluating AI investment strategies

Paper defines Farey Recursive Functions and explores their properties.

The paper explores generalizations of Mirzakhani's recursion and computes volumes for physical gravity models.

Study quantifies how LLMs capture higher-order statistical structure using cumulant expansion.

The CSA-ES is an Evolution Strategy with Cumulative Step size Adaptation, where the step size is adapted measuring the length of a so-called cumulative path. The cumulative path is a combination of the previous steps realized by the algorithm, where the importance of each step decreases with time. This article studies …

Tab-TRM uses recursive model for insurance pricing on tabular data.

Using methods introduced by Scargle in 1978 we derive a cumulative version of the Lomb periodogram that exhibits frequency independent statistics when applied to cumulative noise. We show how this cumulative Lomb periodogram allows us to estimate the significance of log-periodic signatures in the S&P 500 anti-bubble th…

The rough Heston model emerges from scaling bivariate INAR processes, linking microstructure to option pricing.

New recursion formula for non-orientable surfaces resolves divergences.

Harer and Zagier proved a recursion to enumerate gluings of a $2d$-gon that result in an orientable genus $g$ surface, in their work on Euler characteristics of moduli spaces of curves. Analogous results have been discovered for other enumerative problems, so it is natural to pose the following question: how large is t…

This paper studies recursive ensembles driven by Fibonacci updates, improving learning dynamics.

The article improves prediction by aggregating Kalman recursions online.

Solves a recursion for Gromov-Witten invariants of the unknot.

New recursion found for hyperbolic sphere volumes.

This work generalizes a formula linking Seiberg-Witten prepotential and topological recursion.

Efficient online kernel CUSUM detects changes quickly and accurately.

Kernelized cumulants improve statistical analysis in high-dimensional spaces.

Unified analysis of finite weight averaging methods in deep learning.

LASER compresses recursive model activations by exploiting their low-dimensional structure.

We describe a method for recursively calculating Gromov-Witten invariants of all blowups of the projective plane. This recursive formula is different from the recursive formulas due to Göttsche and Pandharipande in the zero genus case, and Caporaso and Harris in the case of no blowups. We use tropical curves and a recu…

Neural networks can learn from higher-order cumulants efficiently, requiring quadratic samples.

Topological recursion recovers a specific partition function for colored knots.

We derive the Do and Norbury recursion formula for the one-loop mean of an irregular spectral curve from a variant of replica method by Brezín and Hikami. We express this recursion in special times in which all terms $W_1^{(g)}$ of the genus expansion of the one-loop mean are polynomials. We find a generalization of th…

This paper concerns the recursive utility maximization problem under partial information. We first transform our problem under partial information into the one under full information. When the generator of the recursive utility is concave, we adopt the variational formulation of the recursive utility which leads to a s…

Private estimation of many quantiles using differential privacy.

The paper calculates bounds for risk metrics and entropies under partial information constraints.

A new GAN loss function based on cumulant generating functions improves stability and robustness.

We derive a recursion relation for hyperbolic string vertices and apply it to string field theory.

In this paper, we investigate cost-aware joint learning and optimization for multi-channel opportunistic spectrum access in a cognitive radio system. We investigate a discrete time model where the time axis is partitioned into frames. Each frame consists of a sensing phase, followed by a transmission phase. During the …

Benchmarking recursive collapse claims with a new framework under false-positive control.

SRRM improves recursive transport surrogates in the small-discrepancy regime.

ERM uses energy-based selection to improve recursive reasoning.