On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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4 results for “self-balancing”

LSB: Local Self-Balancing MCMC in Discrete Spacescs.AI

LSB is a new MCMC method for discrete spaces that reduces target evaluations.

problem Sampling in discrete domains with high efficiency and adaptability.

method Local self-balancing proposals, mutual information objective, self-balancing learning.

result LSB converges with fewer target evaluations compared to existing methods.

MCMC discrete sampling local proposals mutual information self-balancing

2021-09-08abs ↗pdf ↗

Self-Balancing Sequential Sampling: Fast Convergence with Controlled Predictabilitymath.PR

Self-balancing sampler improves sampling efficiency and unpredictability.

problem Efficient and unpredictable sampling in various applications.

method Adaptive biasing of sampling probabilities to achieve faster convergence and unpredictability.

result Self-balancing sampler converges at

O(n^{-1})

rate, outperforming IID sampling.

sequential sampling audit scheduling representative sampling treatment assignment stochastic mirror-descent

2026-07-23abs ↗pdf ↗

Self Balancing Neural Network: A Novel Method to Estimate Average Treatment Effectstat.ML

Proposes a novel neural network method to estimate average treatment effect.

problem Bias in estimating average treatment effect due to confounding and instrumental variables.

method Self-balancing neural network (Sbnet) that estimates pseudo propensity scores and average treatment effect in one step.

result Proposed method outperforms state-of-the-art methods in simulations and real-world datasets.

average treatment effect neural network propensity score observational studies confounding variables

2025-07-17abs ↗pdf ↗

Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applicationscs.LG

Federated learning (FL) is a distributed deep learning method which enables multiple participants, such as mobile phones and IoT devices, to contribute a neural network model while their private training data remains in local devices. This distributed approach is promising in the edge computing system where have a larg…

2019-07-02abs ↗pdf ↗