arXiv subjects

Kyle Ritscher

Publications and source records attributed to Kyle Ritscher.

3 recordsLinked to original sources

arXiv2026

FlexAct: Why Learn when you can Pick?

Learning activation functions has emerged as a promising direction in deep learning, allowing networks to adapt activation mechanisms to task-specific demands. In this work, we introduce a novel framework that employs the Gumbel-Softmax trick to enable discrete yet differentiable selection among a predefined set of activation functions during training. Our method dynamically learns the optimal activation function independently of the input, thereby enhancing both predictive accuracy and architectural flexibility. Experiments on synthetic datasets show that our model consistently selects the most suitable activation function, underscoring its effectiveness. These results connect theoretical advances with practical utility, paving the way for more adaptive and modular neural architectures in complex learning scenarios.

cs.LG↗

arXiv2025

Change Point Localization and Inference in Dynamic Multilayer Networks

We study offline change point localization and inference in dynamic multilayer random dot product graphs (D-MRDPGs), where at each time point, a multilayer network is observed with shared node latent positions and time-varying, layer-specific connectivity patterns. We propose a novel two-stage algorithm that combines seeded binary segmentation with low-rank tensor estimation, and establish its consistency in estimating both the number and locations of change points. Furthermore, we derive the limiting distributions of the refined estimators under both vanishing and non-vanishing jump regimes. To the best of our knowledge, this is the first result of its kind in the context of dynamic network data. We also develop a fully data-driven procedure for constructing confidence intervals. Extensive numerical experiments demonstrate the superior performance and practical utility of our methods compared to existing alternatives.

stat.ME↗

arXiv2023

Quantile Additive Trend Filtering

This paper investigates risk bounds for quantile additive trend filtering, a method gaining increasing significance in the realms of additive trend filtering and quantile regression. We investigate the constrained version of quantile trend filtering within additive models, considering both fixed and growing input dimensions. In the fixed dimension case, we discover an error rate that mirrors the non-quantile minimax rate for additive trend filtering, featuring the main term $n^{-2r/(2r+1)}V^{2/(2r+1)}$, when the underlying quantile function is additive, with components whose $(r-1)$th derivatives are of bounded variation by $V$. In scenarios with a growing input dimension $d$, quantile additive trend filtering introduces a polynomial factor of $d^{(2r+2)/(2r+1)}$. This aligns with the non-quantile variant, featuring a linear factor $d$, particularly pronounced for larger $r$ values. Additionally, we propose a practical algorithm for implementing quantile trend filtering within additive models, using dimension-wise backfitting. We conduct experiments with evenly spaced data points or data that samples from a uniform distribution in the interval $[0,1]$, applying distinct component functions and introducing noise from normal and heavy-tailed distributions. Our findings confirm the estimator's convergence as $n$ increases and its superiority, particularly in heavy-tailed distribution scenarios. These results deepen our understanding of additive trend filtering models in quantile settings, offering valuable insights for practical applications and future research.

math.ST↗