Low-Rank Adaptation (LoRA) is the prevailing approach for efficient large language model (LLM) fine-tuning. Building on this paradigm, recent studies have proposed alternative initialization strategies and architectural modifications, reporting substantial improvements over vanilla LoRA. However, these gains are often demonstrated under fixed or narrowly tuned hyperparameter settings, despite the known sensitivity of neural networks to training configurations. In this work, we systematically re-evaluate four representative LoRA variants alongside vanilla LoRA through extensive hyperparameter searches. Across mathematical and code […]

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arXiv:2603.25224v1 Announce Type: new Abstract: Demographic parity (DP) is a widely used group fairness criterion requiring predictive distributions to be invariant across sensitive groups. While natural in classification, full distributional DP is often overly restrictive in regression and can lead to substantial accuracy loss. We propose a relaxation of DP tailored to regression, enforcing parity only at a finite set of quantile levels and/or score thresholds. Concretely, we introduce a novel (${ell}$, Z)-fair predictor, which imposes groupwise CDF […]

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I used to treat Test-Driven Development (TDD) as busywork. Write a failing test. Watch it fail. Write code to pass it. Rinse and repeat. It always felt like it slowed me down. Then AI changed everything. Tools like GitHub Copilot can now generate implementation and comprehensive tests in seconds. Suddenly, the mechanical ritual of TDD seemed… optional. But the more I experimented, the more I realized something important: TDD’s real value was never the mechanics—it was the thinking […]

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arXiv:2604.04588v1 Announce Type: new Abstract: Pairwise comparisons are widely used in decision analysis, preference modeling, and evaluation problems. In many practical situations, the observed comparison matrix is not reciprocal. This lack of reciprocity is often treated as a defect to be corrected immediately. In this article, we adopt a different point of view: part of the nonreciprocity may reflect a genuine variation in the evaluation scale, while another part is due to random perturbations. We introduce an additive […]

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arXiv:2401.09346v2 Announce Type: replace Abstract: Uncertainty quantification for estimation through stochastic optimization solutions in an online setting has gained popularity recently. This paper introduces a novel inference method focused on constructing confidence intervals with efficient computation and fast convergence to the nominal level. Specifically, we propose to use a small number of independent multi-runs to acquire distribution information and construct a t-based confidence interval. Our method requires minimal additional computation and memory beyond the standard updating of estimates, […]

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arXiv:2602.10125v1 Announce Type: new Abstract: Network segmentation is a popular security practice for limiting lateral movement, yet practitioners lack a metric to measure how segmented a network actually is. We model a network as a graph and study segmentedness as a property captured by the global edge density that can be estimated from sampled node pairs. Then, we derive an estimator and evaluate its uncertainty using confidence intervals. For a 95% confidence interval with a margin-of-error of $pm […]

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arXiv:2603.24545v1 Announce Type: cross Abstract: We study the problem of detecting local geometry in random graphs. We introduce a model $mathcal{G}(n, p, d, k)$, where a hidden community of average size $k$ has edges drawn as a random geometric graph on $mathbb{S}^{d-1}$, while all remaining edges follow the ErdH{o}s–R’enyi model $mathcal{G}(n, p)$. The random geometric graph is generated by thresholding inner products of latent vectors on $mathbb{S}^{d-1}$, with each edge having marginal probability equal to $p$. This implies […]

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arXiv:2602.02791v1 Announce Type: new Abstract: We study a supervised multiclass classification problem for diffusion processes, where each class is characterized by a distinct drift function and trajectories are observed at discrete times. Extending the one-dimensional multiclass framework of Denis et al. (2024) to multidimensional diffusions, we propose a neural network-based plug-in classifier that estimates the drift functions for each class from independent sample paths and assigns labels based on a Bayes-type decision rule. Under standard regularity assumptions, we […]

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arXiv:2509.19929v3 Announce Type: replace Abstract: Uncertainty Quantification (UQ) is paramount for inference in engineering. A common inference task is to recover full-field information of physical systems from a small number of noisy observations, a usually highly ill-posed problem. Sharing information from multiple distinct yet related physical systems can alleviate this ill-posedness. Critically, engineering systems often have complicated variable geometries prohibiting the use of standard multi-system Bayesian UQ. In this work, we introduce Geometric Autoencoders for Bayesian Inversion (GABI), […]

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I just went through some pain (again) when I wanted to install some of Python’s scientific libraries on my second Mac. I summarized the setup and installation process for future reference. If you encounter any different or additional obstacles let me know, and please feel free to make any suggestions to improve this short walkthrough.

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