January 2026

When daily urgencies and immediate results take priority over long-term goals, when quick fixes are the default mode of execution instead of big-picture thinking or when you spend most of your time firefighting issues—putting out fires, responding to crises and reacting to challenges that surface unexpectedly—it may feel like you’re accomplishing a lot. Problems are being solved, challenges are being resolved and things are getting done. You’re highly engaged, super involved and extremely busy in trying to keep […]

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As a senior cloud engineer who has architected solutions across multiple cloud platforms, I’ve learned that choosing the right load balancer can make or break your application’s performance, scalability, and cost efficiency. This guide dives deep into the load balancing offerings from AWS, GCP, and Azure, explaining how they work internally and when to use each type. Understanding Load Balancer Fundamentals Before we dive into specific offerings, let’s understand what happens inside a load balancer. At its core, […]

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arXiv:2601.06730v2 Announce Type: replace-cross Abstract: The Rashomon effect — the existence of multiple, distinct models that achieve nearly equivalent predictive performance — has emerged as a fundamental phenomenon in modern machine learning and statistics. In this paper, we explore the causes underlying the Rashomon effect, organizing them into three categories: statistical sources arising from finite samples and noise in the data-generating process; structural sources arising from non-convexity of optimization objectives and unobserved variables that create fundamental non-identifiability; and […]

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arXiv:2512.17340v2 Announce Type: replace-cross Abstract: Fair regression methods have the potential to mitigate societal bias concerns in health care, but there has been little work on penalized fair regression when multiple groups experience such bias. We propose a general regression framework that addresses this gap with unfairness penalties for multiple groups. Our approach is demonstrated for binary outcomes with true positive rate disparity penalties. It can be efficiently implemented through reduction to a cost-sensitive classification problem. We additionally […]

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arXiv:2512.04299v2 Announce Type: replace-cross Abstract: Spectral gradient methods, such as the recently popularized Muon optimizer, are a promising alternative to standard Euclidean gradient descent for training deep neural networks and transformers, but it is still unclear in which regimes they are expected to perform better. We propose a simple layerwise condition that predicts when a spectral update yields a larger decrease in the loss than a Euclidean gradient step. This condition compares, for each parameter block, the squared […]

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arXiv:2510.09676v2 Announce Type: replace-cross Abstract: Inverse problems, where the goal is to recover an unknown signal from noisy or incomplete measurements, are central to applications in medical imaging, remote sensing, and computational biology. Diffusion models have recently emerged as powerful priors for solving such problems. However, existing methods either rely on projection-based techniques that enforce measurement consistency through heuristic updates, or they approximate the likelihood $p(boldsymbol{y} mid boldsymbol{x})$, often resulting in artifacts and instability under complex or high-noise […]

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arXiv:2507.15112v4 Announce Type: replace-cross Abstract: Machine learning systems increasingly face requirements to remove entire domains of information–such as toxic language or biases–rather than individual user data. This task presents a dilemma: full removal of the unwanted domain data is computationally expensive, while random partial removal is statistically inefficient. We find that a domain’s statistical influence is often concentrated in a small subset of its data samples, suggesting a path between ineffective partial removal and unnecessary complete removal. We […]

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arXiv:2506.17139v3 Announce Type: replace-cross Abstract: In recent years, diffusion models trained on equilibrium molecular distributions have proven effective for sampling biomolecules. Beyond direct sampling, the score of such a model can also be used to derive the forces that act on molecular systems. However, while classical diffusion sampling usually recovers the training distribution, the corresponding energy-based interpretation of the learned score is often inconsistent with this distribution, even for low-dimensional toy systems. We trace this inconsistency to inaccuracies […]

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arXiv:2505.01078v3 Announce Type: replace-cross Abstract: Backward stochastic differential equation (BSDE)-based deep learning methods provide an alternative to Physics-Informed Neural Networks (PINNs) for solving high-dimensional partial differential equations (PDEs), offering potential algorithmic advantages in settings such as stochastic optimal control, where the PDEs of interest are tied to an underlying dynamical system. However, standard BSDE-based solvers have empirically been shown to underperform relative to PINNs in the literature. In this paper, we identify the root cause of this performance […]

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arXiv:2505.00356v3 Announce Type: replace-cross Abstract: In an era of increasing computational capabilities and growing environmental consciousness, organizations face a critical challenge in balancing the accuracy of forecasting models with computational efficiency and sustainability. Global forecasting models, lowering the computational time, have gained significant attention over the years. However, the common practice of retraining these models with new observations raises important questions about the costs of forecasting. Using ten different machine learning and deep learning models, we analyzed various […]

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