January 2026

Supervised machine learning frameworks rely on extensive labeled datasets for robust performance on real-world tasks. However, there is a lack of large annotated datasets in audio and music domains, as annotating such recordings is resource-intensive, laborious, and often require expert domain knowledge. In this work, we explore the use of label propagation (LP), a graph-based semi-supervised learning technique, for automatically labeling the unlabeled set in an unsupervised manner. By constructing a similarity graph over audio embeddings, we propagate […]

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In the dynamic world of software engineering, where technical mastery meets strategic business impact, exceptional professionals distinguish themselves through transformative solutions while nurturing engineering talent. Rishabh Agarwal’s eight-year journey across retail, financial services, consulting, and entertainment sectors exemplifies how curiosity-driven learning, combined with relentless performance optimization, creates compounding organizational value. The Foundation: From Hardware Fascination to Software Innovation Rishabh’s engineering journey began with an unexpected pivot. Initially drawn to computer engineering and fascinated by physical system architecture, he […]

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Smartphone sensing offers an unobtrusive and scalable way to track daily behaviors linked to mental health, capturing changes in sleep, mobility, and phone use that often precede symptoms of stress, anxiety, or depression. While most prior studies focus on detection that responds to existing conditions, forecasting mental health enables proactive support through Just-in-Time Adaptive Interventions. In this paper, we present the first comprehensive benchmarking study comparing traditional machine learning (ML), deep learning (DL), and large language model (LLM) […]

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“Social reputation” keeps turning up on every timeline with the confidence of a solved problem. It’s pitched as the missing layer. The thing that will finally let DeFi graduate from over-collateralized lending into something resembling real credit. The thing that will make identity usable without surrendering to surveillance. The thing that will push the ecosystem toward maturity—clean it up, make it legible, make it safer. I understand the itch. DeFi has spent years pretending that credit is an […]

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arXiv:2512.08179v2 Announce Type: replace-cross Abstract: We study estimation of the conditional law $P(Y|X=x)$ and continuous functionals $Psi(P(Y|X=x))$ when $Y$ takes values in a locally compact Polish space, $X in mathbb{R}^p$, and the observations arise from a complex survey design. We propose a survey-calibrated distributional random forest (SDRF) that incorporates complex-design features via a pseudo-population bootstrap, PSU-level honesty, and a Maximum Mean Discrepancy (MMD) split criterion computed from kernel mean embeddings of H'{a}jek-type (design-weighted) node distributions. We provide a […]

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arXiv:2510.10089v3 Announce Type: replace-cross Abstract: While looped transformers (termed as Looped-Attn) often outperform standard transformers (termed as Single-Attn) on complex reasoning tasks, the mechanism for this advantage remains underexplored. In this paper, we explain this phenomenon through the lens of loss landscape geometry, inspired by empirical observations of their distinct dynamics at both sample and Hessian levels. To formalize this, we extend the River-Valley landscape model by distinguishing between U-shaped valleys (flat) and V-shaped valleys (steep). Based on […]

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arXiv:2502.18428v2 Announce Type: replace-cross Abstract: We study the asymptotic spectral distribution of the conjugate kernel random matrix $YY^top$, where $Y= f(WX)$ arises from a two-layer neural network model. We consider the setting where $W$ and $X$ are random rectangular matrices with i.i.d. entries, where the entries of $W$ follow a heavy-tailed distribution, while those of $X$ have light tails. Our assumptions on $W$ include a broad class of heavy-tailed distributions, such as symmetric $alpha$-stable laws with $alpha in […]

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arXiv:2410.13341v3 Announce Type: replace-cross Abstract: High quality annotations are increasingly a bottleneck in the explosively growing machine learning ecosystem. Scalable evaluation methods that avoid costly annotation have therefore become an important research ambition. Many hope to use strong existing models in lieu of costly labels to provide cheap model evaluations. Unfortunately, this method of using models as judges introduces biases, such as self-preferencing, that can distort model comparisons. An emerging family of debiasing tools promises to fix these […]

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arXiv:2409.20412v2 Announce Type: replace-cross Abstract: Understanding the dose-response relation between a continuous treatment and the outcome for an individual can greatly drive decision-making, particularly in areas like personalized drug dosing and personalized healthcare interventions. Point estimates are often insufficient in these high-risk environments, highlighting the need for uncertainty quantification to support informed decisions. Conformal prediction, a distribution-free and model-agnostic method for uncertainty quantification, has seen limited application in continuous treatments or dose-response models. To address this gap, we […]

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