arXiv:2406.05014v4 Announce Type: replace Abstract: The goal of Root Cause Analysis (RCA) is to explain why an anomaly occurred by identifying where the fault originated. Several recent works model the anomalous event as resulting from a change in the causal mechanism at the root cause, i.e., as a soft intervention. RCA is then the task of identifying which causal mechanism changed. In real-world applications, one often has either few or only a single sample from the post-intervention distribution: […]

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arXiv:2510.04579v2 Announce Type: replace-cross Abstract: The Busemann function has recently found much interest in a variety of geometric machine learning problems, as it naturally defines projections onto geodesic rays of Riemannian manifolds and generalizes the notion of hyperplanes. As several sources of data can be conveniently modeled as probability distributions, it is natural to study this function in the Wasserstein space, which carries a rich formal Riemannian structure induced by Optimal Transport metrics. In this work, we investigate […]

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arXiv:2603.08925v1 Announce Type: cross Abstract: Variational inference approximates Bayesian posterior distributions by projecting onto a tractable family of distributions. While most theoretical analyses evaluate the quality of this approximation using global divergence measures, many applications rely on specific posterior summaries such as expectations, variances, or tail probabilities. We develop a geometric framework for analyzing the bias of posterior functionals under variational approximations. We show that the leading-order bias of a posterior functional is determined by its component orthogonal […]

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arXiv:2601.20028v1 Announce Type: new Abstract: The Linear Representation Hypothesis asserts that the embeddings learned by neural networks can be understood as linear combinations of features corresponding to high-level concepts. Based on this ansatz, sparse autoencoders (SAEs) have recently become a popular method for decomposing embeddings into a sparse combination of linear directions, which have been shown empirically to often correspond to human-interpretable semantics. However, recent attempts to apply SAEs to multimodal embedding spaces (such as the popular CLIP […]

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arXiv:2505.04007v2 Announce Type: replace Abstract: This paper provides a formulation of the log-homotopy particle flow from the perspective of variational inference. We show that the transient density used to derive the particle flow follows a time-scaled trajectory of the Fisher-Rao gradient flow in the space of probability densities. The Fisher-Rao gradient flow is obtained as a continuous-time algorithm for variational inference, minimizing the Kullback-Leibler divergence between a variational density and the true posterior density. When considering a parametric […]

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Although it is not always immediately obvious to higher-ups, employee disengagement can quickly have a huge impact on a wider team within an organisation if it is not addressed. Even in large companies where employees often have a higher tenure than in smaller businesses due to the stability and growth prospects, the large scale of the workforce means that small issues can blow up into significant problems. Once disengagement starts to spread, company culture and productivity can take […]

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TL;DR: Current schedulers in PyTorch are limited to just learning rate (lr) changes and often lead to hardcoded, error-prone logic in training loops for anything more complex. I built a flexible suite for scheduling any optimizer hyperparam (LR, momentum, betas, etc.), with support for custom functions, presets, cyclic patterns, and per-group overrides. It’s stateless where possible, picklable for checkpointing, and well-tested. It currently lives in my research monorepo, but I can separate it into a standalone package if […]

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arXiv:2601.20046v1 Announce Type: new Abstract: Metastatic castration-resistant prostate cancer (mCRPC) is a highly aggressive disease with poor prognosis and heterogeneous treatment response. In this work, we developed and externally validated a visit-level 180-day mortality risk model using longitudinal data from two Phase III cohorts (n=526 and n=640). Only visits with observable 180-day outcomes were labeled; right-censored cases were excluded from analysis. We compared five candidate architectures: Long Short-Term Memory, Gated Recurrent Unit (GRU), Cox Proportional Hazards, Random Survival […]

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