arXiv subjects

Neel Shah

Publications and source records attributed to Neel Shah.

9 recordsLinked to original sources

arXiv2026

Constraining dark energy with complementary probes of large-scale structure

To observationally pin down the nature of dark energy, it is essential to consistently model cosmological perturbations in the presence of dark energy alongside the background expansion and constrain this joint theory space with a large array of complementary probes. Here, we achieve this by constraining a model in the Effective Field Theory of Dark Energy (EFTofDE) framework by supplementing probes of the expansion history with several probes of large-scale structure: redshift space distortions (RSD) from DESI DR1, $3\times2$pt measurements from DES Y3, and the Integrated Sachs-Wolfe effect from cross-correlating CMB temperature anisotropies with galaxy number counts or CMB lensing. We demonstrate the complementarity of different probes which leads to strong improvements on constraints on DE perturbations. For our most constraining dataset combination that supplements CMB+BAO+SNe probes with DESI DR1 RSD, DES Y3 $3\times2$pt and ISW cross-correlations between CMB temperature and galaxy counts, we find an improvement in the Figure of Merit (FoM) for the DE perturbation parameters $\{c_B, c_M\}$ by a factor of 2.69. We show the phenomenological implications of these constraints by mapping them to the present-day values of the phenomenological functions $\{\mu(z), \Sigma(z)\}$, where we see an FoM improvement by a factor of 3.37. We find a significant interdependence between the posteriors of $\{w_0, w_a\}$ and $\{c_B, c_M\}$, caused by the theoretical prior imposed by the gradient stability condition within the EFTofDE framework. Finally, we compute the significance of deviation from $\Lambda$CM for the EFTofDE model when constrained with CMB+BAO+SNe datasets, finding it to be at 2.9$\sigma$. This significance is nontrivially similar to the significance for the $w_0w_a$CDM model for the same dataset combination which we find to be 3.1$\sigma$.

astro-ph.CO↗

arXiv2026

cloelib: A Flexible Python Library for Computing Cosmological Observables in the Euclid Era

cloelib is a Python library developed to compute cosmological observables within the Cosmology Likelihood for Observables in Euclid (CLOE) ecosystem (cloe-org). As cosmology enters a precision era driven by galaxy survey missions such as Euclid, there is a growing need for flexible, efficient, and differentiable software capable of supporting next-generation inference pipelines. cloelib addresses these demands through a modular architecture that interfaces seamlessly with established Boltzmann solvers whilst incorporating JAX-based automatic differentiation to enable gradient-based methods. The library defines consistent protocols for background evolution, perturbations, and non-linear structure formation, and supports a wide range of observables, including photometric and spectroscopic large-scale structure probes, as well as cross-correlations with the Cosmic Microwave Background and galaxy clusters. In its finalised form, cloelib is intended to serve as the reference theory computation infrastructure for Euclid's first cosmological release, bridging traditional numerical cosmology with modern optimisation techniques and emerging machine learning approaches to inference.

astro-ph.CO↗

arXiv2026

CROCS Data Release I: Constraints on the Hubble Constant

Recent cosmological surveys and datasets have highlighted a variety of tensions to the concordance model of our universe, $\Lambda$CDM. Of particular interest is the Hubble tension, the $5.5\sigma$ discrepancy between measurements of the Hubble constant $H_0$ using high redshift CMB data from Planck ($67.27\pm0.60$km$\text{s}^{-1}\text{Mpc}^{-1}$) and low redshift supernovae from SH0ES ($73.2\pm1.3$km$\text{s}^{-1}\text{Mpc}^{-1}$). To avoid stepping on any toes, we have initiated the CROCS collaboration to resolve this tension, gathering experts from across many fields of cosmology, astrophysics, astronomy, machine learning, data science, philosophy, and astrology. In this paper, we present findings from CROCS Data Release 1, corresponding to the first $\sim3$ days and 27 minutes (rest frame) of observation. We perform a robust statistical analysis, showing that Planck and SH0ES both suffer from imperial biasing systematics (IBS) at $5\sigma$ significance. Accounting for these errors by converting to metric units reconciles the high and low redshift data, with $H_0 = 69.00\pm0.420$km$\text{s}^{-1}\text{Mpc}^{-1}$. We thus report that our results are sufficient to end the Hubble tension for good.

astro-ph.CO↗

arXiv2025

Dark energy constraints in light of theoretical priors

In order to derive model-independent observational bounds on dark energy/modified gravity theories, a typical approach is to constrain parametrised models intended to capture the space of dark energy theories. Here we investigate in detail the effect that the nature of these parametrisations can have, finding significant effects on the resulting cosmological dark energy constraints. In order to observationally distinguish well-motivated and physical parametrisations from unphysical ones, it is crucial to understand the theoretical priors that physical parametrisations place on the phenomenology of dark energy. To this end we discuss a range of theoretical priors that can be imposed on general dark energy parametrisations, and their effect on the constraints on the phenomenology of dynamical dark energy. More specifically, we investigate both the phenomenological $\{\mu,\Sigma\}$ parametrisation as well as effective field theory (EFT) inspired approaches to model dark energy interactions. We compare the constraints obtained in both approaches for different phenomenological and theory-informed time-dependences for the underlying functional degrees of freedom, discuss the effects of priors derived from gravitational wave physics, and investigate the interplay between constraints on parameters constraining only the background evolution vs. parameters controlling linear perturbations.

astro-ph.CO↗

arXiv2023

Finding Paths for Explainable MOOC Recommendation: A Learner Perspective

The increasing availability of Massive Open Online Courses (MOOCs) has created a necessity for personalized course recommendation systems. These systems often combine neural networks with Knowledge Graphs (KGs) to achieve richer representations of learners and courses. While these enriched representations allow more accurate and personalized recommendations, explainability remains a significant challenge which is especially problematic for certain domains with significant impact such as education and online learning. Recently, a novel class of recommender systems that uses reinforcement learning and graph reasoning over KGs has been proposed to generate explainable recommendations in the form of paths over a KG. Despite their accuracy and interpretability on e-commerce datasets, these approaches have scarcely been applied to the educational domain and their use in practice has not been studied. In this work, we propose an explainable recommendation system for MOOCs that uses graph reasoning. To validate the practical implications of our approach, we conducted a user study examining user perceptions of our new explainable recommendations. We demonstrate the generalizability of our approach by conducting experiments on two educational datasets: COCO and Xuetang.

cs.IR↗

arXiv2023

Assisting Clinical Decisions for Scarcely Available Treatment via Disentangled Latent Representation

Extracorporeal membrane oxygenation (ECMO) is an essential life-supporting modality for COVID-19 patients who are refractory to conventional therapies. However, the proper treatment decision has been the subject of significant debate and it remains controversial about who benefits from this scarcely available and technically complex treatment option. To support clinical decisions, it is a critical need to predict the treatment need and the potential treatment and no-treatment responses. Targeting this clinical challenge, we propose Treatment Variational AutoEncoder (TVAE), a novel approach for individualized treatment analysis. TVAE is specifically designed to address the modeling challenges like ECMO with strong treatment selection bias and scarce treatment cases. TVAE conceptualizes the treatment decision as a multi-scale problem. We model a patient's potential treatment assignment and the factual and counterfactual outcomes as part of their intrinsic characteristics that can be represented by a deep latent variable model. The factual and counterfactual prediction errors are alleviated via a reconstruction regularization scheme together with semi-supervision, and the selection bias and the scarcity of treatment cases are mitigated by the disentangled and distribution-matched latent space and the label-balancing generative strategy. We evaluate TVAE on two real-world COVID-19 datasets: an international dataset collected from 1651 hospitals across 63 countries, and a institutional dataset collected from 15 hospitals. The results show that TVAE outperforms state-of-the-art treatment effect models in predicting both the propensity scores and factual outcomes on heterogeneous COVID-19 datasets. Additional experiments also show TVAE outperforms the best existing models in individual treatment effect estimation on the synthesized IHDP benchmark dataset.

cs.LG↗

arXiv2020

A Preliminary Approach for Learning Relational Policies for the Management of Critically Ill Children

The increased use of electronic health records has made possible the automated extraction of medical policies from patient records to aid in the development of clinical decision support systems. We adapted a boosted Statistical Relational Learning (SRL) framework to learn probabilistic rules from clinical hospital records for the management of physiologic parameters of children with severe cardiac or respiratory failure who were managed with extracorporeal membrane oxygenation. In this preliminary study, the results were promising. In particular, the algorithm returned logic rules for medical actions that are consistent with medical reasoning.

cs.AI↗

arXiv2018

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.

cs.AI↗

arXiv2014

A Multi-Plane Block-Coordinate Frank-Wolfe Algorithm for Training Structural SVMs with a Costly max-Oracle

Structural support vector machines (SSVMs) are amongst the best performing models for structured computer vision tasks, such as semantic image segmentation or human pose estimation. Training SSVMs, however, is computationally costly, because it requires repeated calls to a structured prediction subroutine (called \emph{max-oracle}), which has to solve an optimization problem itself, e.g. a graph cut. In this work, we introduce a new algorithm for SSVM training that is more efficient than earlier techniques when the max-oracle is computationally expensive, as it is frequently the case in computer vision tasks. The main idea is to (i) combine the recent stochastic Block-Coordinate Frank-Wolfe algorithm with efficient hyperplane caching, and (ii) use an automatic selection rule for deciding whether to call the exact max-oracle or to rely on an approximate one based on the cached hyperplanes. We show experimentally that this strategy leads to faster convergence to the optimum with respect to the number of requires oracle calls, and that this translates into faster convergence with respect to the total runtime when the max-oracle is slow compared to the other steps of the algorithm. A publicly available C++ implementation is provided at http://pub.ist.ac.at/~vnk/papers/SVM.html .

cs.LG↗