pwc-archive/papers-with-abstracts huggingface dataset:
In almost all shallow analytic neural network optimization landscapes, efficient minimizers have strongly convex neighborhoods
Authors: Felix Benning, Steffen Dereich
Date: 2025-04-11 00:00:00
Tasks: All, regression

Whether or not a local minimum of a cost function has a strongly convex neighborhood greatly influences the asymptotic convergence rate of optimizers. In this article, we rigorously analyze the prevalence of this property for the mean squared error induced by shallow, 1-hidden layer neural networks with analytic activation functions when applied to regression problems. The parameter space is divided into two domains: the 'efficient domain' (all parameters for which the respective realization function cannot be generated by a network having a smaller number of neurons) and the 'redundant domain' (the remaining parameters). In almost all regression problems on the efficient domain the optimization landscape only features local minima that are strongly convex. Formally, we will show that for certain randomly picked regression problems the optimization landscape is almost surely a Morse function on the efficient domain. The redundant domain has significantly smaller dimension than the efficient domain and on this domain, potential local minima are never isolated.
Inferring Outcome Means of Exponential Family Distributions Estimated by Deep Neural Networks
Authors: Xuran Meng, Yi Li
Date: 2025-04-12 00:00:00
Tasks: regression

While deep neural networks (DNNs) are widely used for prediction, inference on DNN-estimated subject-specific means for categorical or exponential family outcomes remains underexplored. We address this by proposing a DNN estimator under generalized nonparametric regression models (GNRMs) and developing a rigorous inference framework. Unlike existing approaches that assume independence between prediction errors and inputs to establish the error bound, a condition often violated in GNRMs, we allow for dependence and our theoretical analysis demonstrates the feasibility of drawing inference under GNRMs. To implement inference, we consider an Ensemble Subsampling Method (ESM) that leverages U-statistics and the Hoeffding decomposition to construct reliable confidence intervals for DNN estimates. We show that, under GNRM settings, ESM enables model-free variance estimation and accounts for heterogeneity among individuals in the population. Through simulations under nonparametric logistic, Poisson, and binomial regression models, we demonstrate the effectiveness and efficiency of our method. We further apply the method to the electronic Intensive Care Unit (eICU) dataset, a large-scale collection of anonymized health records from ICU patients, to predict ICU readmission risk and offer patient-centric insights for clinical decision-making.
On relative universality, regression operator, and conditional independence
Authors: Bing Li, Ben Jones, Andreas Artemiou
Date: 2025-04-15 00:00:00
Tasks: Causal Inference, Dimensionality Reduction, regression, valid

The notion of relative universality with respect to a {\sigma}-field was introduced to establish the unbiasedness and Fisher consistency of an estimator in nonlinear sufficient dimension reduction. However, there is a gap in the proof of this result in the existing literature. The existing definition of relative universality seems to be too strong for the proof to be valid. In this note we modify the definition of relative universality using the concept of \k{o}-measurability, and rigorously establish the mentioned unbiasedness and Fisher consistency. The significance of this result is beyond its original context of sufficient dimension reduction, because relative universality allows us to use the regression operator to fully characterize conditional independence, a crucially important statistical relation that sits at the core of many areas and methodologies in statistics and machine learning, such as dimension reduction, graphical models, probability embedding, causal inference, and Bayesian estimation.
On the Contractivity of Stochastic Interpolation Flow
Authors: Max Daniels
Date: 2025-04-14 00:00:00

We investigate stochastic interpolation, a recently introduced framework for high dimensional sampling which bears many similarities to diffusion modeling. Stochastic interpolation generates a data sample by first randomly initializing a particle drawn from a simple base distribution, then simulating deterministic or stochastic dynamics such that in finite time the particle's distribution converges to the target. We show that for a Gaussian base distribution and a strongly log-concave target distribution, the stochastic interpolation flow map is Lipschitz with a sharp constant which matches that of Caffarelli's theorem for optimal transport maps. We are further able to construct Lipschitz transport maps between non-Gaussian distributions, generalizing some recent constructions in the literature on transport methods for establishing functional inequalities. We discuss the practical implications of our theorem for the sampling and estimation problems required by stochastic interpolation.
Kullback-Leibler excess risk bounds for exponential weighted aggregation in Generalized linear models
Authors: The Tien Mai
Date: 2025-04-14 00:00:00
Tasks: Density Estimation

Aggregation methods have emerged as a powerful and flexible framework in statistical learning, providing unified solutions across diverse problems such as regression, classification, and density estimation. In the context of generalized linear models (GLMs), where responses follow exponential family distributions, aggregation offers an attractive alternative to classical parametric modeling. This paper investigates the problem of sparse aggregation in GLMs, aiming to approximate the true parameter vector by a sparse linear combination of predictors. We prove that an exponential weighted aggregation scheme yields a sharp oracle inequality for the Kullback-Leibler risk with leading constant equal to one, while also attaining the minimax-optimal rate of aggregation. These results are further enhanced by establishing high-probability bounds on the excess risk.
Offline Dynamic Inventory and Pricing Strategy: Addressing Censored and Dependent Demand
Authors: Korel Gundem, Zhengling Qi
Date: 2025-04-14 00:00:00
Tasks: Sequential Decision Making, Survival Analysis

In this paper, we study the offline sequential feature-based pricing and inventory control problem where the current demand depends on the past demand levels and any demand exceeding the available inventory is lost. Our goal is to leverage the offline dataset, consisting of past prices, ordering quantities, inventory levels, covariates, and censored sales levels, to estimate the optimal pricing and inventory control policy that maximizes long-term profit. While the underlying dynamic without censoring can be modeled by Markov decision process (MDP), the primary obstacle arises from the observed process where demand censoring is present, resulting in missing profit information, the failure of the Markov property, and a non-stationary optimal policy. To overcome these challenges, we first approximate the optimal policy by solving a high-order MDP characterized by the number of consecutive censoring instances, which ultimately boils down to solving a specialized Bellman equation tailored for this problem. Inspired by offline reinforcement learning and survival analysis, we propose two novel data-driven algorithms to solving these Bellman equations and, thus, estimate the optimal policy. Furthermore, we establish finite sample regret bounds to validate the effectiveness of these algorithms. Finally, we conduct numerical experiments to demonstrate the efficacy of our algorithms in estimating the optimal policy. To the best of our knowledge, this is the first data-driven approach to learning optimal pricing and inventory control policies in a sequential decision-making environment characterized by censored and dependent demand. The implementations of the proposed algorithms are available at https://github.com/gundemkorel/Inventory_Pricing_Control
Optimal sparse phase retrieval via a quasi-Bayesian approach
Authors: The Tien Mai
Date: 2025-04-13 00:00:00
Tasks: Retrieval

This paper addresses the problem of sparse phase retrieval, a fundamental inverse problem in applied mathematics, physics, and engineering, where a signal need to be reconstructed using only the magnitude of its transformation while phase information remains inaccessible. Leveraging the inherent sparsity of many real-world signals, we introduce a novel sparse quasi-Bayesian approach and provide the first theoretical guarantees for such an approach. Specifically, we employ a scaled Student distribution as a continuous shrinkage prior to enforce sparsity and analyze the method using the PAC-Bayesian inequality framework. Our results establish that the proposed Bayesian estimator achieves minimax-optimal convergence rates under sub-exponential noise, matching those of state-of-the-art frequentist methods. To ensure computational feasibility, we develop an efficient Langevin Monte Carlo sampling algorithm. Through numerical experiments, we demonstrate that our method performs comparably to existing frequentist techniques, highlighting its potential as a principled alternative for sparse phase retrieval in noisy settings.
Perfect Clustering in Nonuniform Hypergraphs
Authors: Ga-Ming Angus Chan, Zachary Lubberts
Date: 2025-04-11 00:00:00
Tasks: Clustering

While there has been tremendous activity in the area of statistical network inference on graphs, hypergraphs have not enjoyed the same attention, on account of their relative complexity and the lack of tractable statistical models. We introduce a hyper-edge-centric model for analyzing hypergraphs, called the interaction hypergraph, which models natural sampling methods for hypergraphs in neuroscience and communication networks, and accommodates interactions involving different numbers of entities. We define latent embeddings for the interactions in such a network, and analyze their estimators. In particular, we show that a spectral estimate of the interaction latent positions can achieve perfect clustering once enough interactions are observed.
Cryo-em images are intrinsically low dimensional
Authors: Luke Evans, Octavian-Vlad Murad, Lars Dingeldein, Pilar Cossio, Roberto Covino, Marina Meila
Date: 2025-04-15 00:00:00

Simulation-based inference provides a powerful framework for cryo-electron microscopy, employing neural networks in methods like CryoSBI to infer biomolecular conformations via learned latent representations. This latent space represents a rich opportunity, encoding valuable information about the physical system and the inference process. Harnessing this potential hinges on understanding the underlying geometric structure of these representations. We investigate this structure by applying manifold learning techniques to CryoSBI representations of hemagglutinin (simulated and experimental). We reveal that these high-dimensional data inherently populate low-dimensional, smooth manifolds, with simulated data effectively covering the experimental counterpart. By characterizing the manifold's geometry using Diffusion Maps and identifying its principal axes of variation via coordinate interpretation methods, we establish a direct link between the latent structure and key physical parameters. Discovering this intrinsic low-dimensionality and interpretable geometric organization not only validates the CryoSBI approach but enables us to learn more from the data structure and provides opportunities for improving future inference strategies by exploiting this revealed manifold geometry.
AI-guided Antibiotic Discovery Pipeline from Target Selection to Compound Identification
Authors: Maximilian G. Schuh, Joshua Hesse, Stephan A. Sieber
Date: 2025-04-15 00:00:00
Tasks: Drug Discovery, Graph Neural Network, Protein Structure Prediction

Antibiotic resistance presents a growing global health crisis, demanding new therapeutic strategies that target novel bacterial mechanisms. Recent advances in protein structure prediction and machine learning-driven molecule generation offer a promising opportunity to accelerate drug discovery. However, practical guidance on selecting and integrating these models into real-world pipelines remains limited. In this study, we develop an end-to-end, artificial intelligence-guided antibiotic discovery pipeline that spans target identification to compound realization. We leverage structure-based clustering across predicted proteomes of multiple pathogens to identify conserved, essential, and non-human-homologous targets. We then systematically evaluate six leading 3D-structure-aware generative models$\unicode{x2014}$spanning diffusion, autoregressive, graph neural network, and language model architectures$\unicode{x2014}$on their usability, chemical validity, and biological relevance. Rigorous post-processing filters and commercial analogue searches reduce over 100 000 generated compounds to a focused, synthesizable set. Our results highlight DeepBlock and TamGen as top performers across diverse criteria, while also revealing critical trade-offs between model complexity, usability, and output quality. This work provides a comparative benchmark and blueprint for deploying artificial intelligence in early-stage antibiotic development.
ProtFlow: Fast Protein Sequence Design via Flow Matching on Compressed Protein Language Model Embeddings
Authors: Zitai Kong, Yiheng Zhu, Yinlong Xu, Hanjing Zhou, Mingzhe Yin, Jialu Wu, Hongxia Xu, Chang-Yu Hsieh, Tingjun Hou, Jian Wu
Date: 2025-04-15 00:00:00
Tasks: Language Modeling, Language Modelling, Protein Design, Protein Language Model

The design of protein sequences with desired functionalities is a fundamental task in protein engineering. Deep generative methods, such as autoregressive models and diffusion models, have greatly accelerated the discovery of novel protein sequences. However, these methods mainly focus on local or shallow residual semantics and suffer from low inference efficiency, large modeling space and high training cost. To address these challenges, we introduce ProtFlow, a fast flow matching-based protein sequence design framework that operates on embeddings derived from semantically meaningful latent space of protein language models. By compressing and smoothing the latent space, ProtFlow enhances performance while training on limited computational resources. Leveraging reflow techniques, ProtFlow enables high-quality single-step sequence generation. Additionally, we develop a joint design pipeline for the design scene of multichain proteins. We evaluate ProtFlow across diverse protein design tasks, including general peptides and long-chain proteins, antimicrobial peptides, and antibodies. Experimental results demonstrate that ProtFlow outperforms task-specific methods in these applications, underscoring its potential and broad applicability in computational protein sequence design and analysis.
FLOWR: Flow Matching for Structure-Aware De Novo, Interaction- and Fragment-Based Ligand Generation
Authors: Julian Cremer, Ross Irwin, Alessandro Tibo, Jon Paul Janet, Simon Olsson, Djork-Arné Clevert
Date: 2025-04-14 00:00:00

We introduce FLOWR, a novel structure-based framework for the generation and optimization of three-dimensional ligands. FLOWR integrates continuous and categorical flow matching with equivariant optimal transport, enhanced by an efficient protein pocket conditioning. Alongside FLOWR, we present SPINDR, a thoroughly curated dataset comprising ligand-pocket co-crystal complexes specifically designed to address existing data quality issues. Empirical evaluations demonstrate that FLOWR surpasses current state-of-the-art diffusion- and flow-based methods in terms of PoseBusters-validity, pose accuracy, and interaction recovery, while offering a significant inference speedup, achieving up to 70-fold faster performance. In addition, we introduce FLOWR:multi, a highly accurate multi-purpose model allowing for the targeted sampling of novel ligands that adhere to predefined interaction profiles and chemical substructures for fragment-based design without the need of re-training or any re-sampling strategies
Software package for simulations using the coarse-grained CALVADOS model
Authors: Sören von Bülow, Ikki Yasuda, Fan Cao, Thea K. Schulze, Anna Ida Trolle, Arriën Symon Rauh, Ramon Crehuet, Kresten Lindorff-Larsen, Giulio Tesei
Date: 2025-04-14 00:00:00

We present the CALVADOS package for performing simulations of biomolecules using OpenMM and the coarse-grained CALVADOS model. The package makes it easy to run simulations using the family of CALVADOS models of biomolecules including disordered proteins, multi-domain proteins, proteins in crowded environments, and disordered RNA. We briefly describe the CALVADOS force fields and how they were parametrised. We then discuss the design paradigms and architecture of the CALVADOS package, and give examples of how to use it for running and analysing simulations. The simulation package is freely available under a GNU GPL license; therefore, it can easily be extended and we provide some examples of how this might be done.
Inferring genotype-phenotype maps using attention models
Authors: Krishna Rijal, Caroline M. Holmes, Samantha Petti, Gautam Reddy, Michael M. Desai, Pankaj Mehta
Date: 2025-04-14 00:00:00
Tasks: Transfer Learning

Predicting phenotype from genotype is a central challenge in genetics. Traditional approaches in quantitative genetics typically analyze this problem using methods based on linear regression. These methods generally assume that the genetic architecture of complex traits can be parameterized in terms of an additive model, where the effects of loci are independent, plus (in some cases) pairwise epistatic interactions between loci. However, these models struggle to analyze more complex patterns of epistasis or subtle gene-environment interactions. Recent advances in machine learning, particularly attention-based models, offer a promising alternative. Initially developed for natural language processing, attention-based models excel at capturing context-dependent interactions and have shown exceptional performance in predicting protein structure and function. Here, we apply attention-based models to quantitative genetics. We analyze the performance of this attention-based approach in predicting phenotype from genotype using simulated data across a range of models with increasing epistatic complexity, and using experimental data from a recent quantitative trait locus mapping study in budding yeast. We find that our model demonstrates superior out-of-sample predictions in epistatic regimes compared to standard methods. We also explore a more general multi-environment attention-based model to jointly analyze genotype-phenotype maps across multiple environments and show that such architectures can be used for "transfer learning" - predicting phenotypes in novel environments with limited training data.
Classifying Copy Number Variations Using State Space Modeling of Targeted Sequencing Data: A Case Study in Thalassemia
Authors: Austin Talbot, Alex Kotlar, Lavanya Rishishiwar, Yue Ke
Date: 2025-04-14 00:00:00
Tasks: Prognosis, Specificity

Thalassemia, a blood disorder and one of the most prevalent hereditary genetic disorders worldwide, is often caused by copy number variations (CNVs) in the hemoglobin genes. This disorder has incredible diversity, with a large number of distinct profiles corresponding to alterations of different regions in the genes. Correctly classifying an individual's profile is critical as it impacts treatment, prognosis, and genetic counseling. However, genetic classification is challenging due to the large number of profiles worldwide, and often requires a large number of sequential tests. Targeted next generation sequencing (NGS), which characterizes segments of an individual's genome, has the potential to dramatically reduce the cost of testing and increase accuracy. In this work, we introduce a probabilistic state space model for profiling thalassemia from targeted NGS data, which naturally characterize the spatial ordering of the genes along the chromosome. We then use decision theory to choose the best profile among the different options. Due to our use of Bayesian methodology, we are also able to detect low-quality samples to be excluded from consideration, an important component of clinical screening. We evaluate our model on a dataset of 57 individuals, including both controls and cases with a variety of thalassemia profiles. Our model has a sensitivity of 0.99 and specificity of 0.93 for thalassemia detection, and accuracy of 91.5\% for characterizing subtypes. Furthermore, the specificity and accuracy rise to $0.96$ and 93.9\% when low-quality samples are excluded using our automated quality control method. This approach outperforms alternative methods, particularly in specificity, and is broadly applicable to other disorders.
Multimodal 3D Genome Pre-training
Authors: Minghao Yang, Pengteng Li, Yan Liang, Qianyi Cai, Zhihang Zheng, Shichen Zhang, Pengfei Zhang, Zhi-An Huang, Hui Xiong
Date: 2025-04-12 00:00:00

Deep learning techniques have driven significant progress in various analytical tasks within 3D genomics in computational biology. However, a holistic understanding of 3D genomics knowledge remains underexplored. Here, we propose MIX-HIC, the first multimodal foundation model of 3D genome that integrates both 3D genome structure and epigenomic tracks, which obtains unified and comprehensive semantics. For accurate heterogeneous semantic fusion, we design the cross-modal interaction and mapping blocks for robust unified representation, yielding the accurate aggregation of 3D genome knowledge. Besides, we introduce the first large-scale dataset comprising over 1 million pairwise samples of Hi-C contact maps and epigenomic tracks for high-quality pre-training, enabling the exploration of functional implications in 3D genomics. Extensive experiments show that MIX-HIC can significantly surpass existing state-of-the-art methods in diverse downstream tasks. This work provides a valuable resource for advancing 3D genomics research.
Emergence of Creativity and Individuality in Music: Insights from Brain's Statistical Learning and its Embodied Mechanisms
Authors: Tatsuya Daikoku
Date: 2025-04-15 00:00:00

Music is a universal feature of human culture, linked to embodied cognitive functions that drive learning, action, and the emergence of creativity and individuality. Evidence highlights the critical role of statistical learning an implicit cognitive process of the brain in musical creativity and individuality. Despite its significance, the precise neural and computational mechanisms underpinning these dynamic and embodied cognitive processes re-main poorly understood. This paper discusses how individuality and creativity emerge within the framework of the brain's statistical learning, drawing on a series of neural and computational studies. This work offers perspectives on the mechanisms driving the heterogeneous nature of statistical learning abilities and embodied mechanisms and provides a framework to explain the paradoxical phenomenon where individuals with specific cognitive traits that limit certain perceptual abilities excel in creative domains.