Cover Image

Self-Organizing Survival Manifolds: A Theory for Unsupervised Discovery of Prognostic Structures in Biological Systems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Karagoz, Atahan
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Computational Inertia as a Conserved Quantity in Frictionless and Damped Learning Dynamics
by: Karagoz, Atahan
Published: (2025)

Energentic Intelligence: From Self-Sustaining Systems to Enduring Artificial Life
by: Karagoz, Atahan
Published: (2025)

OmicsCL: Unsupervised Contrastive Learning for Cancer Subtype Discovery and Survival Stratification
by: Karagoz, Atahan
Published: (2025)

Riemannian Zeroth-Order Gradient Estimation with Structure-Preserving Metrics for Geodesically Incomplete Manifolds
by: Ma, Shaocong, et al.
Published: (2026)

A Framework for Bilevel Optimization on Riemannian Manifolds
by: Han, Andi, et al.
Published: (2024)

Manifold constrained steepest descent
by: Yang, Kaiwei, et al.
Published: (2026)

Mirror Descent on Riemannian Manifolds
by: Jiang, Jiaxin, et al.
Published: (2026)

Fréchet Regression on the Bures-Wasserstein Manifold
by: Nguyen, Duc Toan, et al.
Published: (2026)

Intrinsic Gaussian Processes on Manifolds and Their Accelerations by Symmetry
by: Ye, Ke, et al.
Published: (2020)

Distributionally Robust Learning in Survival Analysis
by: Jin, Yeping, et al.
Published: (2025)

Towards a Systems Theory of Algorithms
by: Dörfler, Florian, et al.
Published: (2024)

Geometry-Preserving Neural Architectures on Manifolds with Boundary
by: Elamvazhuthi, Karthik, et al.
Published: (2026)

Bi-Sparse Unsupervised Feature Selection
by: Xiu, Xianchao, et al.
Published: (2024)

Fast, Accurate Manifold Denoising by Tunneling Riemannian Optimization
by: Wang, Shiyu, et al.
Published: (2025)

Federated Learning on Riemannian Manifolds: A Gradient-Free Projection-Based Approach
by: Wang, Hongye, et al.
Published: (2025)

Stable Robot Motions on Manifolds: Learning Lyapunov-Constrained Neural Manifold ODEs
by: Boetius, David, et al.
Published: (2025)

Decentralized Riemannian Conjugate Gradient Method on the Stiefel Manifold
by: Chen, Jun, et al.
Published: (2023)

Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds
by: Dodd, Daniel, et al.
Published: (2024)

Manifold Regularization Classification Model Based On Improved Diffusion Map
by: Guo, Hongfu, et al.
Published: (2024)

Finite-Time Analysis of Stochastic Nonconvex Nonsmooth Optimization on the Riemannian Manifolds
by: Sahinoglu, Emre, et al.
Published: (2025)

A Persona-Based Evaluation Framework for Pluralistic Alignment in Generative AI
by: Karagoz, Atahan
Published: (2026)

Online Optimization on Hadamard Manifolds: Curvature Independent Regret Bounds on Horospherically Convex Objectives
by: Sahinoglu, Emre, et al.
Published: (2025)

Enhancing Unsupervised Feature Selection via Double Sparsity Constrained Optimization
by: Xiu, Xianchao, et al.
Published: (2025)

An Efficient Unsupervised Framework for Convex Quadratic Programs via Deep Unrolling
by: Yang, Linxin, et al.
Published: (2024)

Towards An Unsupervised Learning Scheme for Efficiently Solving Parameterized Mixed-Integer Programs
by: Qu, Shiyuan, et al.
Published: (2024)

Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization: Cardinality, Minimum, Covering, and More
by: Bu, Fanchen, et al.
Published: (2024)

Data-Driven Discovery of PDEs via the Adjoint Method
by: Sadr, Mohsen, et al.
Published: (2024)

When Deep Learning Meets Polyhedral Theory: A Survey
by: Huchette, Joey, et al.
Published: (2023)

A Tight Theory of Error Feedback Algorithms in Distributed Optimization
by: Thomsen, Daniel Berg, et al.
Published: (2026)

A Unified Theory of Stochastic Proximal Point Methods without Smoothness
by: Richtárik, Peter, et al.
Published: (2024)

A Reliability Theory of Compromise Decisions for Large-Scale Stochastic Programs
by: Diao, Shuotao, et al.
Published: (2024)

Unsupervised Ground Metric Learning
by: Auffenberg, Janis, et al.
Published: (2025)

Demystifying Manifold Constraints in LLM Pre-training
by: An, Kang, et al.
Published: (2026)

A Finite-Iteration Theory for Asynchronous Categorical Distributional Temporal-Difference Learning
by: Kaya, Ege C., et al.
Published: (2026)

Tighter Performance Theory of FedExProx
by: Anyszka, Wojciech, et al.
Published: (2024)

A Theoretical Analysis of Self-Supervised Learning for Vision Transformers
by: Huang, Yu, et al.
Published: (2024)

Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory
by: Zhang, Yufeng, et al.
Published: (2020)

A Theory of the Risk for Optimization with Relaxation and its Application to Support Vector Machines
by: Campi, Marco C., et al.
Published: (2020)

Ethics and Technical Aspects of Generative AI Models in Digital Content Creation
by: Karagoz, Atahan
Published: (2024)

Decentralized Online Riemannian Optimization Beyond Hadamard Manifolds
by: Sahinoglu, Emre, et al.
Published: (2025)