Machine Learning and more specifically Deep Learning involves solving large scale nonconvex optimization problems. Several algorithms have been proposed in the literature, that seem to achieve satisfactory practical efficiency for difficult instances, the Stochastic Gradient Method being the most rudimentary, while sti...
A unified pipeline for univariate time series classification via complex networks and persistent homology is presented, showing that no single construction dominates and persistence-based features degrade gracefully under noise, consistent with the classical stability theorem of persistent homology.
It is shown that zeroth persistent homology together with the cophenetic metric and hierarchical clustering algorithms with a number of different metrics do deliver statistically verifiable commensurate topological information based on experimental results the authors obtained on different datasets.
İsmail Güzel, A. Kaygun· Computational statistics (Ze...· 4 citations
A penalized DRO formulation in which the adversary may choose any distribution but incurs a Wasserstein penalty for deviating from the empirical distribution is studied, showing that the adversary's problem can be reformulated as an optimization problem over transport maps that push empirical samples to adversarial one...
Alireza Abdollahpoorrostam, Ehsan Sharifian, Buse Sen et al.· 0 citations
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A regime-adaptive deconfounding layer that first estimates the confounding regime from data using a Mar\v{c}enko--Pastur spectral router, then applies a deconfounding strategy matched to that regime, which attenuates factor-dominated variation and recovers contemporaneous structure from the resulting innovations.
A Sampling-Aware Global Evaluation (SAGE) benchmark is introduced, combining GBIF records for training with sPlotOpen vegetation plots for presence-absence evaluation across 5771 plant species, and an evaluation framework that groups species based on two properties, sampling effort and relative prevalence is proposed.
Emilia Arens, Nina Van Tiel, Robin Zbinden et al.· 0 citations
It is shown that minimizing cross-entropy can be misaligned with minimizing verifier risk; two policies can assign identical likelihood to the observed demonstrations while placing different probability mass on incorrect outputs, and entropy-regularized training consistently improves verifier accuracy over standard cro...
Mihir Dhanakshirur, Adam Ousherovitch, A. Tewari· 0 citations
Neural Optimal Particle Filters are introduced, which learn an amortized approximation to the optimal proposal from offline simulated one-step conditioning tuples, and improve sample efficiency and distributional accuracy over standard PF baselines with modest computational overhead.
A functional neural network for learning trajectories on the Riemannian manifold of symmetric positive definite (SPD) matrices, termed MatFAE, which features intrinsic layers that map manifold-valued functions to Euclidean vector-valued functions, followed by a functional layer that projects them into a finite-dimensio...
Modern recommendation systems increasingly rely on dynamically routing diverse queries to multiple embedding models. Despite its practical significance, this problem remains poorly understood under realistic conditions like adversarial queries, bandit feedback, and limited observability of models. We formalize embeddin...
Yan Dai, Negin Golrezaei, Patrick Jaillet· 0 citations
INFUSER is introduced, an iterative co-training framework with two co-evolving roles: a Generator that drafts questions and reference golden answers from a pool of unstructured, automatically collected documents, and a Solver that improves by training on them, and DuGRPO, a dual-normalized variant of GRPO, for generato...
Neural operators have achieved strong performance in learning solution operators of partial differential equations (PDEs), but their inherently continuous representations struggle to capture discontinuities and sharp transitions. Existing approaches typically approximate such features within continuous function spaces,...
Ha Dang, Sebastian Schmidt, Juergen Hesser· 0 citations
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
Is your company’s AI sovereign? A practical guide for CIOs on AI vendor lock-in, EU Data Act switching, cloud sovereignty, and testing your exit plan before you need it. The post Sovereignty Is the Option to Leave appeared first on GPT-Lab.
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