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arXiv stat.ML Papers

On the convergence of graph Laplacians with a symmetric divergence

arXiv:2607.05892v1 Announce Type: new Abstract: When analyzing a manifold learning algorithm for data lying on a smooth, compact, connected Riemannian submanifold $(mathcal{M}, g)$ of $mathbb{R}^d$, a…

arXiv stat.ML Papers

Boosting with List-Decodable Codes

arXiv:2607.05791v1 Announce Type: new Abstract: Boosting is a fundamental technique for generically improving the accuracy of learning algorithms (Schapire 1989). Existing boosting algorithms construct a strong…

arXiv stat.ML Papers

Separation Capacity of Scattering Networks on Low-Dimensional Datasets

arXiv:2607.06048v1 Announce Type: new Abstract: We aim to identify scattering network architectures that maximize the separation capacity on data with low intrinsic dimension. The networks we…

arXiv stat.ML Papers

Deep Neural Variation Spaces: A Unifying Perspective on Depth and Complexity

arXiv:2607.05546v1 Announce Type: new Abstract: We develop a unified function space theory of deep fully connected neural networks. Functions in our spaces are defined recursively as…

arXiv stat.ML Papers

Gaussian Process Latent Factor Regression for Low-Data, High-Dimensional Output Problems

arXiv:2606.06576v2 Announce Type: replace-cross Abstract: In the sciences, regression tasks often require predicting high-dimensional outputs from few training examples. Multi-output Gaussian processes excel in low-data regimes…

arXiv stat.ML Papers

Distribution-free changepoint localization after sequential change detection

arXiv:2606.01256v2 Announce Type: replace Abstract: This paper introduces a distribution-free framework for constructing post-detection confidence sets for changepoints after stopping a sequential change detection procedure. It…

arXiv stat.ML Papers

Model-based Bootstrap of Controlled Markov Chains

arXiv:2605.12410v2 Announce Type: replace Abstract: We propose and analyze a model-based bootstrap for transition kernels in finite controlled Markov chains (CMCs) with possibly nonstationary or history-dependent…

arXiv cs.NE Papers

Neuromorphic Silicon Neuron Controller for Adaptive Deep Brain Stimulation in Parkinson's Disease

arXiv:2607.05453v1 Announce Type: cross Abstract: Parkinson's disease (PD) affects millions worldwide and causes severe motor symptoms. Adaptive deep brain stimulation (aDBS) delivers physiologically informed stimulation that…

arXiv cs.NE Papers

Scalable Perturbation Learning for Online Self-Supervised Echo State Networks

arXiv:2607.06079v1 Announce Type: cross Abstract: Intelligent systems should not only solve tasks but also adapt under real-world constraints. Autonomous adaptation via self-supervised learning, sequential adaptation via…

arXiv cs.NE Papers

A Hardware-Aware Open-Source Framework for Design Space Exploration of Mixed-Signal Spiking Neural Networks

arXiv:2607.06456v1 Announce Type: cross Abstract: Energy-efficient neuromorphic computing at the edge requires simulation tools that can capture the non-ideal behavior of mixed-signal spiking neural network (SNN)…

arXiv cs.NE Papers

ADSEQ: A delay-aware autograd-compatible framework for spike-event delivery in SNNs

arXiv:2512.05906v2 Announce Type: replace Abstract: Spiking neural networks (SNNs), central to computational neuroscience and neuromorphic machine learning (ML), require efficient simulation and gradient-based training. While AI…

arXiv cs.NE Papers

Variable Search Stepsize for Randomized Local Search in Multi-Objective Combinatorial Optimization

arXiv:2602.05675v2 Announce Type: replace Abstract: Over the past two decades, research in evolutionary multi-objective optimization has predominantly focused on continuous domains, with comparatively limited attention given…

arXiv cs.NE Papers

Finding Low Star Discrepancy 3D Kronecker Point Sets Using Algorithm Configuration Techniques

arXiv:2604.00786v2 Announce Type: replace Abstract: The L infinity star discrepancy is a measure for how uniformly a point set is distributed in a given space. Point…

arXiv cs.LG Papers

Conformal Prediction Sets for Instance Segmentation

arXiv:2602.10045v2 Announce Type: replace-cross Abstract: Current instance segmentation models achieve high performance on average predictions, but lack principled uncertainty quantification: their outputs are not calibrated, and…

arXiv cs.LG Papers

$mathbf{lambda}$-VAE: Variance Equalization for Posterior Collapse

arXiv:2607.05531v1 Announce Type: new Abstract: Variational Autoencoders (VAEs) frequently suffer from posterior collapse, a failure mode in which the approximate posterior converges to the prior, rendering…

arXiv cs.LG Papers

MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning

arXiv:2409.06067v3 Announce Type: replace-cross Abstract: Previous studies on federated learning (FL) often encounter performance degradation due to data heterogeneity among different clients. In light of the…

arXiv cs.LG Papers

PatchOptic for Shared-State LLM Workflows with Projected Views and Verified Structured Updates

arXiv:2607.05483v1 Announce Type: new Abstract: Agentic workflows often operate over shared, structured state. Because LLM context windows are limited, each model invocation is typically shown only…

arXiv cs.LG Papers

Active Quantum Kernel Acquisition for Gaussian Process Regression

arXiv:2606.28833v3 Announce Type: replace Abstract: Quantum kernel estimation on near-term hardware is shot-budgeted: every entry of the kernel Gram matrix is a Bernoulli expectation that must…

arXiv cs.LG Papers

InvWeaver: Deductive Feedback for Invariant Synthesis in Interacting-Loop Programs

arXiv:2607.05478v1 Announce Type: new Abstract: Loop invariant inference is a fundamental yet challenging problem in program verification. Recent LLM-aided guess-and-check techniques have shown strong performance on…

arXiv cs.LG Papers

Transformers converge to invariant algorithmic cores

arXiv:2602.22600v2 Announce Type: replace Abstract: Training selects for behavior, not circuitry: many weight configurations can implement the same function. Studying any single trained neural network thus…

arXiv cs.LG Papers

Parameter-Free Encoders Remain Viable for RDB Foundation Models

arXiv:2607.05476v1 Announce Type: new Abstract: Given a relational database (RDB) storing heterogeneous tabular information, how can we predict missing (or future) values in some target column…

arXiv cs.LG Papers

SmartMixed: A Two-Phase Training Strategy for Adaptive Activation Function Learning in Neural Networks

arXiv:2510.22450v4 Announce Type: replace Abstract: The choice of activation function plays a critical role in neural networks, yet most architectures still rely on fixed, uniform activation…

arXiv cs.LG Papers

Breaking Structural Isolation: Scalable Graph Clustering via Community-Aware Sampling and Structural Entropy

arXiv:2607.05469v1 Announce Type: new Abstract: Unsupervised graph clustering is a fundamental technique for uncovering underlying semantic patterns in large-scale networks. Although Graph Contrastive Learning has demonstrated…

arXiv cs.LG Papers

Variational Learning of Disentangled Representations

arXiv:2506.17182v3 Announce Type: replace Abstract: Disentangled representations separate factors that are shared across conditions from those that are condition-specific. Such separation is needed for generalization to…