arXiv Machine Learning

Eigenvalue-Decomposition Cost Denoising as an Alternative to Predict-then-Optimize for Shortest-Path Problems

The paper proposes using eigenvalue decomposition (or PCA) to denoise noisy cost observations for shortest‑path problems, instead of the traditional predict‑then‑optimize approach. By projecting new cost vectors onto the top‑k eigenvectors of the training covariance matrix before running Dijkstra’s algorithm, the method can recover the true underlying costs. Experiments on a 5×5 grid benchmark show that choosing k equal to the true latent feature dimension (k=5) yields the best performance, outperforming the SPO+ method especially under high model misspecification.

arXiv Machine Learning

Jun 19

Spectral DPPs via NEPv: A Scalable Continuous Relaxation of Determinantal MAP for Diversity-Aware Data Selection

arXiv:2606. 19411v1 Announce Type: new Abstract: Selecting a small, diverse, high-quality subset from a massive pool of candidates is a recurring primitive in modern machine learning -- data curation and coreset selection for training and fine-tuning large models, active-learning batch acquisition, prompt and exemplar selection for in-context learning, retrieval diversification, and experimental design.

By Richard Yi Da Xu

llms rag fine-tuning benchmarks

arXiv Machine Learning

Sep 22

On Kernel Eigen-alignments of KRR: Reconstruction and Generalization

arXiv:2605.15240v2 Announce Type: replace-cross Abstract: This paper investigates the critical role of eigenalignments between the kernel matrix and learning targets in achieving robust generalizatio...

By Yang Liu, Ernest Fokoue, Richard Lange, Daniel Krutz

safety

arXiv Machine Learning

Jun 5

Efficient Mean Curvature Computation on High-Dimensional Data Manifolds

arXiv:2606. 06329v1 Announce Type: new Abstract: Estimating local mean curvature at each point of a high-dimensional dataset is a key ingredient of geometry-aware machine learning algorithms, such as the Mean Curvature Boundary Points (MCBP) method.

By Alexandre L. M. Levada

arXiv Machine Learning

Sep 15

Sharp Rates and a One-Line Correction for Spectral Representation Learning

arXiv:2609.15825v1 Announce Type: new Abstract: A self-supervised encoder is trained once, frozen, and reused through lightweight probes on tasks nobody named at training time; the practitioner's que...

By Dier Tang, Jing Yee Tan, Guangyue Han

safety

arXiv Machine Learning

Jun 30

Replica Symmetry Breaking and Algorithmic Thresholds in Empirical Risk Minimization under Multi-Index Model

arXiv:2606. 28573v1 Announce Type: new Abstract: Modern machine learning models are trained by optimizing high-dimensional non-convex empirical risk functions.

By Andrea Montanari, Kangjie Zhou

arXiv Machine Learning

Jul 1

Direction-Magnitude Decomposition for Low-Rank Matrix Optimization: Faster Convergence and Saddle-to-saddle Dynamics

arXiv:2606. 31390v1 Announce Type: cross Abstract: Low-rank matrix optimization is often carried out via the Burer-Monteiro (BM) formulation, but choosing the factorization rank $r$ is delicate and can substantially slow optimization.

By Yudong Wei, Liang Zhang, Bingcong Li, Niao He

arXiv Machine Learning

Jun 2

Global Convergence of Adaptive Sensing for Principal Eigenvector Estimation

arXiv:2505. 10882v2 Announce Type: replace Abstract: Principal component analysis classically requires full $d$-dimensional samples, yet in various applications hardware limits acquisition to a few scalar measurements per sample.

By Alex Saad-Falcon, Brighton Ancelin, Justin Romberg

arXiv AI

Jun 9

Scaling Decision-Focused Learning to Large Problems with Lagrangian Decomposition

arXiv:2606. 08797v1 Announce Type: cross Abstract: Decision-focused learning has shown great promise for addressing predict-then-optimize problems, particularly in the presence of under-specified models.

By St\'ephane Eilles-Chan Way, Hugo Percot, Quentin Cappart, Tias Guns, Louis-Martin Rousseau

benchmarks

arXiv Machine Learning

Jun 29

Graph Dimensionality Reduction for Contextual Bandits: Structure-Specific Regret Bounds under Approximate Smoothness and Noisy Eigenspaces

arXiv:2606. 27917v1 Announce Type: new Abstract: Contextual bandits with graph-structured arms arise in recommendation, citation retrieval, and social advertising, where arms connected on a graph tend to share reward signal.

By Joyanta Jyoti Mondal, Ibne Farabi Shihab, Anuj Sharma

benchmarks

arXiv Machine Learning

Jul 14

Bandit PCA with Minimax Optimal Regret

arXiv:2607. 10936v1 Announce Type: new Abstract: We study the bandit-feedback version of online principal component analysis (Bandit PCA): in each round $t = 1,\dots,T$, the adversary selects a $d \times d$ symmetric gain matrix $G_t$ with spectrum in $[0,1]$ and rank at most $r$; the learner simultaneously selects a unit vector $w_t \in S^{d-1}$ and receives the reward $w_t^\top G_t w_t$.

By Mo\"ise Blanchard, Dmitrii Ostrovskii, Aadirupa Saha

reinforcement-learning

arXiv Machine Learning

Jun 26

Fast algorithms for learning a Gaussian under halfspace truncation with optimal sample complexity

arXiv:2606. 27298v1 Announce Type: cross Abstract: We study the fundamental problem of learning a high-dimensional Gaussian truncated to an unknown halfspace.

By Haitong Liu, Deepak Narayanan Sridharan, David Steurer, Manuel Wiedmer

arXiv Machine Learning

Jun 10

Sample-efficient inductive matrix completion with noise and inexact side-information

arXiv:2605. 17189v2 Announce Type: replace-cross Abstract: Inductive matrix completion (IMC) is a variant of low-rank matrix completion that incorporates row and column side-information.

By Yuepeng Yang, Cong Ma

Related stories