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arXiv stat.ML Machine Learning
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Reposted by arXiv stat.ML Machine Learning
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arXiv cs.LG Machine Learning @cslg-bot.bsky.social · 19h
Jinwen Xu, Qin Lu, Georgios B. Giannakis: Conformalized Gaussian processes for online uncertainty quantification over graphs https://arxiv.org/abs/2510.06181 https://arxiv.org/pdf/2510.06181 https://arxiv.org/html/2510.06181
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arXiv cs.LG Machine Learning @cslg-bot.bsky.social · 19h
Kurt Butler, Guanchao Feng, Petar Djuric: Higher-Order Feature Attribution: Bridging Statistics, Explainable AI, and Topological Signal Processing https://arxiv.org/abs/2510.06165 https://arxiv.org/pdf/2510.06165 https://arxiv.org/html/2510.06165
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Reposted by arXiv stat.ML Machine Learning
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arXiv cs.LG Machine Learning @cslg-bot.bsky.social · 19h
Markus Krimmel, Philip Hartout, Karsten Borgwardt, Dexiong Chen: PolyGraph Discrepancy: a classifier-based metric for graph generation https://arxiv.org/abs/2510.06122 https://arxiv.org/pdf/2510.06122 https://arxiv.org/html/2510.06122
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Reposted by arXiv stat.ML Machine Learning
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arXiv cs.LG Machine Learning @cslg-bot.bsky.social · 19h
Alessandro Favero: The Physics of Data and Tasks: Theories of Locality and Compositionality in Deep Learning https://arxiv.org/abs/2510.06106 https://arxiv.org/pdf/2510.06106 https://arxiv.org/html/2510.06106
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Reposted by arXiv stat.ML Machine Learning
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arXiv cs.LG Machine Learning @cslg-bot.bsky.social · 19h
Lulu Gong, Shreya Saxena: Learning Mixtures of Linear Dynamical Systems (MoLDS) via Hybrid Tensor-EM Method https://arxiv.org/abs/2510.06091 https://arxiv.org/pdf/2510.06091 https://arxiv.org/html/2510.06091
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Reposted by arXiv stat.ML Machine Learning
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arXiv cs.LG Machine Learning @cslg-bot.bsky.social · 19h
Andreas Maurer, Erfan Mirzaei, Massimiliano Pontil: Generalization of Gibbs and Langevin Monte Carlo Algorithms in the Interpolation Regime https://arxiv.org/abs/2510.06028 https://arxiv.org/pdf/2510.06028 https://arxiv.org/html/2510.06028
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Reposted by arXiv stat.ML Machine Learning
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arXiv cs.LG Machine Learning @cslg-bot.bsky.social · 19h
Kevin Raina, Tanya Schmah: Out-of-Distribution Detection from Small Training Sets using Bayesian Neural Network Classifiers https://arxiv.org/abs/2510.06025 https://arxiv.org/pdf/2510.06025 https://arxiv.org/html/2510.06025
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Reposted by arXiv stat.ML Machine Learning
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arXiv cs.LG Machine Learning @cslg-bot.bsky.social · 19h
Randall Balestriero, Nicolas Ballas, Mike Rabbat, Yann LeCun: Gaussian Embeddings: How JEPAs Secretly Learn Your Data Density https://arxiv.org/abs/2510.05949 https://arxiv.org/pdf/2510.05949 https://arxiv.org/html/2510.05949
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Reposted by arXiv stat.ML Machine Learning
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arXiv cs.LG Machine Learning @cslg-bot.bsky.social · 19h
Adhithyan Kalaivanan, Zheng Zhao, Jens Sj\"olund, Fredrik Lindsten: ESS-Flow: Training-free guidance of flow-based models as inference in source space https://arxiv.org/abs/2510.05849 https://arxiv.org/pdf/2510.05849 https://arxiv.org/html/2510.05849
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Reposted by arXiv stat.ML Machine Learning
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arXiv cs.LG Machine Learning @cslg-bot.bsky.social · 19h
Giannone, Xu, Nayak, Awhad, Sudalairaj, Xu, Srivastava: Mitigating Premature Exploitation in Particle-based Monte Carlo for Inference-Time Scaling https://arxiv.org/abs/2510.05825 https://arxiv.org/pdf/2510.05825 https://arxiv.org/html/2510.05825
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arXiv cs.LG Machine Learning @cslg-bot.bsky.social · 19h
Salah Eddine Choutri, Prajwal Chauhan, Othmane Mazhar, Saif Eddin Jabari: Monte Carlo-Type Neural Operator for Differential Equations https://arxiv.org/abs/2510.05620 https://arxiv.org/pdf/2510.05620 https://arxiv.org/html/2510.05620
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arXiv q-fin.CP Computational Finance @qfincp-bot.bsky.social · 18h
Jinho Cha, Sahng-Min Han, Long Pham: Smart Contract Adoption under Discrete Overdispersed Demand: A Negative Binomial Optimization Perspective https://arxiv.org/abs/2510.05487 https://arxiv.org/pdf/2510.05487 https://arxiv.org/html/2510.05487
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Reposted by arXiv stat.ML Machine Learning
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arXiv cs.LG Machine Learning @cslg-bot.bsky.social · 19h
Runlin Zhou, Chixiang Chen, Elynn Chen: Prior-Aligned Meta-RL: Thompson Sampling with Learned Priors and Guarantees in Finite-Horizon MDPs https://arxiv.org/abs/2510.05446 https://arxiv.org/pdf/2510.05446 https://arxiv.org/html/2510.05446
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arXiv cs.AI Artificial Intelligence @csai-bot.bsky.social · 19h
Asli Karacelik: Integrating Bayesian methods with neural network--based model predictive control: a review https://arxiv.org/abs/2510.05338 https://arxiv.org/pdf/2510.05338 https://arxiv.org/html/2510.05338
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Reposted by arXiv stat.ML Machine Learning
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arXiv cs.LG Machine Learning @cslg-bot.bsky.social · 19h
Qian Wang, Mohammad N. Bisheh, Kamran Paynabar: Tensor-on-tensor Regression Neural Networks for Process Modeling with High-dimensional Data https://arxiv.org/abs/2510.05329 https://arxiv.org/pdf/2510.05329 https://arxiv.org/html/2510.05329
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Reposted by arXiv stat.ML Machine Learning
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arXiv cs.LG Machine Learning @cslg-bot.bsky.social · 19h
Joel Wendin, Erik G. Larsson, Claudio Altafini: Computing frustration and near-monotonicity in deep neural networks https://arxiv.org/abs/2510.05286 https://arxiv.org/pdf/2510.05286 https://arxiv.org/html/2510.05286
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arXiv cs.AI Artificial Intelligence @csai-bot.bsky.social · 19h
Joshua Kazdan, Rylan Schaeffer, Youssef Allouah, Colin Sullivan, Kyssen Yu, Noam Levi, Sanmi Koyejo: Efficient Prediction of Pass@k Scaling in Large Language Models https://arxiv.org/abs/2510.05197 https://arxiv.org/pdf/2510.05197 https://arxiv.org/html/2510.05197
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arXiv q-bio.QM Quantitative Methods @qbioqm-bot.bsky.social · 18h
Jiacheng Wu: Aneurysm Growth Time Series Reconstruction Using Physics-informed Autoencoder https://arxiv.org/abs/2510.05183 https://arxiv.org/pdf/2510.05183 https://arxiv.org/html/2510.05183
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arXiv cs.SE Software Engineering @csse-bot.bsky.social · 19h
Yu Zhu: Adaptive Reinforcement Learning for Dynamic Configuration Allocation in Pre-Production Testing https://arxiv.org/abs/2510.05147 https://arxiv.org/pdf/2510.05147 https://arxiv.org/html/2510.05147
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arXiv stat.ML Machine Learning @statml-bot.bsky.social · 18h
Hwanwoo Kim, Dongkyu Derek Cho, Eric Laber: Implicit Updates for Average-Reward Temporal Difference Learning https://arxiv.org/abs/2510.06149 https://arxiv.org/pdf/2510.06149 https://arxiv.org/html/2510.06149
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arXiv stat.ML Machine Learning @statml-bot.bsky.social · 18h
Hossein Taheri, Avishek Ghosh, Arya Mazumdar: On the Theory of Continual Learning with Gradient Descent for Neural Networks https://arxiv.org/abs/2510.05573 https://arxiv.org/pdf/2510.05573 https://arxiv.org/html/2510.05573
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arXiv stat.ML Machine Learning @statml-bot.bsky.social · 18h
Nelsen, Owhadi, Stuart, Yang, Zou: Bilevel optimization for learning hyperparameters: Application to solving PDEs and inverse problems with Gaussian processes https://arxiv.org/abs/2510.05568 https://arxiv.org/pdf/2510.05568 https://arxiv.org/html/2510.05568
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statml-bot.bsky.social
arXiv stat.ML Machine Learning @statml-bot.bsky.social · 18h
Zhexiao Lin, Yuanyuan Li, Neeraj Sarna, Yuanyuan Gao, Michael von Gablenz: Domain-Shift-Aware Conformal Prediction for Large Language Models https://arxiv.org/abs/2510.05566 https://arxiv.org/pdf/2510.05566 https://arxiv.org/html/2510.05566
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arXiv stat.ML Machine Learning @statml-bot.bsky.social · 18h
Simon Segert, Nathan Wycoff: A Probabilistic Basis for Low-Rank Matrix Learning https://arxiv.org/abs/2510.05447 https://arxiv.org/pdf/2510.05447 https://arxiv.org/html/2510.05447
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arXiv stat.ML Machine Learning @statml-bot.bsky.social · 18h
Ran Canetti, Ephraim Linder, Connor Wagaman: Refereed Learning https://arxiv.org/abs/2510.05440 https://arxiv.org/pdf/2510.05440 https://arxiv.org/html/2510.05440
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