Cover Image

Fast Compute for ML Optimization

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Polson, Nick, Sokolov, Vadim
Format:	Preprint
Published:	2026
Subjects:	Computation Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Generative Bayesian Computation as a Scalable Alternative to Gaussian Process Surrogates
by: Polson, Nick, et al.
Published: (2026)

Bayesian Double Descent
by: Polson, Nick, et al.
Published: (2025)

Generative Bayesian Computation for Maximum Expected Utility
by: Polson, Nick, et al.
Published: (2024)

Generative Modeling: A Review
by: Nareklishvili, Maria, et al.
Published: (2024)

Generative Bayesian Hyperparameter Tuning
by: Lopes, Hedibert, et al.
Published: (2025)

Horseshoe Mixtures-of-Experts (HS-MoE)
by: Polson, Nick, et al.
Published: (2026)

Kolmogorov GAM Networks are all you need!
by: Polson, Sarah, et al.
Published: (2025)

Fast Compute via MC Boosting
by: Polson, Sarah, et al.
Published: (2026)

$MC^2$ Mixed Integer and Linear Programming
by: Polson, Nick, et al.
Published: (2025)

Counting $N$ Queens
by: Polson, Nick, et al.
Published: (2024)

Bell's Inequality, Causal Bounds, and Quantum Bayesian Computation: A Unified Framework
by: Polson, Nick, et al.
Published: (2026)

Generative AI for Bayesian Computation
by: Polson, Nicholas G., et al.
Published: (2023)

Generative Quantile Bayesian Prediction
by: Nareklishvili, Maria, et al.
Published: (2025)

Horseshoe Priors and MDP
by: Polson, Nick, et al.
Published: (2026)

Synthetic Priors
by: Polson, Nick, et al.
Published: (2026)

Negative Probability
by: Polson, Nick, et al.
Published: (2024)

Bayesian Methods for the Navier-Stokes Equations
by: Polson, Nicholas, et al.
Published: (2026)

Photons = Tokens: The Physics of AI and the Economics of Knowledge
by: Litowitz, Alec, et al.
Published: (2026)

Some Bayesian Perspectives on Clinical Trials
by: Sokolova, Alexandra, et al.
Published: (2026)

Bayesian Global-Local Regularization
by: Datta, Jyotishka, et al.
Published: (2025)

Kramnik vs Nakamura: A Chess Scandal
by: Maharaj, Shiva, et al.
Published: (2024)

FluidML: Fast and Memory Efficient Inference Optimization
by: Liu, Jinjie, et al.
Published: (2024)

FAST: An Optimization Framework for Fast Additive Segmentation in Transparent ML
by: Liu, Brian, et al.
Published: (2024)

skscope: Fast Sparsity-Constrained Optimization in Python
by: Wang, Zezhi, et al.
Published: (2024)

Fast and Robust: Computationally Efficient Covariance Estimation for Sub-Weibull Vectors
by: He, Even
Published: (2025)

Generative Causal Inference
by: Nareklishvili, Maria, et al.
Published: (2023)

Generative AI for Validating Physics Laws
by: Nareklishvili, Maria, et al.
Published: (2025)

Mapping on a Budget: Optimizing Spatial Data Collection for ML
by: Betti, Livia, et al.
Published: (2025)

Compress the Context, Keep the Commitments: A Formal Framework for Verifiable LLM Context Compression
by: Trukhina, Natalia, et al.
Published: (2026)

MicroHD: An Accuracy-Driven Optimization of Hyperdimensional Computing Algorithms for TinyML systems
by: Ponzina, Flavio, et al.
Published: (2024)

A Fast Task Offloading Optimization Framework for IRS-Assisted Multi-Access Edge Computing System
by: Wu, Jianqiu, et al.
Published: (2023)

Tensor Decomposition Networks for Fast Machine Learning Interatomic Potential Computations
by: Lin, Yuchao, et al.
Published: (2025)

Open Artificial Knowledge
by: Borisov, Vadim, et al.
Published: (2024)

MosaicBERT: A Bidirectional Encoder Optimized for Fast Pretraining
by: Portes, Jacob, et al.
Published: (2023)

Magneton: Optimizing Energy Efficiency of ML Systems via Differential Energy Debugging
by: Pan, Yi, et al.
Published: (2025)

MLonMCU: TinyML Benchmarking with Fast Retargeting
by: van Kempen, Philipp, et al.
Published: (2023)

ML Interpretability: Simple Isn't Easy
by: Räz, Tim
Published: (2022)

Merging two cultures: Deep and statistical learning
by: Anindya Bhadra, et al.
Published: (2024)

Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning
by: Lillo, Lute, et al.
Published: (2026)

MARINA-P: Superior Performance in Non-smooth Federated Optimization with Adaptive Stepsizes
by: Sokolov, Igor, et al.
Published: (2024)