惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

The GitHub Blog
The GitHub Blog
K
Kaspersky official blog
Stack Overflow Blog
Stack Overflow Blog
Blog — PlanetScale
Blog — PlanetScale
Recorded Future
Recorded Future
Engineering at Meta
Engineering at Meta
U
Unit 42
D
Docker
I
InfoQ
D
DataBreaches.Net
Google DeepMind News
Google DeepMind News
N
Netflix TechBlog - Medium
C
Check Point Blog
The Cloudflare Blog
美
美团技术团队
V
Vulnerabilities – Threatpost
博
博客园_首页
T
Threat Research - Cisco Blogs
Google DeepMind News
Google DeepMind News
Attack and Defense Labs
Attack and Defense Labs
A
Arctic Wolf
IT之家
IT之家
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
Exploit-DB.com RSS Feed
Exploit-DB.com RSS Feed
T
Troy Hunt's Blog
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
人人都是产品经理
人人都是产品经理
C
Cyber Attacks, Cyber Crime and Cyber Security
钛媒体：引领未来商业与生活新知
钛媒体：引领未来商业与生活新知
Security Archives - TechRepublic
Security Archives - TechRepublic
S
Schneier on Security
Apple Machine Learning Research
Apple Machine Learning Research
MyScale Blog
MyScale Blog
P
Privacy International News Feed
云风的 BLOG
云风的 BLOG
Recent Commits to openclaw:main
Recent Commits to openclaw:main
T
The Blog of Author Tim Ferriss
GbyAI
GbyAI
The Last Watchdog
The Last Watchdog
D
Darknet – Hacking Tools, Hacker News & Cyber Security
aimingoo的专栏
aimingoo的专栏
P
Proofpoint News Feed
The Register - Security
The Register - Security
博
博客园 - 三生石上(FineUI控件)
Forbes - Security
Forbes - Security
NISL@THU
NISL@THU
Y
Y Combinator Blog
T
Threatpost
Microsoft Azure Blog
Microsoft Azure Blog
L
Lohrmann on Cybersecurity

stat.ML updates on arXiv.org

Adaptive multi-fidelity optimization with fast learning rates Enhancing AI and Dynamical Subseasonal Forecasts with Probabilistic Bias Correction Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model The Harder Path: Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback Stylistic-STORM (ST-STORM) : Perceiving the Semantic Nature of Appearance Collective Kernel EFT for Pre-activation ResNets PRIM-cipal components analysis One-Shot Generative Flows: Existence and Obstructions Structural interpretability in SVMs with truncated orthogonal polynomial kernels Amortized Optimal Transport from Sliced Potentials MinShap: A Modified Shapley Value Approach for Feature Selection Unsupervised feature selection using Bayesian Tucker decomposition Multi-User mmWave Beam and Rate Adaptation via Combinatorial Satisficing Bandits Best of both worlds: Stochastic & adversarial best-arm identification Scalable Model-Based Clustering with Sequential Monte Carlo Expert-Guided Class-Conditional Goodness-of-Fit Scores for Interpretable Classification with Informative Missingness: An Application to Seismic Monitoring Lightweight Geometric Adaptation for Training Physics-Informed Neural Networks Gating Enables Curvature: A Geometric Expressivity Gap in Attention Zeroth-Order Optimization at the Edge of Stability Differentially Private Conformal Prediction CLion: Efficient Cautious Lion Optimizer with Enhanced Generalization Generative Augmented Inference Improving Machine Learning Performance with Synthetic Augmentation PAC-MCTS: Bias-Aware Pruning for Robust LLM-Guided Search and Planning Path-Sampled Integrated Gradients Heat and Matérn Kernels on Matchings Doubly Outlier-Robust Online Infinite Hidden Markov Model Momentum Further Constrains Sharpness at the Edge of Stochastic Stability Multistage Conditional Compositional Optimization BOAT: Navigating the Sea of In Silico Predictors for Antibody Design via Multi-Objective Bayesian Optimization Sandpile Economics: Theory, Identification, and Evidence Online learning with noisy side observations Spectral Thompson sampling Covariance-adapting algorithm for semi-bandits with application to sparse rewards Ordinary Least Squares is a Special Case of Transformer Metric-Aware Principal Component Analysis (MAPCA):A Unified Framework for Scale-Invariant Representation Learning Robust Low-Rank Tensor Completion based on M-product with Weighted Correlated Total Variation and Sparse Regularization Joint Representation Learning and Clustering via Gradient-Based Manifold Optimization Universality of Gaussian-Mixture Reverse Kernels in Conditional Diffusion Interpretable and Explainable Surrogate Modeling for Simulations: A State-of-the-Art Survey and Perspectives on Explainable AI for Decision-Making Estimating Continuous Treatment Effects with Two-Stage Kernel Ridge Regression A short proof of near-linear convergence of adaptive gradient descent under fourth-order growth and convexity Some Theoretical Limitations of t-SNE Bias-Corrected Adaptive Conformal Inference for Multi-Horizon Time Series Forecasting Identifiability of Potentially Degenerate Gaussian Mixture Models With Piecewise Affine Mixing Rare Event Analysis via Stochastic Optimal Control Adaptive Learning via Off-Model Training and Importance Sampling for Fully Non-Markovian Optimal Stochastic Control. Complete version Generalization Guarantees on Data-Driven Tuning of Gradient Descent with Langevin Updates Minimizing classical resources in variational measurement-based quantum computation for generative modeling Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers ADD for Multi-Bit Image Watermarking Beyond Fixed False Discovery Rates: Post-Hoc Conformal Selection with E-Variables Regional Explanations: Bridging Local and Global Variable Importance ShapShift: Explaining Model Prediction Shifts with Subgroup Conditional Shapley Values Cost-optimal Sequential Testing via Doubly Robust Q-learning Query Lower Bounds for Diffusion Sampling Tail-Aware Information-Theoretic Generalization for RLHF and SGLD Beyond Augmented-Action Surrogates for Multi-Expert Learning-to-Defer Hierarchical Kernel Transformer: Multi-Scale Attention with an Information-Theoretic Approximation Analysis Policy-Aware Design of Large-Scale Factorial Experiments Towards Verified and Targeted Explanations through Formal Methods Portfolio Optimization Proxies under Label Scarcity and Regime Shifts via Bayesian and Deterministic Students under Semi-Supervised Sandwich Training Spectral methods: crucial for machine learning, natural for quantum computers? The Devil Is in Gradient Entanglement: Energy-Aware Gradient Coordinator for Robust Generalized Category Discovery A Tutorial Review of Bayesian Optimization with Gaussian Processes to Accelerate Stationary Point Searches Certified and accurate computation of function space norms of deep neural networks Mini-Batch Covariance, Diffusion Limits, and Oracle Complexity in Stochastic Gradient Descent: A Sampling-Design Perspective Conformal Policy Control Diagnostics for Individual-Level Prediction Instability in Machine Learning for Healthcare Neural Networks With Dense Weights Are Not Universal Approximators Continuous-time reinforcement learning: ellipticity enables model-free value function approximation Scalable spatial point process models for forensic footwear analysis A Review of Diffusion-based Simulation-Based Inference: Foundations and Applications in Non-Ideal Data Scenarios Active Learning with Selective Time-Step Acquisition for PDEs Joint Score-Threshold Optimization for Interpretable Risk Assessment Revisiting Entropy Regularization: Adaptive Coefficient Unlocks Its Potential for LLM Reinforcement Learning Discrete Guidance Matching: Exact Guidance for Discrete Flow Matching PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems Online Distributionally Robust LLM Alignment via Regression to Relative Reward Heavy-Tailed Class-Conditional Priors for Long-Tailed Generative Modeling Random Walk Learning and the Pac-Man Attack Sequential Regression Learning with Randomized Algorithms Diagnosing and Improving Diffusion Models by Estimating the Optimal Loss Value Random Matrix Theory for Deep Learning: Beyond Eigenvalues of Linear Models Scalable Spatiotemporal Inference with Biased Scan Attention Transformer Neural Processes Towards AI-assisted Neutrino Flavor Theory Design Towards Reasonable Concept Bottleneck Models Practical estimation of the optimal classification error with soft labels and calibration Flow-based Generative Modeling of Potential Outcomes and Counterfactuals The Gaussian Latent Machine: Efficient Prior and Posterior Sampling for Inverse Problems Two-Dimensional Deep ReLU CNN Approximation for Korobov Functions: A Constructive Approach FSPO: Few-Shot Optimization of Synthetic Preferences Personalizes to Real Users Identifying Information from Observations with Uncertainty and Novelty A ghost mechanism: An analytical model of abrupt learning in recurrent networks A Multiparty Homomorphic Encryption Approach to Confidential Federated Kaplan Meier Survival Analysis Large Language Models for Market Research: A Data-augmentation Approach Transformer Neural Processes - Kernel Regression FIT-GNN: Faster Inference Time for GNNs that 'FIT' in Memory Using Coarsening Estimating Joint Interventional Distributions from Marginal Interventional Data Nonparametric Sparse Online Learning of the Koopman Operator
Improved Complexities for Stochastic Conditional Gradient Methods under Interpolation-like Conditions
Tesi Xiao, Krishnakumar Balasubramanian, Saeed Ghadimi · 2020-06-15 · via stat.ML updates on arXiv.org

We analyze stochastic conditional gradient methods for constrained optimization problems arising in over-parametrized machine learning. We show that one could leverage the interpolation-like conditions satisfied by such models to obtain improved oracle complexities. Specifically, when the objective function is convex, we show that the conditional gradient method requires $\mathcal{O}(ε^{-2})$ calls to the stochastic gradient oracle to find an $ε$-optimal solution. Furthermore, by including a gradient sliding step, we show that the number of calls reduces to $\mathcal{O}(ε^{-1.5})$.

此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。