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

博客园 - 聂微东
GbyAI
GbyAI
S
SegmentFault 最新的问题
H
Hackread – Cybersecurity News, Data Breaches, AI and More
V
Visual Studio Blog
WordPress大学
WordPress大学
Hugging Face - Blog
Hugging Face - Blog
B
Blog
宝玉的分享
宝玉的分享
Last Week in AI
Last Week in AI
雷峰网
雷峰网
爱范儿
爱范儿
Vercel News
Vercel News
人人都是产品经理
人人都是产品经理
U
Unit 42
Microsoft Azure Blog
Microsoft Azure Blog
Microsoft Security Blog
Microsoft Security Blog
Jina AI
Jina AI
P
Proofpoint News Feed
A
About on SuperTechFans
I
InfoQ
F
Fortinet All Blogs
L
LangChain Blog
T
Tailwind CSS Blog

math.ST updates on arXiv.org

What is Learnable in Valiant's Theory of the Learnable? Learning Perturbations to Extrapolate Your LLM Byzantine-Robust Distributed Sparse Learning Revisited The Sample Complexity of Multiple Change Point Identification under Bandit Feedback A proximal gradient algorithm for composite log-concave sampling Model-based Bootstrap of Controlled Markov Chains Approximation of Maximally Monotone Operators : A Graph Convergence Perspective Posterior Contraction Rates for Sparse Kolmogorov-Arnold Networks in Anisotropic Besov Spaces MIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound A Spectral Framework for Closed-Form Relative Density Estimation Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability Higher-Order Equilibrium Tracking for EM-Compressible Online Estimation Scaling Limits of Long-Context Transformers A Note on Non-Negative $L_1$-Approximating Polynomials Susceptibilities and Patterning: A Primer on Linear Response in Bayesian Learning Linear Response Estimators for Singular Statistical Models Statistical inference with belief functions: A survey Robust stochastic first order methods in heavy-tailed noise via medoid mini-batch gradient sampling Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity Adaptive auditing of AI systems with anytime-valid guarantees Locally Near Optimal Piecewise Linear Regression in High Dimensions via Difference of Max-Affine Functions Risk-Controlled Post-Processing of Decision Policies Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning A Unified Pair-GRPO Family: From Implicit to Explicit Preference Constraints for Stable and General RL Alignment Time-Inhomogeneous Preconditioned Langevin Dynamics A Fine-Grained Understanding of Uniform Convergence for Halfspaces CITE: Anytime-Valid Statistical Inference in LLM Self-Consistency Ratio-based Loss Functions Optimal Confidence Band for Kernel Gradient Flow Estimator A renormalization-group inspired lattice-based framework for piecewise generalized linear models
Rejoinder: "Gene Hunting with Hidden Markov Model Knockoffs"
Matteo Sesia, Chiara Sabatti, Emmanuel J. Candès · 2019-03-14 · via math.ST updates on arXiv.org

In this paper we deepen and enlarge the reflection on the possible advantages of a knockoff approach to genome wide association studies (Sesia et al., 2018), starting from the discussions in Bottolo & Richardson (2019); Jewell & Witten (2019); Rosenblatt et al. (2019) and Marchini (2019). The discussants bring up a number of important points, either related to the knockoffs methodology in general, or to its specific application to genetic studies. In the following we offer some clarifications, mention relevant recent developments and highlight some of the still open problems.