New Study Establishes Near-Optimal Sample Complexity for Deterministic Agnostic PAC Learning

A new paper studies agnostic PAC learning under deterministic labels, where examples are labeled by an arbitrary deterministic function, and establishes near-optimal sample complexity for the setting.

Read the full story at arXiv stat.ML ↗

Timeline · 1 report

2026-11-11 05:00 · arXiv stat.ML
Near-Optimal Sample Complexity of Deterministic Agnostic PAC Learning

More stories

Nvidia CEO Jensen Huang faces spy scandal over alleged relationship with Chinese asset Alice Huang — New York Times AI
Anthropic targets $1.4 trillion valuation in IPO, trading expected to begin Nov. 19 — CNBC Technology
Google launches Gemini 4 Carbon across Copilot, API, and Ultra tiers — Google DeepMind Blog
Cognizant Launches Claude Managed Services With 25,000-Strong Frontier Capability Group — Economic Times Tech
OpenAI launches Businesses in ChatGPT in beta with claimable profiles and agents — OpenAI News
TSMC to invest up to $1 billion in Anthropic and OpenAI — Economic Times Tech
STATERA Library Adds Chapters 11-18 on Omni and Steer Drive Robotics — DEV Community — AI
Anthropic expands Claude into a workspace with Sheets, Whiteboards, and Meetings — Anthropic (X)

Get the daily brief of stories like this at 6:30 every morning →