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Out-of-Distribution

3 articles · Page 1 of 1

Aug 12, 2026 · Artificial Intelligence

Live #44: Multi‑Agent Automation of Rust Code Verification & 30× Storage Reduction for User Representations

This live session reviews four KDD 2026 papers that introduce a multi‑agent framework for industrial Rust code verification, a unified quantized tokenizer that cuts user representation storage by 30×, a query‑anchored LLM approach for scenario‑adaptive user modeling, and the HIVE ensemble method for efficient out‑of‑distribution generalization, each validated with extensive experiments and real‑world deployments.

Out-of-DistributionRustUser Representation

0 likes · 9 min read

Live #44: Multi‑Agent Automation of Rust Code Verification & 30× Storage Reduction for User Representations

AI Frontier Lectures

Jan 21, 2026 · Artificial Intelligence

Introducing ICONIC-444: A 3.1M Industrial Image Dataset Redefining OOD Detection

The article presents ICONIC-444, a 3.1‑million‑image, 444‑class industrial dataset designed for out‑of‑distribution (OOD) detection, explains its realistic acquisition process, hierarchical OOD categories, benchmark tasks, and evaluates 22 state‑of‑the‑art OOD methods, revealing how dataset characteristics influence algorithm performance.

AI safetyICONIC-444OOD detection

0 likes · 10 min read

Introducing ICONIC-444: A 3.1M Industrial Image Dataset Redefining OOD Detection

Alimama Tech

Sep 11, 2024 · Artificial Intelligence

A Generative Approach for Treatment Effect Estimation under Collider Bias: From an Out-of-Distribution Perspective

The paper introduces a coupled generative adversarial framework that merges biased observational with unbiased experimental data to create a bias‑free dataset for causal inference, enabling robust treatment‑effect estimation under collider bias from an out‑of‑distribution perspective, and demonstrates superior bias reduction on three public advertising datasets.

Causal InferenceGenerative Adversarial NetworksOut-of-Distribution

0 likes · 10 min read

A Generative Approach for Treatment Effect Estimation under Collider Bias: From an Out-of-Distribution Perspective