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counterfactual learning

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Alibaba International Intelligent Technology

Apr 24, 2026 · Artificial Intelligence

Alibaba International AI Team Lands Multiple Papers at SIGIR, WWW, and WSDM 2026

Alibaba International Intelligent Technology showcases nine industrial‑grade recommendation and search papers accepted at SIGIR, WWW, and WSDM 2026, detailing sparse scaling, counterfactual multi‑task learning, generative recommendation, MoE routing, and multimodal semantic ID breakthroughs with extensive offline and online results.

Generative RecommendationLarge Language ModelsMixture of Experts

0 likes · 24 min read

Alibaba International AI Team Lands Multiple Papers at SIGIR, WWW, and WSDM 2026

DataFunTalk

Jun 1, 2023 · Artificial Intelligence

Counterfactual Causal Inference for Credit‑Limit Modeling (Mono‑CFR)

This article presents a comprehensive overview of causal inference paradigms, the evolution of uplift and representation‑learning frameworks, and introduces the Mono‑CFR counterfactual credit‑limit model that estimates treatment effects for continuous credit limits using observational data while addressing confounding factors.

AIcausal inferencecounterfactual learning

0 likes · 14 min read

Counterfactual Causal Inference for Credit‑Limit Modeling (Mono‑CFR)

Meituan Technology Team

Aug 18, 2022 · Artificial Intelligence

Highlights of Meituan’s KDD 2022 Papers

This article presents concise introductions and download links for seven Meituan research papers accepted at KDD 2022, covering knowledge‑graph pre‑training, automatic feature and architecture selection for pre‑ranking, user intent discovery, persuasion factor modeling, counterfactual policy learning for top‑K recommendation, probabilistic forecasting of food preparation time, and a multi‑stage bonus allocation framework for meal delivery.

KDD 2022Knowledge GraphProbabilistic Forecasting

0 likes · 15 min read

AntTech

Feb 13, 2020 · Artificial Intelligence

Ant Group’s Six AI Papers Presented at AAAI 2020

At AAAI 2020, Ant Group showcased six peer‑reviewed papers covering dynamic network pruning, pruning without pre‑training, video‑based auto‑insurance damage assessment, long‑short‑term sample distillation, a span‑based neural buffer for long‑range context, and cost‑effective counterfactual incentive allocation, highlighting the company’s active research in artificial intelligence.

AAAI 2020Ant Groupcounterfactual learning

0 likes · 10 min read

Ant Group’s Six AI Papers Presented at AAAI 2020