NeurIPS | BestHub

Alimama Tech

Dec 17, 2024 · Artificial Intelligence

Auto‑Bidding in Large‑Scale Auctions: The AIGB Paradigm and the AuctionNet Benchmark

At NeurIPS 2024, Alibaba's AliMama workshop introduced AIGB, a generative-model based auto-bidding solution, and released AuctionNet benchmark with billions of records; the competition attracted 1,861 participants across generative and uncertainty tracks, with winners earning prizes and internships, highlighting broader applications beyond advertising.

AuctionNetAuto-BiddingGenerative Models

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Auto‑Bidding in Large‑Scale Auctions: The AIGB Paradigm and the AuctionNet Benchmark

Kuaishou Tech

Dec 17, 2024 · Artificial Intelligence

NeurIPS 2024 Auto‑Bidding in Large‑Scale Auctions: Kuaishou Team Wins Both General and AIGB Tracks

The NeurIPS 2024 Auto‑Bidding competition attracted over 15,000 submissions and 1,500 teams, featuring two tracks—General and AI‑Generated Bidding—where Kuaishou’s commercial algorithm team secured first place in both by leveraging reinforcement‑learning‑based online exploration and a decision‑transformer‑driven generative approach, achieving more than a 5% lift in ad revenue.

Generative ModelsKuaishouNeurIPS

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NeurIPS 2024 Auto‑Bidding in Large‑Scale Auctions: Kuaishou Team Wins Both General and AIGB Tracks

Architecture Digest

Dec 5, 2024 · Artificial Intelligence

NeurIPS 2024 Best Paper Introduces Visual Autoregressive Modeling (VAR) for Image Generation

A recent NeurIPS 2024 best‑paper award highlights a novel Visual Autoregressive Modeling (VAR) approach that uses multi‑scale token prediction to improve image generation, while the surrounding article also mentions a free book giveaway and a legal dispute involving the paper's author.

Deep LearningNeurIPSVAR

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NeurIPS 2024 Best Paper Introduces Visual Autoregressive Modeling (VAR) for Image Generation

AntTech

Dec 2, 2024 · Artificial Intelligence

Ant Group’s Morse & ARCLab Wins Both Attack and Defense Tracks in NeurIPS 2024 LLM Privacy Challenge

Ant Group’s Morse & ARCLab team secured the champion title in the attack track and the best practical defense award in the LLM Privacy Challenge at NeurIPS 2024, showcasing cutting‑edge methods for extracting training data from large language models and protecting model privacy with data sanitization and differential privacy techniques.

AI securityLLM privacyNeurIPS

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Ant Group’s Morse & ARCLab Wins Both Attack and Defense Tracks in NeurIPS 2024 LLM Privacy Challenge

Alimama Tech

Jul 2, 2024 · Artificial Intelligence

NeurIPS 2024 Competition: Auto-Bidding in Large-Scale Auctions

The 2024 NeurIPS Competition, organized by Peking University's PAAI lab and Alibaba‑Mama, challenges teams to build auto‑bidding agents—using generative models for the AIGB Track or handling uncertainty for the General Track—to maximize ad performance in large‑scale auctions, with registration open until August 8 and prize pools up to $6,000 per track.

Auto-BiddingGenerative ModelsMachine Learning

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NeurIPS 2024 Competition: Auto-Bidding in Large-Scale Auctions

AntTech

Dec 14, 2023 · Artificial Intelligence

Highlights of Ant Group’s 20 Accepted Papers at NeurIPS 2023

The article summarizes Ant Group's twenty accepted NeurIPS 2023 papers, covering advances in generative AI, time‑series forecasting, 3D image synthesis, and other machine‑learning topics, and provides brief overviews of three highlighted works along with links to the remaining studies.

3D Image SynthesisAnt GroupGenerative AI

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Highlights of Ant Group’s 20 Accepted Papers at NeurIPS 2023

AntTech

Dec 11, 2023 · Artificial Intelligence

Ant Group Open-Sources OpenASCE: A Distributed Full-Stack Causal Learning System Presented at NeurIPS

At NeurIPS 2023, Ant Group unveiled OpenASCE, the industry's first open‑source distributed full‑link causal learning system, detailing its architecture, large‑scale capabilities, and real‑world applications in credit risk, marketing, and recommendation while emphasizing its role in advancing causal AI research.

Ant GroupNeurIPSOpen Source

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Ant Group Open-Sources OpenASCE: A Distributed Full-Stack Causal Learning System Presented at NeurIPS

AntTech

Oct 31, 2022 · Artificial Intelligence

Automated Attacker A² for Enhancing Model Robustness in Adversarial Training

The paper presents A², an automated, parameterized attacker that dynamically adjusts perturbation methods and step sizes during adversarial training, demonstrating improved robustness across multiple benchmarks with modest computational overhead, and outlines future directions for further efficiency and effectiveness in secure AI systems.

NeurIPSadversarial trainingautomated attacker

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Automated Attacker A² for Enhancing Model Robustness in Adversarial Training

AntTech

Sep 27, 2022 · Artificial Intelligence

Ant Group’s Research Institute Publishes Four NeurIPS 2022 Papers on Advanced Computer Vision and AI

Ant Group’s Ant Technology Research Institute had four papers from its Visual Intelligence Lab accepted at NeurIPS 2022, covering rank diminishing in deep networks, geometry‑aware 3D image synthesis, dynamic discriminators for GANs, and uncertainty‑aware hierarchical refinement for incremental classification, highlighting the institute’s cutting‑edge AI research.

AI researchDeep LearningGANs

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Ant Group’s Research Institute Publishes Four NeurIPS 2022 Papers on Advanced Computer Vision and AI

Alimama Tech

Sep 21, 2022 · Artificial Intelligence

Alibaba's Three Papers Accepted at NeurIPS 2022

Alibaba’s research team secured three NeurIPS 2022 papers—introducing an Adaptive Parameter Generation network that boosts click‑through rates and revenue, a tuning‑free Global Batch Gradient Aggregation method that speeds recommendation model training by 2.4×, and a Sustainable Online Reinforcement Learning framework that outperforms existing auto‑bidding strategies.

CTR predictionMachine LearningNeurIPS

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Alibaba's Three Papers Accepted at NeurIPS 2022

Beike Product & Technology

Dec 23, 2021 · Artificial Intelligence

KeSpeech: A Large-Scale Chinese Mandarin Dialect Speech Benchmark Presented at NeurIPS 2021

KeSpeech, a benchmark jointly released by Beike AI and Tsinghua University at NeurIPS 2021, provides a massive Chinese Mandarin dialect dataset covering 30,000 speakers from 34 cities, supporting speech recognition, speaker verification, dialect identification, and voice conversion tasks, and includes rich multi‑scenario and parallel corpora for advanced research.

NeurIPSSpeech Recognitionai

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KeSpeech: A Large-Scale Chinese Mandarin Dialect Speech Benchmark Presented at NeurIPS 2021

AntTech

Dec 16, 2021 · Artificial Intelligence

Robust AI: Ant Group’s Self‑Supervised Feature‑Compatible Model Wins NeurIPS ISC2021 Image Representation Competition

The Ant Group’s TitanShield Team secured the image representation track at NeurIPS ISC2021 using a self‑supervised, feature‑compatible pre‑training model that dramatically cuts labeling effort, speeds up training, and lowers image adversarial risk by 80%, highlighting AI robustness as a critical challenge for content‑security applications.

AI robustnessAnt GroupNeurIPS

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Robust AI: Ant Group’s Self‑Supervised Feature‑Compatible Model Wins NeurIPS ISC2021 Image Representation Competition

Kuaishou Tech

Dec 10, 2021 · Artificial Intelligence

Kuaishou and Tsinghua University Win NeurIPS'21 Billion-Scale ANN Challenge with FAISS‑Optimized KST_ANN Solution

On December 6, Kuaishou and Tsinghua University’s joint team secured first place in the NeurIPS'21 Billion‑Scale Approximate Nearest Neighbor Search Challenge by leveraging a FAISS‑optimized, memory‑efficient KST_ANN algorithm that achieved over 6% higher recall on multiple billion‑scale datasets, showcasing the practical impact of large‑scale vector retrieval in AI‑driven services.

ANNKST_ANNNeurIPS

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Kuaishou and Tsinghua University Win NeurIPS'21 Billion-Scale ANN Challenge with FAISS‑Optimized KST_ANN Solution

AntTech

Dec 13, 2019 · Artificial Intelligence

Ant Group Showcases AI Research at NeurIPS 2019: Deep Exponential Family and Graph Neural Network Innovations

At NeurIPS 2019 in Vancouver, Ant Group presented several award‑winning papers—including a deep exponential family distribution estimation method and a conditional graph logic network for retrosynthesis—while also demonstrating AI‑driven applications such as smart waste classification, underscoring its broad AI research and deployment efforts.

Deep LearningExponential FamilyGraph Neural Networks

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Ant Group Showcases AI Research at NeurIPS 2019: Deep Exponential Family and Graph Neural Network Innovations