Media Summary: Sang-Hong Kim, Kookmin University How can we design a Shiqi Zhang, National University of Singapore. Tiantian Feng, University of Southern California Federated Learning is a popular privacy-enhancing learning algorithm that ...

Kdd 2023 Efficient Distributed Approximate - Detailed Analysis & Overview

Sang-Hong Kim, Kookmin University How can we design a Shiqi Zhang, National University of Singapore. Tiantian Feng, University of Southern California Federated Learning is a popular privacy-enhancing learning algorithm that ... Yunjia Xi, Shanghai Jiao Tong University. Kunal Dahiya, IIT Delhi Large language models or encoders are widely used in real-world search and recommendation ... Shibal Ibrahim, Massachusetts Institute of Technology Sparse Mixture-of-Experts (Sparse-MoE) framework

Tsuyoshi "Ide-san" Ide, IBM Research, T. J. Watson Research Center. Optimal Quantile Approximation in Streams Thomas M. McDonald, University of Manchester Across many platforms, recommender systems are increasingly being explicitly ... William Shiao, University of California, Riverside. Zhuoran Ji, Cho-Li Wang Session 3: Graph Processing. Yeping Hu, Lawrence Livermore National Laboratory Dynamic systems, encompassing everything from chaotic systems to ...

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KDD 2023 - Efficient Distributed Approximate k-Nearest Neighbor Graph Construction
KDD 2023 - Efficient Approximation Algorithms for Spanning Centrality
KDD 2023 - Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering
KDD 2023 - FedMultimodal: A Benchmark for Multimodal Federated Learning
KDD 2023 - How to robustly detect failures with 3 types of telemetry data?
KDD 2023 - On-device Integrated Re-ranking with Heterogeneous Behavior Modeling
KDD 2023 - Domain-Specific Risk Minimization for Domain Generalization
KDD 2023 - Deep Encoders with Auxiliary Parameters for Extreme Classification
KDD 2023 - Learning Cardinality Constrained Mixture of Experts with Trees and Local Search
KDD 2023 - Generative Perturbation Analysis for Probabilistic Black Box Anomaly Attribution
Optimal Quantile Approximation in Streams
KDD 2023 - Generative Flow Network for Listwise Recommendation
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KDD 2023 - Efficient Distributed Approximate k-Nearest Neighbor Graph Construction

KDD 2023 - Efficient Distributed Approximate k-Nearest Neighbor Graph Construction

Sang-Hong Kim, Kookmin University How can we design a

KDD 2023 - Efficient Approximation Algorithms for Spanning Centrality

KDD 2023 - Efficient Approximation Algorithms for Spanning Centrality

Shiqi Zhang, National University of Singapore.

Sponsored
KDD 2023 - Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering

KDD 2023 - Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering

Yan Wen, Tsinghua University.

KDD 2023 - FedMultimodal: A Benchmark for Multimodal Federated Learning

KDD 2023 - FedMultimodal: A Benchmark for Multimodal Federated Learning

Tiantian Feng, University of Southern California Federated Learning is a popular privacy-enhancing learning algorithm that ...

KDD 2023 - How to robustly detect failures with 3 types of telemetry data?

KDD 2023 - How to robustly detect failures with 3 types of telemetry data?

Chenyu Zhao, Nankai University.

Sponsored
KDD 2023 - On-device Integrated Re-ranking with Heterogeneous Behavior Modeling

KDD 2023 - On-device Integrated Re-ranking with Heterogeneous Behavior Modeling

Yunjia Xi, Shanghai Jiao Tong University.

KDD 2023 - Domain-Specific Risk Minimization for Domain Generalization

KDD 2023 - Domain-Specific Risk Minimization for Domain Generalization

Yi-fan Zhang, Institute of Automation.

KDD 2023 - Deep Encoders with Auxiliary Parameters for Extreme Classification

KDD 2023 - Deep Encoders with Auxiliary Parameters for Extreme Classification

Kunal Dahiya, IIT Delhi Large language models or encoders are widely used in real-world search and recommendation ...

KDD 2023 - Learning Cardinality Constrained Mixture of Experts with Trees and Local Search

KDD 2023 - Learning Cardinality Constrained Mixture of Experts with Trees and Local Search

Shibal Ibrahim, Massachusetts Institute of Technology Sparse Mixture-of-Experts (Sparse-MoE) framework

KDD 2023 - Generative Perturbation Analysis for Probabilistic Black Box Anomaly Attribution

KDD 2023 - Generative Perturbation Analysis for Probabilistic Black Box Anomaly Attribution

Tsuyoshi "Ide-san" Ide, IBM Research, T. J. Watson Research Center.

Optimal Quantile Approximation in Streams

Optimal Quantile Approximation in Streams

Optimal Quantile Approximation in Streams

KDD 2023 - Generative Flow Network for Listwise Recommendation

KDD 2023 - Generative Flow Network for Listwise Recommendation

Shuchang Liu, Kuaishou Technology.

KDD 2023 - Density-Connectivity Distance unifies DBSCAN, k-Center and Spectral Clustering

KDD 2023 - Density-Connectivity Distance unifies DBSCAN, k-Center and Spectral Clustering

Anna Beer, Aarhus University.

KDD 2023 - Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay

KDD 2023 - Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay

Thomas M. McDonald, University of Manchester Across many platforms, recommender systems are increasingly being explicitly ...

KDD 2023 - Clustering-Accelerated Representation Learning on Graphs

KDD 2023 - Clustering-Accelerated Representation Learning on Graphs

William Shiao, University of California, Riverside.

Brief Announcement: Efficient Distributed Algorithms for the K-Nearest Neighbors Problem

Brief Announcement: Efficient Distributed Algorithms for the K-Nearest Neighbors Problem

Brief Announcement:

KDD 2023 - Rank-heterogeneous Preference Models for School Choice

KDD 2023 - Rank-heterogeneous Preference Models for School Choice

Amel Awadelkarim, Stanford University.

KDD 2023 - Efficient Bi-Level Optimization for Recommendation Denoising

KDD 2023 - Efficient Bi-Level Optimization for Recommendation Denoising

Zongwei Wang, University of Chongqing.

Efficient Exact K-Nearest Neighbor Graph Construction for Billion-Scale Datasets on GPUs TensorCores

Efficient Exact K-Nearest Neighbor Graph Construction for Billion-Scale Datasets on GPUs TensorCores

Zhuoran Ji, Cho-Li Wang Session 3: Graph Processing.

KDD 2023 - Graph Learning in Physical-informed Mesh-reduced Space for Real-world Dynamic Systems

KDD 2023 - Graph Learning in Physical-informed Mesh-reduced Space for Real-world Dynamic Systems

Yeping Hu, Lawrence Livermore National Laboratory Dynamic systems, encompassing everything from chaotic systems to ...

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