Jure Leskovec

verfied profile

∙ 1 follower
Stanford University ∙ Pinterest, Inc.

Professor at Stanford University, Chief Scientist at Pinterest.

Featured Co-authors

Sergey Levine
240 publications
Percy Liang
104 publications
Anima Anandkumar
102 publications
C. -C. Jay Kuo
90 publications
Chelsea Finn
86 publications
Bin Wang
82 publications
Bo Liu
80 publications
Xiang Ren
75 publications
Christopher Ré
73 publications
Marco Pavone
63 publications
Bo Wang
58 publications

Model-Agnostic Graph Regularization for Few-Shot Learning

In many domains, relationships between categories are encoded in the kno...
02/14/2021 ∙ by Ethan Shen, et al. ∙ 15 ∙ share

read it
Driver2vec: Driver Identification from Automotive Data

With increasing focus on privacy protection, alternative methods to iden...
02/10/2021 ∙ by Jingbo Yang, et al. ∙ 62 ∙ share

read it
Open-World Semi-Supervised Learning

Supervised and semi-supervised learning methods have been traditionally ...
02/06/2021 ∙ by Kaidi Cao, et al. ∙ 3 ∙ share

read it
Identity-aware Graph Neural Networks

Message passing Graph Neural Networks (GNNs) provide a powerful modeling...
01/25/2021 ∙ by Jiaxuan You, et al. ∙ 9 ∙ share

read it
Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks

Temporal networks serve as abstractions of many real-world dynamic syste...
01/15/2021 ∙ by Yanbang Wang, et al. ∙ 6 ∙ share

read it
WILDS: A Benchmark of in-the-Wild Distribution Shifts

Distribution shifts can cause significant degradation in a broad range o...
12/14/2020 ∙ by Pang Wei Koh, et al. ∙ 10 ∙ share

read it
Design Space for Graph Neural Networks

The rapid evolution of Graph Neural Networks (GNNs) has led to a growing...
11/17/2020 ∙ by Jiaxuan You, et al. ∙ 24 ∙ share

read it
Coresets for Robust Training of Neural Networks against Noisy Labels

Modern neural networks have the capacity to overfit noisy labels frequen...
11/15/2020 ∙ by Baharan Mirzasoleiman, et al. ∙ 3 ∙ share

read it
F-FADE: Frequency Factorization for Anomaly Detection in Edge Streams

Edge streams are commonly used to capture interactions in dynamic networ...
11/09/2020 ∙ by Yen-Yu Chang, et al. ∙ 8 ∙ share

read it
Handling Missing Data with Graph Representation Learning

Machine learning with missing data has been approached in two different ...
10/30/2020 ∙ by Jiaxuan You, et al. ∙ 12 ∙ share

read it
Graph Information Bottleneck

Representation learning of graph-structured data is challenging because ...
10/24/2020 ∙ by Tailin Wu, et al. ∙ 20 ∙ share

read it
Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs

One of the fundamental problems in Artificial Intelligence is to perform...
10/22/2020 ∙ by Hongyu Ren, et al. ∙ 23 ∙ share

read it
Direct Multi-hop Attention based Graph Neural Network

Introducing self-attention mechanism in graph neural networks (GNNs) ach...
09/29/2020 ∙ by Guangtao Wang, et al. ∙ 13 ∙ share

read it
Inductive Learning on Commonsense Knowledge Graph Completion

Commonsense knowledge graph (CKG) is a special type of knowledge graph (...
09/19/2020 ∙ by Bin Wang, et al. ∙ 29 ∙ share

read it
Distance Encoding – Design Provably More Powerful Graph Neural Networks for Structural Representation Learning

Learning structural representations of node sets from graph-structured d...
08/31/2020 ∙ by Pan Li, et al. ∙ 42 ∙ share

read it
OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation

Real-world tasks often exhibit a compositional structure that contains a...
08/17/2020 ∙ by Hongyu Ren, et al. ∙ 1 ∙ share

read it
Concept Learners for Generalizable Few-Shot Learning

Developing algorithms that are able to generalize to a novel task given ...
07/14/2020 ∙ by Kaidi Cao, et al. ∙ 9 ∙ share

read it
Graph Structure of Neural Networks

Neural networks are often represented as graphs of connections between n...
07/13/2020 ∙ by Jiaxuan You, et al. ∙ 23 ∙ share

read it
PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest

Latent user representations are widely adopted in the tech industry for ...
07/07/2020 ∙ by Aditya Pal, et al. ∙ 7 ∙ share

read it
Neural Subgraph Matching

Subgraph matching is the problem of determining the presence and locatio...
07/06/2020 ∙ by Zhaoyu Lou, et al. ∙ 6 ∙ share

read it
Improving Query Safety at Pinterest

Query recommendations in search engines is a double edged sword, with un...
06/20/2020 ∙ by Abhijit Mahabal, et al. ∙ 0 ∙ share

read it
M2P2: Multimodal Persuasion Prediction using Adaptive Fusion

Identifying persuasive speakers in an adversarial environment is a criti...
06/03/2020 ∙ by Chongyang Bai, et al. ∙ 0 ∙ share

read it
Open Graph Benchmark: Datasets for Machine Learning on Graphs

We present the Open Graph Benchmark (OGB), a diverse set of challenging ...
05/02/2020 ∙ by Weihua Hu, et al. ∙ 63 ∙ share

read it
Learning to Simulate Complex Physics with Graph Networks

Here we present a general framework for learning simulation, and provide...
02/21/2020 ∙ by Alvaro Sanchez-Gonzalez, et al. ∙ 26 ∙ share

read it
Entity Context and Relational Paths for Knowledge Graph Completion

Knowledge graph completion aims to predict missing relations between ent...
02/17/2020 ∙ by Hongwei Wang, et al. ∙ 7 ∙ share

read it
Unifying Graph Convolutional Neural Networks and Label Propagation

Label Propagation (LPA) and Graph Convolutional Neural Networks (GCN) ar...
02/17/2020 ∙ by Hongwei Wang, et al. ∙ 9 ∙ share

read it
Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings

Answering complex logical queries on large-scale incomplete knowledge gr...
02/14/2020 ∙ by Hongyu Ren, et al. ∙ 11 ∙ share

read it
G2SAT: Learning to Generate SAT Formulas

The Boolean Satisfiability (SAT) problem is the canonical NP-complete pr...
10/29/2019 ∙ by Jiaxuan You, et al. ∙ 6 ∙ share

read it
Hyperbolic Graph Convolutional Neural Networks

Graph convolutional neural networks (GCNs) embed nodes in a graph into E...
10/28/2019 ∙ by Ines Chami, et al. ∙ 23 ∙ share

read it
Improving Graph Attention Networks with Large Margin-based Constraints

Graph Attention Networks (GATs) are the state-of-the-art neural architec...
10/25/2019 ∙ by Guangtao Wang, et al. ∙ 18 ∙ share

read it
Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks

Modeling sequential interactions between users and items/products is cru...
08/03/2019 ∙ by Srijan Kumar, et al. ∙ 0 ∙ share

read it
Selection Via Proxy: Efficient Data Selection For Deep Learning

Data selection methods such as active learning and core-set selection ar...
06/26/2019 ∙ by Cody Coleman, et al. ∙ 12 ∙ share

read it
Position-aware Graph Neural Networks

Learning node embeddings that capture a node's position within the broad...
06/11/2019 ∙ by Jiaxuan You, et al. ∙ 2 ∙ share

read it
Redundancy-Free Computation Graphs for Graph Neural Networks

Graph Neural Networks (GNNs) are based on repeated aggregations of infor...
06/09/2019 ∙ by Zhihao Jia, et al. ∙ 1 ∙ share

read it
Data Sketching for Faster Training of Machine Learning Models

Many machine learning problems reduce to the problem of minimizing an ex...
06/05/2019 ∙ by Baharan Mirzasoleiman, et al. ∙ 8 ∙ share

read it
Pre-training Graph Neural Networks

Many applications of machine learning in science and medicine, including...
05/29/2019 ∙ by Weihua Hu, et al. ∙ 23 ∙ share

read it
Knowledge Graph Convolutional Networks for Recommender Systems with Label Smoothness Regularization

Knowledge graphs capture interlinked information between entities and th...
05/11/2019 ∙ by Hongwei Wang, et al. ∙ 9 ∙ share

read it
Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems

Knowledge graphs capture structured information and relations between a ...
05/11/2019 ∙ by Hongwei Wang, et al. ∙ 0 ∙ share

read it
Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommendation

Knowledge graphs capture structured information and relations between a ...
05/11/2019 ∙ by Hongwei Wang, et al. ∙ 0 ∙ share

read it
Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems

Recommender systems that can learn from cross-session data to dynamicall...
04/08/2019 ∙ by Jiaxuan You, et al. ∙ 0 ∙ share

read it
Goal-setting And Achievement In Activity Tracking Apps: A Case Study Of MyFitnessPal

Activity tracking apps often make use of goals as one of their core moti...
04/04/2019 ∙ by Mitchell L. Gordon, et al. ∙ 0 ∙ share

read it
GNN Explainer: A Tool for Post-hoc Explanation of Graph Neural Networks

Graph Neural Networks (GNNs) are a powerful tool for machine learning on...
03/10/2019 ∙ by Rex Ying, et al. ∙ 10 ∙ share

read it
Learning Dynamic Embeddings from Temporal Interactions

Modeling a sequence of interactions between users and items (e.g., produ...
12/06/2018 ∙ by Srijan Kumar, et al. ∙ 8 ∙ share

read it
Predicting pregnancy using large-scale data from a women's health tracking mobile application

Predicting pregnancy has been a fundamental problem in women's health fo...
12/05/2018 ∙ by Bo Liu, et al. ∙ 0 ∙ share

read it
Complete the Look: Scene-based Complementary Product Recommendation

Modeling fashion compatibility is challenging due to its complexity and ...
12/04/2018 ∙ by Wang-Cheng Kang, et al. ∙ 16 ∙ share

read it
Offline Behaviors of Online Friends

In this work we analyze traces of mobility and co-location among a group...
11/07/2018 ∙ by Piotr Sapiezynski, et al. ∙ 0 ∙ share

read it
How Powerful are Graph Neural Networks?

Graph Neural Networks (GNNs) for representation learning of graphs broad...
10/01/2018 ∙ by Keyulu Xu, et al. ∙ 13 ∙ share

read it
CASC: Context-Aware Segmentation and Clustering for Motif Discovery in Noisy Time Series Data

Complex systems, such as airplanes, cars, or financial markets, produce ...
09/06/2018 ∙ by Saachi Jain, et al. ∙ 0 ∙ share

read it
Inferring Multi-Dimensional Rates of Aging from Cross-Sectional Data

Modeling how individuals evolve over time is a fundamental problem in th...
07/12/2018 ∙ by Emma Pierson, et al. ∙ 4 ∙ share

read it
Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities

New technologies have enabled the investigation of biology and human hea...
06/30/2018 ∙ by Marinka Zitnik, et al. ∙ 0 ∙ share

read it