Flexible Modeling of Diversity with Strongly Log-Concave Distributions. - Joshua Robinson 001; Suvrit Sra; Stefanie Jegelka (NeurIPS, 2019)

Strength from Weakness: Fast Learning Using Weak Supervision. - Joshua Robinson 001; Stefanie Jegelka; Suvrit Sra (ICML, 2020)

Debiased Contrastive Learning. - Ching-Yao Chuang; Joshua Robinson 001; Yen-Chen Lin; Antonio Torralba 001; Stefanie Jegelka (NeurIPS, 2020)

Contrastive Learning with Hard Negative Samples. - Joshua David Robinson; Ching-Yao Chuang; Suvrit Sra; Stefanie Jegelka (ICLR, 2021)

Can contrastive learning avoid shortcut solutions? - Joshua Robinson 001; Li Sun 010; Ke Yu; Kayhan Batmanghelich; Stefanie Jegelka; Suvrit Sra (NeurIPS, 2021)

The First Learning on Graphs Conference: Preface. - Bastian Rieck; Razvan Pascanu; Yuanqi Du; Hannes Stärk; Derek Lim; Chaitanya K. Joshi; Andreea Deac; Iulia Duta; Joshua Robinson 001; Gabriele Corso; Leonardo Cotta; Yanqiao Zhu 001; et al. (LoG, 2022)

Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions. - Nikolaos Karalias; Joshua Robinson 001; Andreas Loukas; Stefanie Jegelka (NeurIPS, 2022)

A simple, efficient and scalable contrastive masked autoencoder for learning visual representations. - Shlok Mishra; Joshua Robinson 001; Huiwen Chang; David Jacobs 001; Aaron Sarna; Aaron Maschinot; Dilip Krishnan (CoRR, 2022)

Sign and Basis Invariant Networks for Spectral Graph Representation Learning. - Derek Lim; Joshua David Robinson; Lingxiao Zhao; Tess E. Smidt; Suvrit Sra; Haggai Maron; Stefanie Jegelka (ICLR, 2023)

The Second Learning on Graphs Conference: Preface. - Soledad Villar; Benjamin Paul Chamberlain; Yuanqi Du; Hannes Stärk; Chaitanya K. Joshi; Andreea Deac; Iulia Duta; Joshua Robinson 001; Yanqiao Zhu 001; Kexin Huang; Michelle M. Li; Sofia Bourhim; et al. (LoG, 2023)

Expressive Sign Equivariant Networks for Spectral Geometric Learning. - Derek Lim; Joshua Robinson 001; Stefanie Jegelka; Haggai Maron (NeurIPS, 2023)

On the Stability of Expressive Positional Encodings for Graph Neural Networks. - Yinan Huang; William Lu; Joshua Robinson 001; Yu Yang; Muhan Zhang; Stefanie Jegelka; Pan Li 005 (CoRR, 2023)

Relational Deep Learning: Graph Representation Learning on Relational Databases. - Matthias Fey; Weihua Hu; Kexin Huang; Jan Eric Lenssen; Rishabh Ranjan; Joshua Robinson 001; Rex Ying; Jiaxuan You; Jure Leskovec (CoRR, 2023)

Modeling the Geometry of Neural Network Representation Spaces - Joshua Robinson 001 (2023)

Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning. - Sharut Gupta; Joshua Robinson 001; Derek Lim; Soledad Villar; Stefanie Jegelka (ICLR, 2024)

On the Stability of Expressive Positional Encodings for Graphs. - Yinan Huang; William Lu; Joshua Robinson 001; Yu Yang; Muhan Zhang; Stefanie Jegelka; Pan Li 005 (ICLR, 2024)

Position: Relational Deep Learning - Graph Representation Learning on Relational Databases. - Matthias Fey; Weihua Hu; Kexin Huang; Jan Eric Lenssen; Rishabh Ranjan; Joshua Robinson 001; Rex Ying; Jiaxuan You; Jure Leskovec (ICML, 2024)

RelBench: A Benchmark for Deep Learning on Relational Databases. - Joshua Robinson 001; Rishabh Ranjan; Weihua Hu; Kexin Huang; Jiaqi Han; Alejandro Dobles; Matthias Fey; Jan Eric Lenssen; Yiwen Yuan; Zecheng Zhang; Xinwei He; Jure Leskovec (NeurIPS, 2024)

Holographic Node Representations: Pre-training Task-Agnostic Node Embeddings. - Beatrice Bevilacqua; Joshua Robinson 001; Jure Leskovec; Bruno Ribeiro 001 (ICLR, 2025)