High-Dimensional Gaussian Process Bandits. - Josip Djolonga; Andreas Krause 001; Volkan Cevher (NIPS, 2013)

From MAP to Marginals: Variational Inference in Bayesian Submodular Models. - Josip Djolonga; Andreas Krause 001 (NIPS, 2014)

Higher-Order Inference for Multi-class Log-Supermodular Models. - Jian Zhang 049; Josip Djolonga; Andreas Krause 001 (ICCV, 2015)

Scalable Variational Inference in Log-supermodular Models. - Josip Djolonga; Andreas Krause 001 (ICML, 2015)

Learning Probabilistic Submodular Diversity Models Via Noise Contrastive Estimation. - Sebastian Tschiatschek; Josip Djolonga; Andreas Krause 001 (AISTATS, 2016)

Cooperative Graphical Models. - Josip Djolonga; Stefanie Jegelka; Sebastian Tschiatschek; Andreas Krause 001 (NIPS, 2016)

Variational Inference in Mixed Probabilistic Submodular Models. - Josip Djolonga; Sebastian Tschiatschek; Andreas Krause 001 (NIPS, 2016)

Differentiable Learning of Submodular Functions. - Josip Djolonga; Andreas Krause 001 (NIPS, 2017)

Improving Optimization-Based Approximate Inference by Clamping Variables. - Junyao Zhao 001; Josip Djolonga; Sebastian Tschiatschek; Andreas Krause 001 (UAI, 2017)

Learning Implicit Generative Models Using Differentiable Graph Tests. - Josip Djolonga; Andreas Krause 001 (CoRR, 2017)

Provable Variational Inference for Constrained Log-Submodular Models. - Josip Djolonga; Stefanie Jegelka; Andreas Krause 001 (NeurIPS, 2018)

Practical and Consistent Estimation of f-Divergences. - Paul K. Rubenstein; Olivier Bousquet; Josip Djolonga; Carlos Riquelme; Ilya O. Tolstikhin (NeurIPS, 2019)

Evaluating Generative Models Using Divergence Frontiers. - Josip Djolonga; Mario Lucic; Marco Cuturi; Olivier Bachem; Olivier Bousquet; Sylvain Gelly (CoRR, 2019)

The Visual Task Adaptation Benchmark. - Xiaohua Zhai; Joan Puigcerver; Alexander Kolesnikov 003; Pierre Ruyssen; Carlos Riquelme; Mario Lucic; Josip Djolonga; André Susano Pinto; Maxim Neumann; Alexey Dosovitskiy; Lucas Beyer; Olivier Bachem; et al. (CoRR, 2019)

Precision-Recall Curves Using Information Divergence Frontiers. - Josip Djolonga; Mario Lucic; Marco Cuturi; Olivier Bachem; Olivier Bousquet; Sylvain Gelly (AISTATS, 2020)

Self-Supervised Learning of Video-Induced Visual Invariances. - Michael Tschannen; Josip Djolonga; Marvin Ritter; Aravindh Mahendran; Neil Houlsby; Sylvain Gelly; Mario Lucic (CVPR, 2020)

You Only Train Once: Loss-Conditional Training of Deep Networks. - Alexey Dosovitskiy; Josip Djolonga (ICLR, 2020)

On Mutual Information Maximization for Representation Learning. - Michael Tschannen; Josip Djolonga; Paul K. Rubenstein; Sylvain Gelly; Mario Lucic (ICLR, 2020)

Fast Differentiable Sorting and Ranking. - Mathieu Blondel; Olivier Teboul; Quentin Berthet; Josip Djolonga (ICML, 2020)

On Robustness and Transferability of Convolutional Neural Networks. - Josip Djolonga; Jessica Yung; Michael Tschannen; Rob Romijnders; Lucas Beyer; Alexander Kolesnikov 003; Joan Puigcerver; Matthias Minderer; Alexander D'Amour; Dan Moldovan; Sylvain Gelly; Neil Houlsby; et al. (CVPR, 2021)

Revisiting the Calibration of Modern Neural Networks. - Matthias Minderer; Josip Djolonga; Rob Romijnders; Frances Hubis; Xiaohua Zhai; Neil Houlsby; Dustin Tran; Mario Lucic (NeurIPS, 2021)

Representation learning from videos in-the-wild: An object-centric approach. - Rob Romijnders; Aravindh Mahendran; Michael Tschannen; Josip Djolonga; Marvin Ritter; Neil Houlsby; Mario Lucic (WACV, 2021)

SI-Score: An image dataset for fine-grained analysis of robustness to object location, rotation and size. - Jessica Yung; Rob Romijnders; Alexander Kolesnikov 003; Lucas Beyer; Josip Djolonga; Neil Houlsby; Sylvain Gelly; Mario Lucic; Xiaohua Zhai (CoRR, 2021)

Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. - Zachary Nado; Neil Band; Mark Collier; Josip Djolonga; Michael W. Dusenberry; Sebastian Farquhar; Angelos Filos; Marton Havasi; Rodolphe Jenatton; Ghassen Jerfel; Jeremiah Z. Liu; Zelda Mariet; et al. (CoRR, 2021)

Beyond Transfer Learning: Co-finetuning for Action Localisation. - Anurag Arnab; Xuehan Xiong; Alexey A. Gritsenko; Rob Romijnders; Josip Djolonga; Mostafa Dehghani 001; Chen Sun 002; Mario Lucic; Cordelia Schmid (CoRR, 2022)

Scaling Vision Transformers to 22 Billion Parameters. - Mostafa Dehghani 001; Josip Djolonga; Basil Mustafa; Piotr Padlewski; Jonathan Heek; Justin Gilmer; Andreas Peter Steiner; Mathilde Caron; Robert Geirhos; Ibrahim Alabdulmohsin; Rodolphe Jenatton; Lucas Beyer; et al. (ICML, 2023)

Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective. - Michael Eli Sander; Joan Puigcerver; Josip Djolonga; Gabriel Peyré; Mathieu Blondel (ICML, 2023)

Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution. - Mostafa Dehghani 001; Basil Mustafa; Josip Djolonga; Jonathan Heek; Matthias Minderer; Mathilde Caron; Andreas Steiner 001; Joan Puigcerver; Robert Geirhos; Ibrahim M. Alabdulmohsin; Avital Oliver; Piotr Padlewski; et al. (NeurIPS, 2023)

PaLI-X: On Scaling up a Multilingual Vision and Language Model. - Xi Chen 071; Josip Djolonga; Piotr Padlewski; Basil Mustafa; Soravit Changpinyo; Jialin Wu; Carlos Riquelme Ruiz; Sebastian Goodman; Xiao Wang 038; Yi Tay; Siamak Shakeri; Mostafa Dehghani 001; et al. (CoRR, 2023)

On Scaling Up a Multilingual Vision and Language Model. - Xi Chen 071; Josip Djolonga; Piotr Padlewski; Basil Mustafa; Soravit Changpinyo; Jialin Wu; Carlos Riquelme Ruiz; Sebastian Goodman; Xiao Wang 038; Yi Tay; Siamak Shakeri; Mostafa Dehghani 001; et al. (CVPR, 2024)

End-to-End Spatio-Temporal Action Localisation with Video Transformers. - Alexey A. Gritsenko; Xuehan Xiong; Josip Djolonga; Mostafa Dehghani 001; Chen Sun 002; Mario Lucic; Cordelia Schmid; Anurag Arnab (CVPR, 2024)