Ding, Nan

19 publications

ICLR 2024 CausalLM Is Not Optimal for In-Context Learning Nan Ding, Tomer Levinboim, Jialin Wu, Sebastian Goodman, Radu Soricut

CoRL 2024 One Model to Drift Them All: Physics-Informed Conditional Diffusion Model for Driving at the Limits Franck Djeumou, Thomas Jonathan Lew, Nan Ding, Michael Thompson, Makoto Suminaka, Marcus Greiff, John Subosits

CVPR 2023 Improving Robust Generalization by Direct PAC-Bayesian Bound Minimization Zifan Wang, Nan Ding, Tomer Levinboim, Xi Chen, Radu Soricut

ICLR 2023 PaLI: A Jointly-Scaled Multilingual Language-Image Model Xi Chen, Xiao Wang, Soravit Changpinyo, Aj Piergiovanni, Piotr Padlewski, Daniel Salz, Sebastian Goodman, Adam Grycner, Basil Mustafa, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Nan Ding, Keran Rong, Hassan Akbari, Gaurav Mishra, Linting Xue, Ashish V Thapliyal, James Bradbury, Weicheng Kuo, Mojtaba Seyedhosseini, Chao Jia, Burcu Karagol Ayan, Carlos Riquelme Ruiz, Andreas Peter Steiner, Anelia Angelova, Xiaohua Zhai, Neil Houlsby, Radu Soricut

ECCV 2022 PACTran: PAC-Bayesian Metrics for Estimating the Transferability of Pretrained Models to Classification Tasks Nan Ding, Xi Chen, Tomer Levinboim, Soravit Changpinyo, Radu Soricut

NeurIPS 2021 Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning Nan Ding, Xi Chen, Tomer Levinboim, Sebastian Goodman, Radu Soricut

CVPR 2021 Conceptual 12m: Pushing Web-Scale Image-Text Pre-Training to Recognize Long-Tail Visual Concepts Soravit Changpinyo, Piyush Sharma, Nan Ding, Radu Soricut

NeurIPS 2017 Cold-Start Reinforcement Learning with SoftMax Policy Gradient Nan Ding, Radu Soricut

NeurIPS 2016 Stochastic Gradient MCMC with Stale Gradients Changyou Chen, Nan Ding, Chunyuan Li, Yizhe Zhang, Lawrence Carin

NeurIPS 2015 Embedding Inference for Structured Multilabel Prediction Farzaneh Mirzazadeh, Siamak Ravanbakhsh, Nan Ding, Dale Schuurmans

NeurIPS 2015 On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators Changyou Chen, Nan Ding, Lawrence Carin

ICCV 2015 Probabilistic Label Relation Graphs with Ising Models Nan Ding, Jia Deng, Kevin P. Murphy, Hartmut Neven

NeurIPS 2014 Bayesian Sampling Using Stochastic Gradient Thermostats Nan Ding, Youhan Fang, Ryan Babbush, Changyou Chen, Robert D Skeel, Hartmut Neven

ECCV 2014 Large-Scale Object Classification Using Label Relation Graphs Jia Deng, Nan Ding, Yangqing Jia, Andrea Frome, Kevin Murphy, Samy Bengio, Yuan Li, Hartmut Neven, Hartwig Adam

ICML 2012 Dependent Hierarchical Normalized Random Measures for Dynamic Topic Modeling Changyou Chen, Nan Ding, Wray L. Buntine

ICML 2012 Robust Classification with Adiabatic Quantum Optimization Vasil S. Denchev, Nan Ding, S. V. N. Vishwanathan, Hartmut Neven

NeurIPS 2011 T-Divergence Based Approximate Inference Nan Ding, Yuan Qi, S.v.n. Vishwanathan

AISTATS 2010 Nonparametric Bayesian Matrix Factorization by Power-EP Nan Ding, Yuan Qi, Rongjing Xiang, Ian Molloy, Ninghui Li

NeurIPS 2010 T-Logistic Regression Nan Ding, S.v.n. Vishwanathan