Robust Deep Reinforcement Learning through Adversarial Loss
- Tuomas Oikarinen
- Wang Zhang
- et al.
- 2021
- NeurIPS 2021
Publications
When does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?
- Lijie Fan
- Sijia Liu
- et al.
- 2021
- NeurIPS 2021
A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics
- Kai Xu
- Akash Srivastava
- et al.
- 2021
- NeurIPS 2021
Catastrophic Data Leakage in Vertical Federated Learning
- Xiao Jin
- Pin-Yu Chen
- et al.
- 2021
- NeurIPS 2021
Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language
- Mingyu Ding
- Zhenfang Chen
- et al.
- 2021
- NeurIPS 2021
Learning with Algorithmic Supervision via Continuous Relaxations
- Felix Petersen
- Christian Borgelt
- et al.
- 2021
- NeurIPS 2021
Targeted Neural Dynamical Modeling
- Cole Hurwitz
- Akash Srivastava
- et al.
- 2021
- NeurIPS 2021
Safe Policy Optimization with Local Generalized Linear Function Approximations
- Akifumi Wachi
- Yunyue Wei
- et al.
- 2021
- NeurIPS 2021
Capacity and Bias of Learned Geometric Embeddings for Directed Graphs
- Michael Boratko
- Dongxu Zhang
- et al.
- 2021
- NeurIPS 2021
CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks
- Ruchir Puri
- David Kung
- et al.
- 2021
- NeurIPS 2021