Papers
Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph
Lu Liu, Tianyi Zhou, Guodong Long et al.
Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of Polytopes
Matt Jordan, Justin Lewis, Alexandros G Dimakis
Provable Guarantees for Gradient-Based Meta-Learning
Maria-Florina Balcan, Mikhail Khodak, Ameet Talwalkar
Provable Non-linear Inductive Matrix Completion
Kai Zhong, Zhao Song, Prateek Jain et al.
Provable Robustness of ReLU networks via Maximization of Linear Regions
Francesco Croce, Maksym Andriushchenko, Matthias Hein
Provably Accurate Double-Sparse Coding
Thanh V. Nguyen, Raymond K. W. Wong, Chinmay Hegde
Provably Efficient Imitation Learning from Observation Alone
Wen Sun, Anirudh Vemula, Byron Boots et al.
Provably Efficient Maximum Entropy Exploration
Elad Hazan, Sham Kakade, Karan Singh et al.
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle
Simon S Du, Yuping Luo, Ruosong Wang et al.
Provably Efficient Q-Learning with Low Switching Cost
Yu Bai, Tengyang Xie, Nan Jiang et al.
Provably efficient RL with Rich Observations via Latent State Decoding
Simon Du, Akshay Krishnamurthy, Nan Jiang et al.
Provably Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost
Zhuoran Yang, Yongxin Chen, Mingyi Hong et al.
Provably Powerful Graph Networks
Haggai Maron, Heli Ben-Hamu, Hadar Serviansky et al.
Provably Robust Blackbox Optimization for Reinforcement Learning
Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder et al.
Provably robust boosted decision stumps and trees against adversarial attacks
Maksym Andriushchenko, Matthias Hein
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers
Hadi Salman, Jerry Li, Ilya Razenshteyn et al.
PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach
Lily Weng, Pin-Yu Chen, Lam Nguyen et al.
Proximal Distance Algorithms: Theory and Practice
Kevin L. Keys, Hua Zhou, Kenneth Lange
Proximal Mean-Field for Neural Network Quantization
Thalaiyasingam Ajanthan, Puneet K. Dokania, Richard Hartley et al.
Proximal Splitting Meets Variance Reduction
Fabian Pedregosa, Kilian Fatras, Mattia Casotto
Proximity Queries for Absolutely Continuous Parametric Curves
Arun Lakshmanan, Andrew Patterson, Venanzio Cichella et al.
ProxQuant: Quantized Neural Networks via Proximal Operators
Yu Bai, Yu-Xiang Wang, Edo Liberty
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
Han Cai, Ligeng Zhu, Song Han
PR Product: A Substitute for Inner Product in Neural Networks
Zhennan Wang, Wenbin Zou, Chen Xu