Papers
Private Adaptive Optimization with Side information
Tian Li, Manzil Zaheer, Sashank Reddi et al.
Private frequency estimation via projective geometry
Vitaly Feldman, Jelani Nelson, Huy Nguyen et al.
Private optimization in the interpolation regime: faster rates and hardness results
Hilal Asi, Karan Chadha, Gary Cheng et al.
Private Streaming SCO in $\ell_p$ geometry with Applications in High Dimensional Online Decision Making
Yuxuan Han, Zhicong Liang, Zhipeng Liang et al.
Probabilistically Robust Learning: Balancing Average and Worst-case Performance
Alexander Robey, Luiz Chamon, George J. Pappas et al.
Probabilistic Bilevel Coreset Selection
Xiao Zhou, Renjie Pi, Weizhong Zhang et al.
Probabilistic ODE Solutions in Millions of Dimensions
Nicholas Krämer, Nathanael Bosch, Jonathan Schmidt et al.
ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning
Jun Xia, Lirong Wu, Ge Wang et al.
ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training
Hui-Po Wang, Sebastian Stich, Yang He et al.
Prompting Decision Transformer for Few-Shot Policy Generalization
Mengdi Xu, Yikang Shen, Shun Zhang et al.
Prototype-Anchored Learning for Learning with Imperfect Annotations
Xiong Zhou, Xianming Liu, Deming Zhai et al.
Prototype Based Classification from Hierarchy to Fairness
Mycal Tucker, Julie A. Shah
Provable Acceleration of Heavy Ball beyond Quadratics for a Class of Polyak-Lojasiewicz Functions when the Non-Convexity is Averaged-Out
Jun-Kun Wang, Chi-Heng Lin, Andre Wibisono et al.
Provable Domain Generalization via Invariant-Feature Subspace Recovery
Haoxiang Wang, Haozhe Si, Bo Li et al.
Provable Reinforcement Learning with a Short-Term Memory
Yonathan Efroni, Chi Jin, Akshay Krishnamurthy et al.
Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance
Zhuoning Yuan, Yuexin Wu, Zi-Hao Qiu et al.
Provably Adversarially Robust Nearest Prototype Classifiers
Václav Voráček, Matthias Hein
Provably Efficient Offline Reinforcement Learning for Partially Observable Markov Decision Processes
Hongyi Guo, Qi Cai, Yufeng Zhang et al.
Proving Theorems using Incremental Learning and Hindsight Experience Replay
Eser Aygün, Ankit Anand, Laurent Orseau et al.
Proximal and Federated Random Reshuffling
Konstantin Mishchenko, Ahmed Khaled, Peter Richtarik
Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularization
Samuel Hurault, Arthur Leclaire, Nicolas Papadakis
Proximal Exploration for Model-guided Protein Sequence Design
Zhizhou Ren, Jiahan Li, Fan Ding et al.
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!
Konstantin Mishchenko, Grigory Malinovsky, Sebastian Stich et al.
Public Data-Assisted Mirror Descent for Private Model Training
Ehsan Amid, Arun Ganesh, Rajiv Mathews et al.
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images
Shiran Zada, Itay Benou, Michal Irani