Papers
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
QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning
Liping Yi, Wang Gang, Liu Xiaoguang
Quant-BnB: A Scalable Branch-and-Bound Method for Optimal Decision Trees with Continuous Features
Rahul Mazumder, Xiang Meng, Haoyue Wang
Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding
Haotian Ma, Hao Zhang, Fan Zhou et al.
Quantifying and Learning Linear Symmetry-Based Disentanglement
Loek Tonnaer, Luis Armando Perez Rey, Vlado Menkovski et al.
Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra
Nadiia Chepurko, Kenneth Clarkson, Lior Horesh et al.
Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimization
Deokjae Lee, Seungyong Moon, Junhyeok Lee et al.
Random Forest Density Estimation
Hongwei Wen, Hanyuan Hang
Random Gegenbauer Features for Scalable Kernel Methods
Insu Han, Amir Zandieh, Haim Avron
RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression
Yu Gong, Greg Mori, Fred Tung
Reachability Constrained Reinforcement Learning
Dongjie Yu, Haitong Ma, Shengbo Li et al.