Papers
Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks
Liam Collins, Hamed Hassani, Mahdi Soltanolkotabi et al.
Provable Privacy with Non-Private Pre-Processing
Yaxi Hu, Amartya Sanyal, Bernhard Schölkopf
Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning
Hongming Zhang, Tongzheng Ren, Chenjun Xiao et al.
Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
Yu Chen, Xiangcheng Zhang, Siwei Wang et al.
Provably Better Explanations with Optimized Aggregation of Feature Attributions
Thomas Decker, Ananta R. Bhattarai, Jindong Gu et al.
Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret
Han Zhong, Jiachen Hu, Yecheng Xue et al.
Provably Efficient Long-Horizon Exploration in Monte Carlo Tree Search through State Occupancy Regularization
Liam Schramm, Abdeslam Boularias
Provably Efficient Partially Observable Risk-sensitive Reinforcement Learning with Hindsight Observation
Tonghe Zhang, Yu Chen, Longbo Huang
Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples
Dake Bu, Wei Huang, Taiji Suzuki et al.
Provably Robust DPO: Aligning Language Models with Noisy Feedback
Sayak Ray Chowdhury, Anush Kini, Nagarajan Natarajan
Provably Scalable Black-Box Variational Inference with Structured Variational Families
Joohwan Ko, Kyurae Kim, Woo Chang Kim et al.
Pruned Pivot: Correlation Clustering Algorithm for Dynamic, Parallel, and Local Computation Models
Mina Dalirrooyfard, Konstantin Makarychev, Slobodan Mitrovic
PruNeRF: Segment-Centric Dataset Pruning via 3D Spatial Consistency
Yeonsung Jung, Heecheol Yun, Joonhyung Park et al.
Pruner-Zero: Evolving Symbolic Pruning Metric From Scratch for Large Language Models
Peijie Dong, Lujun Li, Zhenheng Tang et al.
Pseudo-Calibration: Improving Predictive Uncertainty Estimation in Unsupervised Domain Adaptation
Dapeng Hu, Jian Liang, Xinchao Wang et al.
Purifying Quantization-conditioned Backdoors via Layer-wise Activation Correction with Distribution Approximation
Boheng Li, Yishuo Cai, Jisong Cai et al.
Purify Unlearnable Examples via Rate-Constrained Variational Autoencoders
Yi Yu, Yufei Wang, Song Xia et al.
Pursuing Overall Welfare in Federated Learning through Sequential Decision Making
Seok-Ju Hahn, Gi-Soo Kim, Junghye Lee
Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels
Haoning Wu, Zicheng Zhang, Weixia Zhang et al.
QBMK: Quantum-based Matching Kernels for Un-attributed Graphs
Lu Bai, Lixin Cui, Ming Li et al.
QORA: Zero-Shot Transfer via Interpretable Object-Relational Model Learning
Gabriel Stella, Dmitri Loguinov
Q-Probe: A Lightweight Approach to Reward Maximization for Language Models
Kenneth Li, Samy Jelassi, Hugh Zhang et al.
Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent
Yingru Li, Jiawei Xu, Lei Han et al.
Quality-Diversity Actor-Critic: Learning High-Performing and Diverse Behaviors via Value and Successor Features Critics
Luca Grillotti, Maxence Faldor, Borja G. León et al.