Papers
11,955 papers found
Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks
Jesse Farebrother, Joshua Greaves, Rishabh Agarwal et al.
Provable Defense Against Geometric Transformations
Rem Yang, Jacob Laurel, Sasa Misailovic et al.
Provable Memorization Capacity of Transformers
Junghwan Kim, Michelle Kim, Barzan Mozafari
Provable Robustness against Wasserstein Distribution Shifts via Input Randomization
Aounon Kumar, Alexander Levine, Tom Goldstein et al.
Provable Sim-to-real Transfer in Continuous Domain with Partial Observations
Jiachen Hu, Han Zhong, Chi Jin et al.
Provably Auditing Ordinary Least Squares in Low Dimensions
Ankur Moitra, Dhruv Rohatgi
Provably Efficient Lifelong Reinforcement Learning with Linear Representation
Sanae Amani, Lin Yang, Ching-An Cheng
Provably Efficient Risk-Sensitive Reinforcement Learning: Iterated CVaR and Worst Path
Yihan Du, Siwei Wang, Longbo Huang
Pruning Deep Neural Networks from a Sparsity Perspective
Enmao Diao, Ganghua Wang, Jiawei Zhang et al.
Pseudoinverse-Guided Diffusion Models for Inverse Problems
Jiaming Song, Arash Vahdat, Morteza Mardani et al.
Pseudo-label Training and Model Inertia in Neural Machine Translation
Benjamin Hsu, Anna Currey, Xing Niu et al.
Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play
Jeremiah Zhe Liu, Krishnamurthy Dj Dvijotham, Jihyeon Lee et al.
Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore
Guoyang Xie, Jinbao Wang, Jiaqi Liu et al.
PV3D: A 3D Generative Model for Portrait Video Generation
Zhongcong Xu, Jianfeng Zhang, Jun Hao Liew et al.
QAID: Question Answering Inspired Few-shot Intent Detection
Asaf Yehudai, Matan Vetzler, Yosi Mass et al.
Q-Pensieve: Boosting Sample Efficiency of Multi-Objective RL Through Memory Sharing of Q-Snapshots
Wei Hung, Bo Kai Huang, Ping-Chun Hsieh et al.
Quality-Similar Diversity via Population Based Reinforcement Learning
Shuang Wu, Jian Yao, Haobo Fu et al.
Quantifying and Mitigating the Impact of Label Errors on Model Disparity Metrics
Julius Adebayo, Melissa Hall, Bowen Yu et al.
Quantifying Memorization Across Neural Language Models
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski et al.
Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions
Jake Snell, Thomas P Zollo, Zhun Deng et al.
Quantized Compressed Sensing with Score-Based Generative Models
Xiangming Meng, Yoshiyuki Kabashima
QuAnt: Quantum Annealing with Learnt Couplings
Marcel Seelbach Benkner, Maximilian Krahn, Edith Tretschk et al.
Quasi-optimal Reinforcement Learning with Continuous Actions
Yuhan Li, Wenzhuo Zhou, Ruoqing Zhu
Random Laplacian Features for Learning with Hyperbolic Space
Tao Yu, Christopher De Sa
RandProx: Primal-Dual Optimization Algorithms with Randomized Proximal Updates
Laurent Condat, Peter Richtárik