Papers
938 papers found
Parity calibration
Youngseog Chung, Aaron Rumack, Chirag Gupta
Partial identification of dose responses with hidden confounders
Myrl G. Marmarelis, Elizabeth Haddad, Andrew Jesson et al.
Personalized federated domain adaptation for item-to-item recommendation
Ziwei Fan, Hao Ding, Anoop Deoras et al.
Pessimistic Model Selection for Offline Deep Reinforcement Learning
Chao-Han Huck Yang, Zhengling Qi, Yifan Cui et al.
Phase-shifted adversarial training
Yeachan Kim, Seongyeon Kim, Ihyeok Seo et al.
Piecewise Deterministic Markov Processes for Bayesian Neural Networks
Ethan Goan, Dimitri Perrin, Kerrie Mengersen et al.
Posterior sampling-based online learning for the stochastic shortest path model
Mehdi Jafarnia-Jahromi, Liyu Chen, Rahul Jain et al.
Practical privacy-preserving Gaussian process regression via secret sharing
Jinglong Luo, Yehong Zhang, Jiaqi Zhang et al.
Private Prediction Strikes Back! Private Kernelized Nearest Neighbors with Individual Rényi Filter
Yuqing Zhu, Xuandong Zhao, Chuan Guo et al.
Probabilistically robust conformal prediction
Subhankar Ghosh, Yuanjie Shi, Taha Belkhouja et al.
Probabilistic circuits that know what they don’t know
Fabrizio Ventola, Steven Braun, Zhongjie Yu et al.
Probabilistic Flow Circuits: Towards Unified Deep Models for Tractable Probabilistic Inference
Sahil Sidheekh, Kristian Kersting, Sriraam Natarajan
Probabilistic Multi-Dimensional Classification
Vu-Linh Nguyen, Yang Yang, Cassio De Campos
Provably Efficient Adversarial Imitation Learning with Unknown Transitions
Tian Xu, Ziniu Li, Yang Yu et al.
Provably efficient representation selection in Low-rank Markov Decision Processes: from online to offline RL
W. Zhang, J. He, D. Zhou et al.
Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures?
Lisa Wimmer, Yusuf Sale, Paul Hofman et al.
Quantifying lottery tickets under label noise: accuracy, calibration, and complexity
Viplove Arora, Daniele Irto, Sebastian Goldt et al.
Quasi-Bayesian nonparametric density estimation via autoregressive predictive updates
Sahra Ghalebikesabi, Chris C. Holmes, Edwin Fong et al.
Random Reshuffling with Variance Reduction: New Analysis and Better Rates
Grigory Malinovsky, Alibek Sailanbayev, Peter Richtárik
RDM-DC: Poisoning Resilient Dataset Condensation with Robust Distribution Matching
Tianhang Zheng, Baochun Li
Regularized online DR-submodular optimization
Pengyu Zuo, Yao Wang, Shaojie Tang
Residual-based error bound for physics-informed neural networks
Shuheng Liu, Xiyue Huang, Pavlos Protopapas
Revisiting Bayesian network learning with small vertex cover
Juha Harviainen, Mikko Koivisto
Reward-machine-guided, self-paced reinforcement learning
Cevahir Koprulu, Ufuk Topcu
Risk-aware curriculum generation for heavy-tailed task distributions
Cevahir Koprulu, Thiago D. Simão, Nils Jansen et al.