Papers
4,025 papers found
Parameter-Free Online Linear Optimization with Side Information via Universal Coin Betting
Jongha J. Ryu, Alankrita Bhatt, Young-Han Kim
Parametric Bootstrap for Differentially Private Confidence Intervals
Cecilia Ferrando, Shufan Wang, Daniel Sheldon
Pareto Optimal Model Selection in Linear Bandits
Yinglun Zhu, Robert Nowak
Particle-based Adversarial Local Distribution Regularization
Thanh Nguyen-Duc, Trung Le, He Zhao et al.
Performative Prediction in a Stateful World
Gavin Brown, Shlomi Hod, Iden Kalemaj
Permutation Equivariant Layers for Higher Order Interactions
Horace Pan, Risi Kondor
p-Generalized Probit Regression and Scalable Maximum Likelihood Estimation via Sketching and Coresets
Alexander Munteanu, Simon Omlor, Christian Peters
Physics Informed Deep Kernel Learning
Zheng Wang, Wei Xing, Robert Kirby et al.
Pick-and-Mix Information Operators for Probabilistic ODE Solvers
Nathanael Bosch, Filip Tronarp, Philipp Hennig
Point Cloud Generation with Continuous Conditioning
Larissa T. Triess, Andre Bühler, David Peter et al.
Policy Learning and Evaluation with Randomized Quasi-Monte Carlo
Sébastien M. R. Arnold, Pierre L’Ecuyer, Liyu Chen et al.
Policy Learning for Optimal Individualized Dose Intervals
Guanhua Chen, Xiaomao Li, Menggang Yu
Polynomial Time Reinforcement Learning in Factored State MDPs with Linear Value Functions
Zihao Deng, Siddartha Devic, Brendan Juba
Practical Schemes for Finding Near-Stationary Points of Convex Finite-Sums
Kaiwen Zhou, Lai Tian, Anthony Man-Cho So et al.
Predicting the impact of treatments over time with uncertainty aware neural differential equations.
Edward De Brouwer, Javier Gonzalez, Stephanie Hyland
Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach
Setareh Ariafar, Justin Gilmer, Zachary Nado et al.
Predictive variational Bayesian inference as risk-seeking optimization
Futoshi Futami, Tomoharu Iwata, Naonori Ueda et al.
Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes
Zhiyuan Jerry Lin, Raul Astudillo, Peter Frazier et al.
Primal-Dual Stochastic Mirror Descent for MDPs
Daniil Tiapkin, Alexander Gasnikov
Privacy Amplification by Decentralization
Edwige Cyffers, Aurélien Bellet
Privacy Amplification by Subsampling in Time Domain
Tatsuki Koga, Casey Meehan, Kamalika Chaudhuri
Private Sequential Hypothesis Testing for Statisticians: Privacy, Error Rates, and Sample Size
Wanrong Zhang, Yajun Mei, Rachel Cummings
Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations
Nicholas Krämer, Jonathan Schmidt, Philipp Hennig
Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods
Chirag Agarwal, Marinka Zitnik, Himabindu Lakkaraju
Projection Predictive Inference for Generalized Linear and Additive Multilevel Models
Alejandro Catalina, Paul-Christian Bürkner, Aki Vehtari