Papers
4,025 papers found
Riemannian Laplace Approximation with the Fisher Metric
Hanlin Yu, Marcelo Hartmann, Bernardo Williams Moreno Sanchez et al.
Risk Seeking Bayesian Optimization under Uncertainty for Obtaining Extremum
Shogo Iwazaki, Tomohiko Tanabe, Mitsuru Irie et al.
RL in Markov Games with Independent Function Approximation: Improved Sample Complexity Bound under the Local Access Model
Junyi Fan, Yuxuan Han, Jialin Zeng et al.
Robust Approximate Sampling via Stochastic Gradient Barker Dynamics
Lorenzo Mauri, Giacomo Zanella
Robust Non-linear Normalization of Heterogeneous Feature Distributions with Adaptive Tanh-Estimators
Felip Guimerà Cuevas, Helmut Schmid
Robust Offline Reinforcement Learning with Heavy-Tailed Rewards
Jin Zhu, Runzhe Wan, Zhengling Qi et al.
Robust Sparse Voting
Youssef Allouah, Rachid Guerraoui, Lê-Nguyên Hoang et al.
Robust SVD Made Easy: A fast and reliable algorithm for large-scale data analysis
Sangil Han, Sungkyu Jung, Kyoowon Kim
Robust variance-regularized risk minimization with concomitant scaling
Matthew J. Holland
SADI: Similarity-Aware Diffusion Model-Based Imputation for Incomplete Temporal EHR Data
Zongyu Dai, Emily Getzen, Qi Long
Safe and Interpretable Estimation of Optimal Treatment Regimes
Harsh Parikh, Quinn M Lanners, Zade Akras et al.
Sample Complexity Characterization for Linear Contextual MDPs
Junze Deng, Yuan Cheng, Shaofeng Zou et al.
Sample Efficient Learning of Factored Embeddings of Tensor Fields
Taemin Heo, Chandrajit Bajaj
Sample-efficient neural likelihood-free Bayesian inference of implicit HMMs
Sanmitra Ghosh, Paul Birrell, Daniela De Angelis
Sample-Efficient Personalization: Modeling User Parameters as Low Rank Plus Sparse Components
Soumyabrata Pal, Prateek Varshney, Gagan Madan et al.
Sampling-based Safe Reinforcement Learning for Nonlinear Dynamical Systems
Wesley Suttle, Vipul Kumar Sharma, Krishna Chaitanya Kosaraju et al.
Scalable Algorithms for Individual Preference Stable Clustering
Ron Mosenzon, Ali Vakilian
Scalable Higher-Order Tensor Product Spline Models
David Ruegamer
Scalable Learning of Item Response Theory Models
Susanne Frick, Amer Krivosija, Alexander Munteanu
Scalable Meta-Learning with Gaussian Processes
Petru Tighineanu, Lukas Grossberger, Paul Baireuther et al.
Score Operator Newton transport
Nisha Chandramoorthy, Florian T Schaefer, Youssef M Marzouk
SDEs for Minimax Optimization
Enea Monzio Compagnoni, Antonio Orvieto, Hans Kersting et al.
SDMTR: A Brain-inspired Transformer for Relation Inference
Xiangyu Zeng, Jie Lin, Piao Hu et al.