Papers
572 papers found
Spectral Approximate Inference
Sejun Park, Eunho Yang, Se-Young Yun et al.
CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration
Gellert Weisz, Andras Gyorgy, Csaba Szepesvari
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation
Samuel Wiqvist, Pierre-Alexandre Mattei, Umberto Picchini et al.
Calibrated Approximate Bayesian Inference
Hanwen Xing, Geoff Nicholls, Jeong Lee
Likelihood-free MCMC with Amortized Approximate Ratio Estimators
Joeri Hermans, Volodimir Begy, Gilles Louppe
On Approximate Thompson Sampling with Langevin Algorithms
Eric Mazumdar, Aldo Pacchiano, Yian Ma et al.
Undirected Graphical Models as Approximate Posteriors
Arash Vahdat, Evgeny Andriyash, William Macready
Approximate Group Fairness for Clustering
Bo Li, Lijun Li, Ankang Sun et al.
Debiasing a First-order Heuristic for Approximate Bi-level Optimization
Valerii Likhosherstov, Xingyou Song, Krzysztof Choromanski et al.
Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning
Luisa M Zintgraf, Leo Feng, Cong Lu et al.
Approximate Bayesian Computation with Domain Expert in the Loop
Ayush Bharti, Louis Filstroff, Samuel Kaski
Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity
Lin Guan, Sarath Sreedharan, Subbarao Kambhampati
A Parametric Class of Approximate Gradient Updates for Policy Optimization
Ramki Gummadi, Saurabh Kumar, Junfeng Wen et al.
Efficient Approximate Inference for Stationary Kernel on Frequency Domain
Yohan Jung, Kyungwoo Song, Jinkyoo Park
Differentially Private Approximate Quantiles
Haim Kaplan, Shachar Schnapp, Uri Stemmer
LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood
Piotr Tempczyk, Rafał Michaluk, Lukasz Garncarek et al.
On the Finite-Time Complexity and Practical Computation of Approximate Stationarity Concepts of Lipschitz Functions
Lai Tian, Kaiwen Zhou, Anthony Man-Cho So
Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing
Ramji Venkataramanan, Kevin Kögler, Marco Mondelli
Approximately Equivariant Networks for Imperfectly Symmetric Dynamics
Rui Wang, Robin Walters, Rose Yu
DRAGONN: Distributed Randomized Approximate Gradients of Neural Networks
Zhuang Wang, Zhaozhuo Xu, Xinyu Wu et al.
Multi Resolution Analysis (MRA) for Approximate Self-Attention
Zhanpeng Zeng, Sourav Pal, Jeffery Kline et al.
Approximate Frank-Wolfe Algorithms over Graph-structured Support Sets
Baojian Zhou, Yifan Sun
Approximately Optimal Core Shapes for Tensor Decompositions
Mehrdad Ghadiri, Matthew Fahrbach, Gang Fu et al.
On Sampling with Approximate Transport Maps
Louis Grenioux, Alain Oliviero Durmus, Eric Moulines et al.