Papers
938 papers found
Interpolating between sampling and variational inference with infinite stochastic mixtures
Richard D. Lange, Ari S. Benjamin, Ralf M. Haefner et al.
Intervention target estimation in the presence of latent variables
Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri et al.
Knowledge representation combining quaternion path integration and depth-wise atrous circular convolution
Xinyuan Chen, Zhongmei Zhou, Meichun Gao et al.
Laplace approximated Gaussian process state-space models
Jakob Lindinger, Barbara Rakitsch, Christoph Lippert
Learning a neural Pareto manifold extractor with constraints
Soumyajit Gupta, Gurpreet Singh, Raghu Bollapragada et al.
Learning binary multi-scale games on networks
Sixie Yu, P. Jeffrey Brantingham, Matthew Valasik et al.
Learning explainable templated graphical models
Varun Embar, Sriram Srinivasa, Lise Getoor
Learning functions on multiple sets using multi-set transformers
Kira A. Selby, Ahmad Rashid, Ivan Kobyzev et al.
Learning in Markov games: Can we exploit a general-sum opponent?
Giorgia Ramponi, Marcello Restelli
Learning invariant weights in neural networks
Tycho F.A. van der Ouderaa, Mark van der Wilk
Learning large Bayesian networks with expert constraints
Vaidyanathan Peruvemba Ramaswamy, Stefan Szeider
Learning linear non-Gaussian polytree models
Daniele Tramontano, Anthea Monod, Mathias Drton
Learning soft interventions in complex equilibrium systems
Michel Besserve, Bernhard Schölkopf
Learning sparse representations of preferences within Choquet expected utility theory
Margot Herin, Patrice Perny, Nataliya Sokolovska
Lifting in multi-agent systems under uncertainty
Tanya Braun, Marcel Gehrke, Florian Lau et al.
Linearizing contextual bandits with latent state dynamics
Elliot Nelson, Debarun Bhattacharjya, Tian Gao et al.
Local calibration: metrics and recalibration
Rachel Luo, Aadyot Bhatnagar, Yu Bai et al.
Low-precision arithmetic for fast Gaussian processes
Wesley J. Maddox, Andres Potapcynski, Andrew Gordon Wilson
Marginal MAP estimation for inverse RL under occlusion with observer noise
Prasanth Sengadu Suresh, Prashant Doshi
Meta-learning without data via Wasserstein distributionally-robust model fusion
Zhenyi Wang, Xiaoyang Wang, Li Shen et al.
Mitigating statistical bias within differentially private synthetic data
Sahra Ghalebikesabi, Harry Wilde, Jack Jewson et al.
Modeling extremes with $d$-max-decreasing neural networks
Ali Hasan, Khalil Elkhalil, Yuting Ng et al.
Monotonicity regularization: Improved penalties and novel applications to disentangled representation learning and robust classification
João Monteiro, Mohamed Osama Ahmed, Hoseein Hajimirsadeghi et al.
Multiclass classification for Hawkes processes
Christophe Denis, Charlotte Dion-Blanc, Laure Sansonnet
Multi-objective Bayesian optimization over high-dimensional search spaces
Samuel Daulton, David Eriksson, Maximilian Balandat et al.