Papers
On Lifted Inference Using Neural Embeddings
Mohammad Maminur Islam, Somdeb Sarkhel, Deepak Venugopal
Robust Ordinal Embedding from Contaminated Relative Comparisons
Ke Ma, Qianqian Xu, Xiaochun Cao
Interleave Variational Optimization with Monte Carlo Sampling: A Tale of Two Approximate Inference Paradigms
Qi Lou, Rina Dechter, Alexander Ihler
Finding All Bayesian Network Structures within a Factor of Optimal
Zhenyu A. Liao, Charupriya Sharma, James Cussens et al.
Dirichlet Multinomial Mixture with Variational Manifold Regularization: Topic Modeling over Short Texts
Ximing Li, Jiaojiao Zhang, Jihong Ouyang
Randomized Strategies for Robust Combinatorial Optimization
Yasushi Kawase, Hanna Sumita
MFBO-SSM: Multi-Fidelity Bayesian Optimization for Fast Inference in State-Space Models
Mahdi Imani, Seyede Fatemeh Ghoreishi, Douglas Allaire et al.
Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems
Trong Nghia Hoang, Quang Minh Hoang, Kian Hsiang Low et al.
A Generative Model for Dynamic Networks with Applications
Shubham Gupta, Gaurav Sharma, Ambedkar Dukkipati
Marginal Inference in Continuous Markov Random Fields Using Mixtures
Yuanzhen Guo, Hao Xiong, Nicholas Ruozzi
Exact and Approximate Weighted Model Integration with Probability Density Functions Using Knowledge Compilation
Pedro Zuidberg Dos Martires, Anton Dries, Luc De Raedt
Fast Relational Probabilistic Inference and Learning: Approximate Counting via Hypergraphs
Mayukh Das, Devendra Singh Dhami, Gautam Kunapuli et al.
Path-Specific Counterfactual Fairness
Silvia Chiappa
Learning Diverse Bayesian Networks
Cong Chen, Changhe Yuan
On the Hardness of Probabilistic Inference Relaxations
Supratik Chakraborty, Kuldeep S. Meel, Moshe Y. Vardi
On Testing of Uniform Samplers
Sourav Chakraborty, Kuldeep S. Meel
Probabilistic Logic Programming with Beta-Distributed Random Variables
Federico Cerutti, Lance Kaplan, Angelika Kimmig et al.
Robustness Guarantees for Bayesian Inference with Gaussian Processes
Luca Cardelli, Marta Kwiatkowska, Luca Laurenti et al.
Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications
Daniel S. Brown, Scott Niekum
Active Preference Learning Based on Generalized Gini Functions: Application to the Multiagent Knapsack Problem
Nadjet Bourdache, Patrice Perny
Online Multi-Agent Pathfinding
Jiří Švancara, Marek Vlk, Roni Stern et al.
Rotational Diversity in Multi-Cycle Assignment Problems
Helge Spieker, Arnaud Gotlieb, Morten Mossige
Deep Learning for Cost-Optimal Planning: Task-Dependent Planner Selection
Silvan Sievers, Michael Katz, Shirin Sohrabi et al.