Papers
938 papers found
A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations
Biswajit Paria, Kirthevasan Kandasamy, Barnabás Póczos
An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient
Pan Xu, Felicia Gao, Quanquan Gu
Approximate Causal Abstractions
Sander Beckers, Frederick Eberhardt, Joseph Y. Halpern
Approximate Inference in Structured Instances with Noisy Categorical Observations
Alireza Heidari, Ihab F. Ilyas, Theodoros Rekatsinas
Approximate Relative Value Learning for Average-reward Continuous State MDPs
Hiteshi Sharma, Mehdi Jafarnia-Jahromi, Rahul Jain
A Sparse Representation-Based Approach to Linear Regression with Partially Shuffled Labels
Martin Slawski, Mostafa Rahmani, Ping Li
A Tighter Analysis of Randomised Policy Iteration
Meet Taraviya, Shivaram Kalyanakrishnan
Augmenting and Tuning Knowledge Graph Embeddings
Robert Bamler, Farnood Salehi, Stephan Mandt
A Weighted Mini-Bucket Bound for Solving Influence Diagram
Junkyu Lee, Radu Marinescu, Alexander Ihler et al.
Bayesian Optimization with Binary Auxiliary Information
Yehong Zhang, Zhongxiang Dai, Bryan Kian Hsiang Low
Be Greedy: How Chromatic Number meets Regret Minimization in Graph Bandits
Shreyas S, Aadirupa Saha, Chiranjib Bhattacharyya
Belief Propagation: Accurate Marginals or Accurate Partition Function – Where is the Difference?
Christian Knoll, Franz Pernkopf
Beyond Structural Causal Models: Causal Constraints Models
Tineke Blom, Stephan Bongers, Joris M. Mooij
Block Neural Autoregressive Flow
Nicola De Cao, Wilker Aziz, Ivan Titov
BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback
Chang Li, Branislav Kveton, Tor Lattimore et al.
Cascading Linear Submodular Bandits: Accounting for Position Bias and Diversity in Online Learning to Rank
Gaurush Hiranandani, Harvineet Singh, Prakhar Gupta et al.
Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias
Patrick Forré, Joris M. Mooij
Causal Discovery with General Non-Linear Relationships using Non-Linear ICA
Ricardo Pio Monti, Kun Zhang, Aapo Hyvärinen
Causal Inference Under Interference And Network Uncertainty
Rohit Bhattacharya, Daniel Malinsky, Ilya Shpitser
CCMI : Classifier based Conditional Mutual Information Estimation
Sudipto Mukherjee, Himanshu Asnani, Sreeram Kannan
Comparing EM with GD in Mixture Models of Two Components
Guojun Zhang, Pascal Poupart, George Trimponias
Conditional Expectation Propagation
Zheng Wang, Shandian Zhe
Convergence Analysis of Gradient-Based Learning in Continuous Games
Benjamin Chasnov, Lillian Ratliff, Eric Mazumdar et al.
Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory
Philipp Geiger, Michel Besserve, Justus Winkelmann et al.
Correlated Learning for Aggregation Systems
Tanvi Verma, Pradeep Varakantham