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19 papers · 2 filters
Budgeted and Non-budgeted Causal Bandits
Vineet Nair, Vishakha Patil, Gaurav Sinha
Learning good interventions in a causal graph can be modelled as a stochastic multi-armed bandit problem with side-information. First, we study this problem when interventions are…
Split: Inferring Unobserved Event Probabilities for Disentangling Brand-Customer Interactions
Ayush Chauhan, Aditya Anand, Shaddy Garg +2
Often, data contains only composite events composed of multiple events, some observed and some unobserved. For example, search ad click is observed by a brand, whereas which custom…
Neural Contextual Bandits with Deep Representation and Shallow Exploration
Pan Xu, Zheng Wen, Handong Zhao +1
We study a general class of contextual bandits, where each context-action pair is associated with a raw feature vector, but the reward generating function is unknown. We propose a…
Towards Safe Policy Improvement for Non-Stationary MDPs
Yash Chandak, Scott M. Jordan, Georgios Theocharous +2
Many real-world sequential decision-making problems involve critical systems with financial risks and human-life risks. While several works in the past have proposed methods that a…
Approximate information state for approximate planning and reinforcement learning in partially observed systems
Jayakumar Subramanian, Amit Sinha, Raihan Seraj +1
We propose a theoretical framework for approximate planning and learning in partially observed systems. Our framework is based on the fundamental notion of information state. We pr…
A Context Integrated Relational Spatio-Temporal Model for Demand and Supply Forecasting
Hongjie Chen, Ryan A. Rossi, Kanak Mahadik +1
Traditional methods for demand forecasting only focus on modeling the temporal dependency. However, forecasting on spatio-temporal data requires modeling of complex nonlinear relat…