3 citations · 7 across the 10 of their papers we have counts for
11 papers
Propose to Learn, Learn to Propose: Evaluability-Aware Assistance under Bounded Rationality
Yifan Zhu, Sammie Katt, Samuel Kaski
AI assistants often collaborate by proposing candidate edits, plans, or designs that users evaluate before adoption. Existing assistance methods focus on proposal quality or user-g…
Robust Bayesian Decision Making under Adversarial Uncertainty
Haripriya Harikumar, Sammie Katt, Yasir Zubayr Barlas +1
Scientific experiments are often designed to maximize information gain, yet in many applications the primary objective is to support reliable downstream decision-making. Existing d…
Multi-Objective Bayesian Optimization via Adaptive \varepsilon-Constraints Decomposition
Yaohong Yang, Sammie Katt, Samuel Kaski
Multi-objective Bayesian optimization (MOBO) provides a principled framework for optimizing multiple expensive black-box functions. However, existing MOBO methods often struggle wi…
Constrained Bayesian Experimental Design via Online Planning
Yujia Guo, Daolang Huang, Xinyu Zhang +3
Bayesian experimental design (BED) is a principled framework for data-efficient design of sequential experiments. However, existing BED methods are unable to adapt to dynamic const…
More Than Irrational: Modeling Belief-Biased Agents
Yifan Zhu, Sammie Katt, Samuel Kaski
Despite the explosive growth of AI and the technologies built upon it, predicting and inferring the sub-optimal behavior of users or human collaborators remains a critical challeng…
An Interactive Framework for Finding the Optimal Trade-off in Differential Privacy
Yaohong Yang, Aki Rehn, Sammie Katt +2
Differential privacy (DP) is the standard for privacy-preserving analysis, and introduces a fundamental trade-off between privacy guarantees and model performance. Selecting the op…
On-Robot Bayesian Reinforcement Learning for POMDPs
Hai Nguyen, Sammie Katt, Yuchen Xiao +1
Robot learning is often difficult due to the expense of gathering data. The need for large amounts of data can, and should, be tackled with effective algorithms and leveraging expe…
BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs
Sammie Katt, Hai Nguyen, Frans A. Oliehoek +1
While reinforcement learning (RL) has made great advances in scalability, exploration and partial observability are still active research topics. In contrast, Bayesian RL (BRL) pro…
Removing Dynamic Objects for Static Scene Reconstruction using Light Fields
Pushyami Kaveti, Sammie Katt, Hanumant Singh
There is a general expectation that robots should operate in environments that consist of static and dynamic entities including people, furniture and automobiles. These dynamic env…
Bayesian Reinforcement Learning in Factored POMDPs
Sammie Katt, Frans Oliehoek, Christopher Amato
Bayesian approaches provide a principled solution to the exploration-exploitation trade-off in Reinforcement Learning. Typical approaches, however, either assume a fully observable…
Learning in POMDPs with Monte Carlo Tree Search
Sammie Katt, Frans A. Oliehoek, Christopher Amato
The POMDP is a powerful framework for reasoning under outcome and information uncertainty, but constructing an accurate POMDP model is difficult. Bayes-Adaptive Partially Observabl…