Search Results for author:
Found 13 papers, 5 papers with code
Estimation of Recursive Route Choice Models with Incomplete Trip Observations
This work concerns the estimation of recursive route choice models in the situation that the trip observations are incomplete, i. e., there are unconnected links (or nodes) in the observations.
Rectified Max-Value Entropy Search for Bayesian Optimization
Although the existing max-value entropy search (MES) is based on the widely celebrated notion of mutual information, its empirical performance can suffer due to two misconceptions whose implications on the exploration-exploitation trade-off are investigated in this paper.
Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgotten
Similarly, MCU can be used to erase the lineage of a user's personal data from trained ML models, thus upholding a user's "right to be forgotten".
Optimizing Conditional Value-At-Risk of Black-Box Functions
This paper presents two Bayesian optimization (BO) algorithms with theoretical performance guarantee to maximize the conditional value-at-risk (CVaR) of a black-box function: CV-UCB and CV-TS which are based on the well-established principle of optimism in the face of uncertainty and Thompson sampling, respectively.
Trusted-Maximizers Entropy Search for Efficient Bayesian Optimization
Information-based Bayesian optimization (BO) algorithms have achieved state-of-the-art performance in optimizing a black-box objective function.
Value-at-Risk Optimization with Gaussian Processes
Value-at-risk (VaR) is an established measure to assess risks in critical real-world applications with random environmental factors.
An Information-Theoretic Framework for Unifying Active Learning Problems
This paper presents an information-theoretic framework for unifying active learning problems: level set estimation (LSE), Bayesian optimization (BO), and their generalized variant.
Top-$k$ Ranking Bayesian Optimization
This paper presents a novel approach to top-$k$ ranking Bayesian optimization (top-$k$ ranking BO) which is a practical and significant generalization of preferential BO to handle top-$k$ ranking and tie/indifference observations.
Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization
The problem of inverse reinforcement learning (IRL) is relevant to a variety of tasks including value alignment and robot learning from demonstration.
Variational Bayesian Unlearning
We frame this problem as one of minimizing the Kullback-Leibler divergence between the approximate posterior belief of model parameters after directly unlearning from erased data vs. the exact posterior belief from retraining with remaining data.
Inverse Reinforcement Learning with Missing Data
We consider the problem of recovering an expert's reward function with inverse reinforcement learning (IRL) when there are missing/incomplete state-action pairs or observations in the demonstrated trajectories.
GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection
This paper looks into the problem of detecting network anomalies by analyzing NetFlow records.
Inverse Reinforcement Learning with Locally Consistent Reward Functions
By representing our IRL problem with a probabilistic graphical model, an expectation-maximization (EM) algorithm can be devised to iteratively learn the different reward functions and the stochastic transitions between them in order to jointly improve the likelihood of the expert’s demonstrated trajectories.
