97 citations · 266 across the 25 of their papers we have counts for
10 papers · 1 filter
Improving planning and MBRL with temporally-extended actions
Palash Chatterjee, Roni Khardon
Continuous time systems are often modeled using discrete time dynamics but this requires a small simulation step to maintain accuracy. In turn, this requires a large planning horiz…
Stability-based Generalization Bounds for Variational Inference
Yadi Wei, Roni Khardon
Variational inference (VI) is widely used for approximate inference in Bayesian machine learning. In addition to this practical success, generalization bounds for variational infer…
Variational Inference on the Final-Layer Output of Neural Networks
Yadi Wei, Roni Khardon
Traditional neural networks are simple to train but they typically produce overconfident predictions. In contrast, Bayesian neural networks provide good uncertainty quantification…
On the Performance of Direct Loss Minimization for Bayesian Neural Networks
Yadi Wei, Roni Khardon
Direct Loss Minimization (DLM) has been proposed as a pseudo-Bayesian method motivated as regularized loss minimization. Compared to variational inference, it replaces the loss ter…
Explainable Models via Compression of Tree Ensembles
Siwen Yan, Sriraam Natarajan, Saket Joshi +2
Ensemble models (bagging and gradient-boosting) of relational decision trees have proved to be one of the most effective learning methods in the area of probabilistic logic models…
Direct loss minimization algorithms for sparse Gaussian processes
Yadi Wei, Rishit Sheth, Roni Khardon
The paper provides a thorough investigation of Direct loss minimization (DLM), which optimizes the posterior to minimize predictive loss, in sparse Gaussian processes. For the conj…
Nonparametric Bayesian Mixed-effect Model: a Sparse Gaussian Process Approach
Yuyang Wang, Roni Khardon
Multi-task learning models using Gaussian processes (GP) have been developed and successfully applied in various applications. The main difficulty with this approach is the computa…
Infinite Shift-invariant Grouped Multi-task Learning for Gaussian Processes
Yuyang Wang, Roni Khardon, Pavlos Protopapas
Multi-task learning leverages shared information among data sets to improve the learning performance of individual tasks. The paper applies this framework for data where each task…
Nonparametric Bayesian Estimation of Periodic Functions
Yuyang Wang, Roni Khardon, Pavlos Protopapas
Many real world problems exhibit patterns that have periodic behavior. For example, in astrophysics, periodic variable stars play a pivotal role in understanding our universe. An i…
Efficiency versus Convergence of Boolean Kernels for On-Line Learning Algorithms
R. Khardon, D. Roth, R. A. Servedio
The paper studies machine learning problems where each example is described using a set of Boolean features and where hypotheses are represented by linear threshold elements. One m…