97 citations · 266 across the 25 of their papers we have counts for
27 papers
Learning DNF through Generalized Fourier Representations
Mohsen Heidari, Roni Khardon
The Boolean Fourier representation has been widely used in learning theory, particularly for learning Disjunctive Normal Form (DNF) under uniform and product distributions. Extendi…
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…
POAM: Probabilistic Online Attentive Mapping for Efficient Robotic Information Gathering
Weizhe Chen, Lantao Liu, Roni Khardon
Gaussian Process (GP) models are widely used for Robotic Information Gathering (RIG) in exploring unknown environments due to their ability to model complex phenomena with non-para…
Adaptive Robotic Information Gathering via Non-Stationary Gaussian Processes
Weizhe Chen, Roni Khardon, Lantao Liu
Robotic Information Gathering (RIG) is a foundational research topic that answers how a robot (team) collects informative data to efficiently build an accurate model of an unknown…
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…
DiSProD: Differentiable Symbolic Propagation of Distributions for Planning
Palash Chatterjee, Ashutosh Chapagain, Weizhe Chen +1
The paper introduces DiSProD, an online planner developed for environments with probabilistic transitions in continuous state and action spaces. DiSProD builds a symbolic graph tha…
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…
AK: Attentive Kernel for Information Gathering
Weizhe Chen, Roni Khardon, Lantao Liu
Robotic Information Gathering (RIG) relies on the uncertainty of a probabilistic model to identify critical areas for efficient data collection. Gaussian processes (GPs) with stati…
Approximate Inference for Stochastic Planning in Factored Spaces
Zhennan Wu, Roni Khardon
Stochastic planning can be reduced to probabilistic inference in large discrete graphical models, but hardness of inference requires approximation schemes to be used. In this paper…
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…
Stochastic Planning and Lifted Inference
Roni Khardon, Scott Sanner
Lifted probabilistic inference (Poole, 2003) and symbolic dynamic programming for lifted stochastic planning (Boutilier et al, 2001) were introduced around the same time as algorit…
Monte Carlo Structured SVI for Two-Level Non-Conjugate Models
Rishit Sheth, Roni Khardon
The stochastic variational inference (SVI) paradigm, which combines variational inference, natural gradients, and stochastic updates, was recently proposed for large-scale data ana…
The Complexity of Reasoning with FODD and GFODD
Benjamin J. Hescott, Roni Khardon
Recent work introduced Generalized First Order Decision Diagrams (GFODD) as a knowledge representation that is useful in mechanizing decision theoretic planning in relational domai…
Probabilistic Relational Planning with First Order Decision Diagrams
Saket Joshi, Roni Khardon
Dynamic programming algorithms have been successfully applied to propositional stochastic planning problems by using compact representations, in particular algebraic decision diagr…
Solving Relational MDPs with Exogenous Events and Additive Rewards
S. Joshi, R. Khardon, P. Tadepalli +2
We formalize a simple but natural subclass of service domains for relational planning problems with object-centered, independent exogenous events and additive rewards capturing, fo…
Online Learning with Pairwise Loss Functions
Yuyang Wang, Roni Khardon, Dmitry Pechyony +1
Efficient online learning with pairwise loss functions is a crucial component in building large-scale learning system that maximizes the area under the Receiver Operator Characteri…
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…
Policy Iteration for Relational MDPs
Chenggang Wang, Roni Khardon
Relational Markov Decision Processes are a useful abstraction for complex reinforcement learning problems and stochastic planning problems. Recent work developed representation sch…
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…
First Order Decision Diagrams for Relational MDPs
Chenggang Wang, Saket Joshi, Roni Khardon
Markov decision processes capture sequential decision making under uncertainty, where an agent must choose actions so as to optimize long term reward. The paper studies efficient r…
A Refined QSO Selection Method Using Diagnostics Tests: 663 QSO Candidates in the LMC
Dae-Won Kim, Pavlos Protopapas, Markos Trichas +4
We present 663 QSO candidates in the Large Magellanic Cloud (LMC) selected using multiple diagnostics. We started with a set of 2,566 QSO candidates from our previous work selected…