1 code implementation • 5 Feb 2023 • Yadi Wei, Roni Khardon
In contrast, Bayesian neural networks provide good uncertainty quantification but optimizing them is time consuming due to the large parameter space.
no code implementations • 3 Feb 2023 • Palash Chatterjee, Ashutosh Chapagain, Weizhe Chen, Roni Khardon
DiSProD builds a symbolic graph that captures the distribution of future trajectories, conditioned on a given policy, using independence assumptions and approximate propagation of distributions.
no code implementations • 15 Nov 2022 • Yadi Wei, Roni Khardon
Direct Loss Minimization (DLM) has been proposed as a pseudo-Bayesian method motivated as regularized loss minimization.
no code implementations • 16 Jun 2022 • Siwen Yan, Sriraam Natarajan, Saket Joshi, Roni Khardon, Prasad Tadepalli
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 (PLMs).
1 code implementation • 23 Mar 2022 • 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.
1 code implementation • 7 Apr 2020 • Yadi Wei, Rishit Sheth, Roni Khardon
The application of DLM in non-conjugate cases is more complex because the logarithm of expectation in the log-loss DLM objective is often intractable and simple sampling leads to biased estimates of gradients.
1 code implementation • NeurIPS 2019 • Hao(Jackson) Cui, Roni Khardon
Our approach enables scaling to large factored action spaces in addition to large state spaces and observation spaces.
no code implementations • pproximateinference AABI Symposium 2019 • Rishit Sheth, Roni Khardon
Our criterion can be used to derive new sparse Gaussian process algorithms that have error guarantees applicable to various likelihoods.
no code implementations • NeurIPS 2018 • Hao Cui, Radu Marinescu, Roni Khardon
This yields a novel algebraic gradient-based solver (AGS) for MMAP.
no code implementations • NeurIPS 2017 • Rishit Sheth, Roni Khardon
The paper furthers such analysis by providing bounds on the excess risk of variational inference algorithms and related regularized loss minimization algorithms for a large class of latent variable models with Gaussian latent variables.
no code implementations • 4 Jan 2017 • 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 algorithmic efforts to use abstraction in stochastic systems.
no code implementations • 12 Dec 2016 • 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 analysis in conjugate Bayesian models and demonstrated to be effective in several problems.
no code implementations • 5 Jul 2014 • Benjamin J. Hescott, Roni Khardon
In particular, we study the evaluation problem, the satisfiability problem, and the equivalence problem for GFODDs under the assumption that the size of the intended model is given with the problem, a restriction that guarantees decidability.
no code implementations • 16 Jan 2014 • Saket Joshi, Roni Khardon
Recent work introduced a first order variant of decision diagrams (FODD) and developed a value iteration algorithm for this representation.
no code implementations • NeurIPS 2013 • Aswin Raghavan, Roni Khardon, Alan Fern, Prasad Tadepalli
We address the scalability of symbolic planning under uncertainty with factored states and actions.
no code implementations • 5 Mar 2012 • Yuyang Wang, Roni Khardon, Pavlos Protopapas
The paper applies this framework for data where each task is a phase-shifted periodic time series.