Publications (21)
Boosting AND/OR-Based Computational Protein Design: Dynamic Heuristics and Generalizable UFO
Bobak Pezeshki, Radu Marinescu, Alexander Ihler +1
Scientific computing has experienced a surge empowered by advancements in technologies such as neural networks. However, certain important tasks are less amenable to these technolo…
Learning Infinite RBMs with Frank-Wolfe
Wei Ping, Qiang Liu, Alexander Ihler
In this work, we propose an infinite restricted Boltzmann machine~(RBM), whose maximum likelihood estimation~(MLE) corresponds to a constrained convex optimization. We consider the…
Decomposition Bounds for Marginal MAP
Wei Ping, Qiang Liu, Alexander Ihler
Marginal MAP inference involves making MAP predictions in systems defined with latent variables or missing information. It is significantly more difficult than pure marginalization…
Belief Propagation in Conditional RBMs for Structured Prediction
Wei Ping, Alexander Ihler
Restricted Boltzmann machines~(RBMs) and conditional RBMs~(CRBMs) are popular models for a wide range of applications. In previous work, learning on such models has been dominated…
Pipe Routing with Topology Control for UAV Networks
Shreyas Devaraju, Shivam Garg, Alexander Ihler +1
Routing protocols help in transmitting the sensed data from UAVs monitoring the targets (called target UAVs) to the BS. However, the highly dynamic nature of an autonomous, decentr…
Graph-based Complexity for Causal Effect by Empirical Plug-in
Rina Dechter, Annie Raichev, Alexander Ihler +1
This paper focuses on the computational complexity of computing empirical plug-in estimates for causal effect queries. Given a causal graph and observational data, any identifiable…
Distributed Estimation, Information Loss and Exponential Families
Qiang Liu, Alexander Ihler
Distributed learning of probabilistic models from multiple data repositories with minimum communication is increasingly important. We study a simple communication-efficient learnin…
Marginal Structured SVM with Hidden Variables
Wei Ping, Qiang Liu, Alexander Ihler
In this work, we propose the marginal structured SVM (MSSVM) for structured prediction with hidden variables. MSSVM properly accounts for the uncertainty of hidden variables, and c…
A Deep Q-Learning based, Base-Station Connectivity-Aware, Decentralized Pheromone Mobility Model for Autonomous UAV Networks
Shreyas Devaraju, Alexander Ihler, Sunil Kumar
UAV networks consisting of low SWaP (size, weight, and power), fixed-wing UAVs are used in many applications, including area monitoring, search and rescue, surveillance, and tracki…
A Hybrid Reactive Routing Protocol for Decentralized UAV Networks
Shivam Garg, Alexander Ihler, Elizabeth Serena Bentley +1
Wireless networks consisting of low SWaP, FW-UAVs are used in many applications, such as monitoring, search and surveillance of inaccessible areas. A decentralized and autonomous a…
Design Amortization for Bayesian Optimal Experimental Design
Noble Kennamer, Steven Walton, Alexander Ihler
Bayesian optimal experimental design is a sub-field of statistics focused on developing methods to make efficient use of experimental resources. Any potential design is evaluated i…
Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks
Litian Liang, Yaosheng Xu, Stephen McAleer +4
In temporal-difference reinforcement learning algorithms, variance in value estimation can cause instability and overestimation of the maximal target value. Many algorithms have be…
An Evaluation of Sparse Inverse Covariance Models for Group Functional Connectivity in Molecular Imaging
David B. Keator, Alexander Ihler
Evaluating the functional relationships between brain regions measured with neuroimaging provides insight into how the brain is sharing information at a macro scale. Many functiona…
Multi-Person Pose Estimation via Column Generation
Shaofei Wang, Chong Zhang, Miguel A. Gonzalez-Ballester +2
We study the problem of multi-person pose estimation in natural images. A pose estimate describes the spatial position and identity (head, foot, knee, etc.) of every non-occluded b…
Accurate Link Lifetime Computation in Autonomous Airborne UAV Networks
Shivam Garg, Alexander Ihler, Sunil Kumar
An autonomous airborne network (AN) consists of multiple unmanned aerial vehicles (UAVs), which can self-configure to provide seamless, low-cost and secure connectivity. AN is pref…
Temporal-Difference Value Estimation via Uncertainty-Guided Soft Updates
Litian Liang, Yaosheng Xu, Stephen McAleer +4
Temporal-Difference (TD) learning methods, such as Q-Learning, have proven effective at learning a policy to perform control tasks. One issue with methods like Q-Learning is that t…
Connectivity-Aware Pheromone Mobility Model for Autonomous UAV Networks
Shreyas Devaraju, Alexander Ihler, Sunil Kumar
UAV networks consisting of reduced size, weight, and power (low SWaP) fixed-wing UAVs are used for civilian and military applications such as search and rescue, surveillance, and t…
Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients
Noble Kennamer, Emille E. O. Ishida, Santiago Gonzalez-Gaitan +12
The recent increase in volume and complexity of available astronomical data has led to a wide use of supervised machine learning techniques. Active learning strategies have been pr…
Distributed Parameter Estimation via Pseudo-likelihood
Qiang Liu, Alexander Ihler
Estimating statistical models within sensor networks requires distributed algorithms, in which both data and computation are distributed across the nodes of the network. We propose…
Variational Algorithms for Marginal MAP
Qiang Liu, Alexander Ihler
The marginal maximum a posteriori probability (MAP) estimation problem, which calculates the mode of the marginal posterior distribution of a subset of variables with the remaining…
Estimating Causal Effects from Learned Causal Networks
Anna Raichev, Alexander Ihler, Jin Tian +1
The standard approach to answering an identifiable causal-effect query (e.g., ) when given a causal diagram and observational data is to first generate an estimand, or p…