539 citations · 1.1k across the 21 of their papers we have counts for
7 papers · 1 filter
Persistent Monitoring of Stochastic Spatio-temporal Phenomena with a Small Team of Robots
Sahil Garg, Nora Ayanian
This paper presents a solution for persistent monitoring of real-world stochastic phenomena, where the underlying covariance structure changes sharply across time, using a small nu…
Learning Non-Stationary Space-Time Models for Environmental Monitoring
Sahil Garg, Amarjeet Singh, Fabio Ramos
One of the primary aspects of sustainable development involves accurate understanding and modeling of environmental phenomena. Many of these phenomena exhibit variations in both sp…
Adaptive Sensing for Learning Nonstationary Environment Models
Sahil Garg, Amarjeet Singh, Fabio Ramos
Most environmental phenomena, such as wind profiles, ozone concentration and sunlight distribution under a forest canopy, exhibit nonstationary dynamics i.e. phenomenon variation c…
Building Models for Biopathway Dynamics Using Intrinsic Dimensionality Analysis
Emilia M. Wysocka, Valery Dzutsati, Tirthankar Bandyopadhyay +2
An important task for many if not all the scientific domains is efficient knowledge integration, testing and codification. It is often solved with model construction in a controlla…
Efficiently Learning Nonstationary Gaussian Processes for Real World Impact
Sahil Garg
Most real world phenomena such as sunlight distribution under a forest canopy, minerals concentration, stock valuation, exhibit nonstationary dynamics i.e. phenomenon variation cha…
Modeling Psychotherapy Dialogues with Kernelized Hashcode Representations: A Nonparametric Information-Theoretic Approach
Sahil Garg, Irina Rish, Guillermo Cecchi +5
We propose a novel dialogue modeling framework, the first-ever nonparametric kernel functions based approach for dialogue modeling, which learns kernelized hashcodes as compressed…
Stochastic Learning of Nonstationary Kernels for Natural Language Modeling
Sahil Garg, Greg Ver Steeg, Aram Galstyan
Natural language processing often involves computations with semantic or syntactic graphs to facilitate sophisticated reasoning based on structural relationships. While convolution…