15 citations · 42 across the 44 of their papers we have counts for
11 papers · 1 filter
In-Context Planning with Latent Temporal Abstractions
Baiting Luo, Yunuo Zhang, Nathaniel S. Keplinger +3
Planning-based reinforcement learning for continuous control is bottlenecked by two practical issues: planning at primitive time scales leads to prohibitive branching and long hori…
Ensemble Prediction of Task Affinity for Efficient Multi-Task Learning
Afiya Ayman, Ayan Mukhopadhyay, Aron Laszka
A fundamental problem in multi-task learning (MTL) is identifying groups of tasks that should be learned together. Since training MTL models for all possible combinations of tasks…
ESCORT: Efficient Stein-variational and Sliced Consistency-Optimized Temporal Belief Representation for POMDPs
Yunuo Zhang, Baiting Luo, Ayan Mukhopadhyay +2
In Partially Observable Markov Decision Processes (POMDPs), maintaining and updating belief distributions over possible underlying states provides a principled way to summarize act…
Scalable Decision-Making in Stochastic Environments through Learned Temporal Abstraction
Baiting Luo, Ava Pettet, Aron Laszka +2
Sequential decision-making in high-dimensional continuous action spaces, particularly in stochastic environments, faces significant computational challenges. We explore this challe…
Reinforcement Learning-based Approach for Vehicle-to-Building Charging with Heterogeneous Agents and Long Term Rewards
Fangqi Liu, Rishav Sen, Jose Paolo Talusan +5
Strategic aggregation of electric vehicle batteries as energy reservoirs can optimize power grid demand, benefiting smart and connected communities, especially large office buildin…
Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing
Amutheezan Sivagnanam, Ava Pettet, Hunter Lee +3
An emergency responder management (ERM) system dispatches responders, such as ambulances, when it receives requests for medical aid. ERM systems can also proactively reposition res…
Task Grouping for Automated Multi-Task Machine Learning via Task Affinity Prediction
Afiya Ayman, Ayan Mukhopadhyay, Aron Laszka
When a number of similar tasks have to be learned simultaneously, multi-task learning (MTL) models can attain significantly higher accuracy than single-task learning (STL) models.…
On Designing Day Ahead and Same Day Ridership Level Prediction Models for City-Scale Transit Networks Using Noisy APC Data
Jose Paolo Talusan, Ayan Mukhopadhyay, Dan Freudberg +1
The ability to accurately predict public transit ridership demand benefits passengers and transit agencies. Agencies will be able to reallocate buses to handle under or over-utiliz…
Generative Anomaly Detection for Time Series Datasets
Zhuangwei Kang, Ayan Mukhopadhyay, Aniruddha Gokhale +2
Traffic congestion anomaly detection is of paramount importance in intelligent traffic systems. The goals of transportation agencies are two-fold: to monitor the general traffic co…
Practitioner-Centric Approach for Early Incident Detection Using Crowdsourced Data for Emergency Services
Yasas Senarath, Ayan Mukhopadhyay, Sayyed Mohsen Vazirizade +3
Emergency response is highly dependent on the time of incident reporting. Unfortunately, the traditional approach to receiving incident reports (e.g., calling 911 in the USA) has t…
Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response Systems
Sayyed Mohsen Vazirizade, Ayan Mukhopadhyay, Geoffrey Pettet +3
Principled decision making in emergency response management necessitates the use of statistical models that predict the spatial-temporal likelihood of incident occurrence. These st…