1 citations · 2 across the 15 of their papers we have counts for
20 papers
Cross-Regional Grapevine Cold Hardiness Prediction via Learned Multimodal Latent Representations
William Solow, Paola Pesantez-Cabrera, Markus Keller +3
Accurate daily predictions of cold hardiness in woody plants are critical in regions where freezing temperatures can damage dormant buds and reduce seasonal yield. Existing biophys…
Learning Action Models with Conditional and Quantified Effects via Uncertainty-Guided Exploration
Jeffrey Jewett, William Solow, Sandhya Saisubramanian
Accurate action models are critical for effective planning. Existing action-model learning methods largely assume simple action representations or become computationally intractabl…
Learning Transferable Latent User Preferences for Human-Aligned Decision Making
Alina Hyk, Sandhya Saisubramanian
Large language models (LLMs) are increasingly used as reasoning modules in many applications, yet they often struggle to produce human-aligned decisions. Human-aligned decision-mak…
A Hybrid Modeling Framework for Crop Prediction Tasks via Dynamic Parameter Calibration and Multi-Task Learning
William Solow, Paola Pesantez-Cabrera, Markus Keller +3
Accurate prediction of crop states (e.g., phenology stages and cold hardiness) is essential for timely farm management decisions such as irrigation, fertilization, and canopy manag…
Multi-Robot Coordination for Planning under Context Uncertainty
Pulkit Rustagi, Kyle Hollins Wray, Sandhya Saisubramanian
Real-world robots often operate in settings where objective priorities depend on the underlying context of operation. When the underlying context is unknown apriori, multiple robot…
Multi-Objective Multi-Agent Path Finding with Lexicographic Cost Preferences
Pulkit Rustagi, Kyle Hollins Wray, Sandhya Saisubramanian
Many real-world scenarios require multiple agents to coordinate in shared environments, while balancing trade-offs between multiple, potentially competing objectives. Current multi…
Calibrating Biophysical Models for Grape Phenology Prediction via Multi-Task Learning
William Solow, Sandhya Saisubramanian
Accurate prediction of grape phenology is essential for timely vineyard management decisions, such as scheduling irrigation and fertilization, to maximize crop yield and quality. W…
Uncovering Systemic and Environment Errors in Autonomous Systems Using Differential Testing
Yashwanthi Anand, Rahil P Mehta, Manish Motwani +1
When an autonomous agent behaves undesirably, including failure to complete a task, it can be difficult to determine whether the behavior is due to a systemic agent error, such as…
Learning with Expert Abstractions for Efficient Multi-Task Continuous Control
Jeff Jewett, Sandhya Saisubramanian
Decision-making in complex, continuous multi-task environments is often hindered by the difficulty of obtaining accurate models for planning and the inefficiency of learning purely…
WOFOSTGym: A Crop Simulator for Learning Annual and Perennial Crop Management Strategies
William Solow, Sandhya Saisubramanian, Alan Fern
We introduce WOFOSTGym, a novel crop simulation environment designed to train reinforcement learning (RL) agents to optimize agromanagement decisions for annual and perennial crops…
Multi-Objective Planning with Contextual Lexicographic Reward Preferences
Pulkit Rustagi, Yashwanthi Anand, Sandhya Saisubramanian
Autonomous agents are often required to plan under multiple objectives whose preference ordering varies based on context. The agent may encounter multiple contexts during its cours…
Adaptive Querying for Reward Learning from Human Feedback
Yashwanthi Anand, Nnamdi Nwagwu, Kevin Sabbe +2
Learning from human feedback is a popular approach to train robots to adapt to user preferences and improve safety. Existing approaches typically consider a single querying (intera…
Mitigating Side Effects in Multi-Agent Systems Using Blame Assignment
Pulkit Rustagi, Sandhya Saisubramanian
When independently trained or designed robots are deployed in a shared environment, their combined actions can lead to unintended negative side effects (NSEs). To ensure safe and e…
Mitigating Negative Side Effects via Environment Shaping
Sandhya Saisubramanian, Shlomo Zilberstein
Agents operating in unstructured environments often produce negative side effects (NSE), which are difficult to identify at design time. While the agent can learn to mitigate the s…
Learning to Generate Fair Clusters from Demonstrations
Sainyam Galhotra, Sandhya Saisubramanian, Shlomo Zilberstein
Fair clustering is the process of grouping similar entities together, while satisfying a mathematically well-defined fairness metric as a constraint. Due to the practical challenge…
Avoiding Negative Side Effects due to Incomplete Knowledge of AI Systems
Sandhya Saisubramanian, Shlomo Zilberstein, Ece Kamar
Autonomous agents acting in the real-world often operate based on models that ignore certain aspects of the environment. The incompleteness of any given model -- handcrafted or mac…
Balancing the Tradeoff Between Clustering Value and Interpretability
Sandhya Saisubramanian, Sainyam Galhotra, Shlomo Zilberstein
Graph clustering groups entities -- the vertices of a graph -- based on their similarity, typically using a complex distance function over a large number of features. Successful in…
Minimizing the Negative Side Effects of Planning with Reduced Models
Sandhya Saisubramanian, Shlomo Zilberstein
Reduced models of large Markov decision processes accelerate planning by considering a subset of outcomes for each state-action pair. This reduction in reachable states leads to re…
Lexicographically Ordered Multi-Objective Clustering
Sainyam Galhotra, Sandhya Saisubramanian, Shlomo Zilberstein
We introduce a rich model for multi-objective clustering with lexicographic ordering over objectives and a slack. The slack denotes the allowed multiplicative deviation from the op…
Planning in Stochastic Environments with Goal Uncertainty
Sandhya Saisubramanian, Kyle Hollins Wray, Luis Pineda +1
We present the Goal Uncertain Stochastic Shortest Path (GUSSP) problem -- a general framework to model path planning and decision making in stochastic environments with goal uncert…