Sriram Gopalakrishnan | DeepAI

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∙ 08/23/2023

SafeAR: Towards Safer Algorithmic Recourse by Risk-Aware Policies

With the growing use of machine learning (ML) models in critical domains...

0 Haochen Wu, et al. ∙

∙ 02/28/2023

Methods and Mechanisms for Interactive Novelty Handling in Adversarial Environments

Learning to detect, characterize and accommodate novelties is a challeng...

0 Tung Thai, et al. ∙

∙ 02/10/2023

Refined F_5 Algorithms for Ideals of Minors of Square Matrices

We consider the problem of computing a grevlex Gröbner basis for the set...

0 Sriram Gopalakrishnan, et al. ∙

∙ 11/11/2022

pyRDDLGym: From RDDL to Gym Environments

We present pyRDDLGym, a Python framework for auto-generation of OpenAI G...

0 Ayal Taitler, et al. ∙

∙ 09/15/2021

Synthesizing Policies That Account For Human Execution Errors Caused By State-Aliasing In Markov Decision Processes

When humans are given a policy to execute, there can be policy execution...

0 Sriram Gopalakrishnan, et al. ∙

∙ 07/09/2021

Integrating Planning, Execution and Monitoring in the presence of Open World Novelties: Case Study of an Open World Monopoly Solver

The game of monopoly is an adversarial multi-agent domain where there is...

0 Sriram Gopalakrishnan, et al. ∙

∙ 11/03/2020

Goal recognition via model-based and model-free techniques

Goal recognition aims at predicting human intentions from a trace of obs...

0 Daniel Borrajo, et al. ∙

∙ 10/28/2020

Model Minimization For Online Predictability

For humans in a teaming scenario, context switching between reasoning ab...

0 Sriram Gopalakrishnan, et al. ∙

∙ 06/04/2020

Embedding Directed Graphs in Potential Fields Using FastMap-D

Embedding undirected graphs in a Euclidean space has many computational ...

5 Sriram Gopalakrishnan, et al. ∙

∙ 02/15/2020

Let Me At Least Learn What You Really Like: Dealing With Noisy Humans When Learning Preferences

Learning the preferences of a human improves the quality of the interact...

0 Sriram Gopalakrishnan, et al. ∙

∙ 11/24/2018

TGE-viz : Transition Graph Embedding for Visualization of Plan Traces and Domains

Existing work for plan trace visualization in automated planning uses pi...

0 Sriram Gopalakrishnan, et al. ∙

∙ 12/05/2017

Recognizing Plans by Learning Embeddings from Observed Action Distributions

Recent advances in visual activity recognition have raised the possibili...

0 Yantian Zha, et al. ∙

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