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Found 14 papers, 3 papers with code
Synthetic Data Applications in Finance
Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality.
Multi-Modal Financial Time-Series Retrieval Through Latent Space Projections
Moreover, the required storage, computational time, and retrieval complexity to search in the time-series space are often non-trivial.
SafeAR: Safe Algorithmic Recourse by Risk-Aware Policies
However, the uncertainties of feature changes and the risk of higher than average costs in recourse have not been considered.
Methods and Mechanisms for Interactive Novelty Handling in Adversarial Environments
Learning to detect, characterize and accommodate novelties is a challenge that agents operating in open-world domains need to address to be able to guarantee satisfactory task performance.
pyRDDLGym: From RDDL to Gym Environments
We present pyRDDLGym, a Python framework for auto-generation of OpenAI Gym environments from RDDL declerative description.
Computing Policies That Account For The Effects Of Human Agent Uncertainty During Execution In Markov Decision Processes
We present a framework to model the human agent's behavior with respect to state uncertainty, and can be used to compute MDP policies that accounts for these problems.
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 no fixed goal other than to be the last player solvent, There are useful subgoals like monopolizing sets of properties, and developing them.
Long-range connectivity in a superconducting quantum processor using a ring resonator
Qubit coherence and gate fidelity are typically considered the two most important metrics for characterizing a quantum processor.
Quantum Physics
Goal recognition via model-based and model-free techniques
In this paper, we adapt state-of-the-art learning techniques to goal recognition, and compare model-based and model-free approaches in different domains.
Minimizing Robot Navigation-Graph For Position-Based Predictability By Humans
In situations where humans and robots are moving in the same space whilst performing their own tasks, predictable paths taken by mobile robots can not only make the environment feel safer, but humans can also help with the navigation in the space by avoiding path conflicts or not blocking the way.
Embedding Directed Graphs in Potential Fields Using FastMap-D
FastMap is an efficient embedding algorithm that facilitates a geometric interpretation of problems posed on undirected graphs.
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 interaction with the human.
TGE-viz : Transition Graph Embedding for Visualization of Plan Traces and Domains
TGE-viz allows users to visualize and criticize plans more intuitively for mixed-initiative planning.
Recognizing Plans by Learning Embeddings from Observed Action Distributions
The first involves resampling the distribution sequences to single action sequences, from which we could learn an action affinity model based on learned action (word) embeddings for plan recognition.
