Concise and beautiful algorithms written in Julia

Personal notes about scientific and research works on "Decision-Making for Autonomous Driving"

[NeurIPS 2024] Uncertainty of Thoughts: Uncertainty-Aware Planning Enhances Information Seeking in Large Language Models

DecisionProgramming.jl is a Julia package for solving multi-stage decision problems under uncertainty, modeled using influence diagrams. Internally, it relies on mathematical optimization. Decision models can be embedded within other optimization models.

A Python library for Robotic Information Gathering

Implementation of SHARP: Shielding-Aware Robust Planning for Safe and Efficient Human-Robot Interaction - RAL 2022

Implementation of implicit dual control-based active uncertainty learning for human-robot interaction - WAFR 2022 & IJRR 2023

Code for paper: End-to-end Stochastic Optimization with Energy-based Model

Code supporting the paper Collaborative Decision Making Using Action Suggestions.

Introductory (adjustable) robust optimization by matrix computation with box uncertainty and budget of uncertainty.

open-source package for robust optimization

This repository contains glue-code necessary to run dynamic Causal Bayesian optimisation within the Yawning Titan cyber-simulation environment.

Supplemental materials for Karduni et al. (ICWSM 2018) - "Can You Verifi This? Studying Uncertainty and Decision-Making about Misinformation in Visual Analytics"

Final Project for AA 228: Decision-Making under Uncertainty: Decision-Making Towards a Multi-Use Framework for Grid-Scale Energy Storage

Code for "Finding Counterfactually Optimal Action Sequences in Continuous State Spaces", NeurIPS 2023.

Synthesizing safe robot policies in joint physical-belief spaces with deep RL! - CoRL 2023

Code for "On the Within-group Discrimination of Screening Classifiers", ICML 2023

Code for "Human-Aligned Calibration for AI-Assisted Decision Making", NeurIPS 2023

An agent-based model for simulating farmers' decision-making, developed by the AECP Group at ETH Zurich (www.aecp.ethz.ch).

Coding hypothetical graphs to show how accounting for climate change and uncertainty in flood hazard and levee effect in flood exposure would impact flood risk pdfs and expected values.