Harsha
harshakokel
Research Scientist
Languages
Top Repositories
An implementation of the paper Kokel et al. ICAPS 2021, RePReL: Integrating Relational Planning and Reinforcement Learning for Effective Abstraction.
Knowledge-intensive Gradient Boosting: A unified framework for learning gradient boosted decision trees for regression and classification tasks while leveraging human advice for achieving better performance.
An extensible benchmark for evaluating large language models on planning
Repository contains implementation of two Relational Q-Learning algorithms.
Differentiable Logic Machines
Repositories
59No description provided.
arXiv LaTeX Cleaner: Easily clean the LaTeX code of your paper to submit to arXiv
Bibtex files with planning-related references
Inspect: A framework for large language model evaluations
An extensible benchmark for evaluating large language models on planning
No description provided.
Harsha's cheatsheet collections
No description provided.
No description provided.
Differentiable Logic Machines
An implementation of the paper Kokel et al. ICAPS 2021, RePReL: Integrating Relational Planning and Reinforcement Learning for Effective Abstraction.
No description provided.
[NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models
Repository contains implementation of two Relational Q-Learning algorithms.
An Analytical Evaluation Board of Multi-turn LLM Agents
A framework for few-shot evaluation of language models.
IBM-Generative-AI is a Python library built on IBM's large language model REST interface to seamlessly integrate and extend this service in Python programs.
This repository contains implementations of various ML/NLP/CV algorithms including Bayesian-Networks, HMM, Means, various gradient descents, loopy belief propagation, etc.
K* search based implementation of top-k and top-quality planners
No description provided.
An app for the Carbon Design System tutorial
PowerLifted Planner
Planning and Reinforcement Learning workshop
No description provided.
A lightweight STRIPS planner written in Python.
Knowledge-intensive Gradient Boosting: A unified framework for learning gradient boosted decision trees for regression and classification tasks while leveraging human advice for achieving better performance.
Config files for my GitHub profile.
Deep relational learning through differentiable logic programming.
No description provided.
No description provided.