56 citations · 138 across the 16 of their papers we have counts for
16 papers
LLMs as Probabilistic Minimally Adequate Teachers for DFA Learning
Lekai Chen, Ashutosh Trivedi, Alvaro Velasquez
The emergence of intelligence in large language models (LLMs) has inspired investigations into their integration into automata learning. This paper introduces the probabilistic Min…
A Survey on Symbolic Knowledge Distillation of Large Language Models
Kamal Acharya, Alvaro Velasquez, Houbing Herbert Song
This survey paper delves into the emerging and critical area of symbolic knowledge distillation in Large Language Models (LLMs). As LLMs like Generative Pre-trained Transformer-3 (…
Bayesian Inverse Reinforcement Learning for Non-Markovian Rewards
Noah Topper, Alvaro Velasquez, George Atia
Inverse reinforcement learning (IRL) is the problem of inferring a reward function from expert behavior. There are several approaches to IRL, but most are designed to learn a Marko…
Hyperdimensional Quantum Factorization
Prathyush Poduval, Zhuowen Zou, Alvaro Velasquez +1
This paper presents a quantum algorithm for efficiently decoding hypervectors, a crucial process in extracting atomic elements from hypervectors - an essential task in Hyperdimensi…
Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning Agents
Yash Shukla, Tanushree Burman, Abhishek Kulkarni +3
Reinforcement Learning (RL) has made significant strides in enabling artificial agents to learn diverse behaviors. However, learning an effective policy often requires a large numb…
A Survey on Verification and Validation, Testing and Evaluations of Neurosymbolic Artificial Intelligence
Justus Renkhoff, Ke Feng, Marc Meier-Doernberg +2
Neurosymbolic artificial intelligence (AI) is an emerging branch of AI that combines the strengths of symbolic AI and sub-symbolic AI. A major drawback of sub-symbolic AI is that i…