84 citations · 183 across the 39 of their papers we have counts for
4 papers · 1 filter
Neurosymbolic Reinforcement Learning with Formally Verified Exploration
Greg Anderson, Abhinav Verma, Isil Dillig +1
We present Revel, a partially neural reinforcement learning (RL) framework for provably safe exploration in continuous state and action spaces. A key challenge for provably safe de…
Learning Differentiable Programs with Admissible Neural Heuristics
Ameesh Shah, Eric Zhan, Jennifer J. Sun +3
We study the problem of learning differentiable functions expressed as programs in a domain-specific language. Such programmatic models can offer benefits such as composability and…
Meta-Meta Classification for One-Shot Learning
Arkabandhu Chowdhury, Dipak Chaudhari, Swarat Chaudhuri +1
We present a new approach, called meta-meta classification, to learning in small-data settings. In this approach, one uses a large set of learning problems to design an ensemble of…
Searching a Database of Source Codes Using Contextualized Code Search
Rohan Mukherjee, Swarat Chaudhuri, Chris Jermaine
Consider the case where a programmer has written some part of a program, but has left part of the program (such as a method or a function body) incomplete. The goal is to use the c…