research
∙
08/18/2023
Learning Reward Machines through Preference Queries over Sequences
Reward machines have shown great promise at capturing non-Markovian rewa...
share
research
∙
07/05/2023
Deductive Additivity for Planning of Natural Language Proofs
Current natural language systems designed for multi-step claim validatio...
research
∙
06/11/2023
A Probabilistic Framework for Modular Continual Learning
Modular approaches, which use a different composition of modules for eac...
research
∙
05/25/2023
Tuning Models of Code with Compiler-Generated Reinforcement Learning Feedback
Large Language Models (LLMs) pre-trained on code have recently emerged a...
research
∙
02/08/2023
Symbolic Quantum Simulation with Quasimodo
The simulation of quantum circuits on classical computers is an importan...
research
∙
01/26/2023
Policy Optimization with Robustness Certificates
We present a policy optimization framework in which the learned policy c...
research
∙
11/13/2022
CFLOBDDs: Context-Free-Language Ordered Binary Decision Diagrams
This paper presents a new compressed representation of Boolean functions...
research
∙
11/01/2022
Natural Language Deduction with Incomplete Information
A growing body of work studies how to answer a question or verify a clai...
research
∙
10/10/2022
Neurosymbolic Programming for Science
Neurosymbolic Programming (NP) techniques have the potential to accelera...
research
∙
09/28/2022
Guiding Safe Exploration with Weakest Preconditions
In reinforcement learning for safety-critical settings, it is often desi...
research
∙
06/20/2022
Policy Optimization with Linear Temporal Logic Constraints
We study the problem of policy optimization (PO) with linear temporal lo...
research
∙
03/15/2022
Safe Neurosymbolic Learning with Differentiable Symbolic Execution
We study the problem of learning worst-case-safe parameters for programs...
research
∙
01/16/2022
Natural Language Deduction through Search over Statement Compositions
In settings from fact-checking to question answering, we frequently want...
research
∙
10/26/2021
Neural Program Generation Modulo Static Analysis
State-of-the-art neural models of source code tend to be evaluated on th...
research
∙
07/28/2021
Unsupervised Learning of Neurosymbolic Encoders
We present a framework for the unsupervised learning of neurosymbolic en...
research
∙
07/13/2021
Bottom-up Synthesis of Recursive Functional Programs using Angelic Execution
We present a novel bottom-up method for the synthesis of functional recu...
research
∙
06/11/2021
Interpreting Expert Annotation Differences in Animal Behavior
Hand-annotated data can vary due to factors such as subjective differenc...
research
∙
04/18/2021
Flexible Operations for Natural Language Deduction
An interpretable system for complex, open-domain reasoning needs an inte...
research
∙
04/14/2021
OneVision: Centralized to Distributed Controller Synthesis with Delay Compensation
We propose a new algorithm to simplify the controller development for di...
research
∙
09/26/2020
Neurosymbolic Reinforcement Learning with Formally Verified Exploration
We present Revel, a partially neural reinforcement learning (RL) framewo...
research
∙
07/23/2020
Learning Differentiable Programs with Admissible Neural Heuristics
We study the problem of learning differentiable functions expressed as p...
research
∙
04/17/2020
Meta-Meta-Classification for One-Shot Learning
We present a new approach, called meta-meta-classification, to learning ...
research
∙
01/10/2020
Searching a Database of Source Codes Using Contextualized Code Search
We assume a database containing a large set of program source codes and ...
research
∙
07/11/2019
Imitation-Projected Policy Gradient for Programmatic Reinforcement Learning
We present Imitation-Projected Policy Gradient (IPPG), an algorithmic fr...
research
∙
07/11/2019
Imitation-Projected Programmatic Reinforcement Learning
We study the problem of programmatic reinforcement learning, in which po...
research
∙
05/14/2019
Control Regularization for Reduced Variance Reinforcement Learning
Dealing with high variance is a significant challenge in model-free rein...
research
∙
04/22/2019
Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness
In recent years, the notion of local robustness (or robustness for short...
research
∙
04/22/2019
Optimization + Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness
In recent years, the notion of local robustness (or robustness for short...
research
∙
02/27/2019
Representing Formal Languages: A Comparison Between Finite Automata and Recurrent Neural Networks
We investigate the internal representations that a recurrent neural netw...
research
∙
12/17/2018
Comparator automata in quantitative verification
The notion of comparison between system runs is fundamental in formal ve...
research
∙
04/06/2018
Programmatically Interpretable Reinforcement Learning
We study the problem of generating interpretable and verifiable policies...
research
∙
03/31/2018
Synthesis of Differentiable Functional Programs for Lifelong Learning
We present a neurosymbolic approach to the lifelong learning of algorith...
research
∙
01/29/2018
Bounded Policy Synthesis for POMDPs with Safe-Reachability Objectives
Planning robust executions under uncertainty is a fundamental challenge ...
research
∙
05/25/2017
Neural Attribute Machines for Program Generation
Recurrent neural networks have achieved remarkable success at generating...
research
∙
05/25/2017