Armando Solar-Lezama

research

∙ 06/16/2023

Demystifying GPT Self-Repair for Code Generation

Large Language Models (LLMs) have shown remarkable aptitude in code gene...

0 Theo X. Olausson, et al. ∙

research

∙ 02/03/2023

SPARLING: Learning Latent Representations with Extremely Sparse Activations

Real-world processes often contain intermediate state that can be modele...

0 Kavi Gupta, et al. ∙

research

∙ 11/29/2022

Top-Down Synthesis for Library Learning

This paper introduces corpus-guided top-down synthesis as a mechanism fo...

0 Matthew Bowers, et al. ∙

research

∙ 11/22/2022

Human Evaluation of Text-to-Image Models on a Multi-Task Benchmark

We provide a new multi-task benchmark for evaluating text-to-image model...

0 Vitali Petsiuk, et al. ∙

research

∙ 11/16/2022

LEMMA: Bootstrapping High-Level Mathematical Reasoning with Learned Symbolic Abstractions

Humans tame the complexity of mathematical reasoning by developing hiera...

0 Zhening Li, et al. ∙

research

∙ 10/20/2022

ObSynth: An Interactive Synthesis System for Generating Object Models from Natural Language Specifications

We introduce ObSynth, an interactive system leveraging the domain knowle...

0 Alex Gu, et al. ∙

research

∙ 10/10/2022

Neurosymbolic Programming for Science

Neurosymbolic Programming (NP) techniques have the potential to accelera...

23 Jennifer J. Sun, et al. ∙

research

∙ 06/15/2022

Searching Entangled Program Spaces

Many problem domains, including program synthesis and rewrite-based opti...

0 James Koppel, et al. ∙

research

∙ 06/13/2022

Metric Program Synthesis

We present a new domain-agnostic synthesis technique for generating prog...

0 John Feser, et al. ∙

research

∙ 10/11/2021

Safe Human-Interactive Control via Shielding

Ensuring safety for human-interactive robotics is important due to the p...

0 Jeevana Priya Inala, et al. ∙

research

∙ 02/22/2021

Program Synthesis Guided Reinforcement Learning

A key challenge for reinforcement learning is solving long-horizon plann...

0 Yichen Yang, et al. ∙

research

∙ 01/05/2021

Neurosymbolic Transformers for Multi-Agent Communication

We study the problem of inferring communication structures that can solv...

7 Jeevana Priya Inala, et al. ∙

research

∙ 12/23/2020

Representing Partial Programs with Blended Abstract Semantics

Synthesizing programs from examples requires searching over a vast, comb...

3 Maxwell Nye, et al. ∙

research

∙ 10/10/2020

Automatically Deriving Control-Flow Graph Generators from Operational Semantics

We develop the first theory of control-flow graphs from first principles...

0 James Koppel, et al. ∙

research

∙ 07/09/2020

Program Synthesis with Pragmatic Communication

Program synthesis techniques construct or infer programs from user-provi...

0 Yewen Pu, et al. ∙

research

∙ 07/07/2020

Computer-Aided Personalized Education

The shortage of people trained in STEM fields is becoming acute, and uni...

0 Rajeev Alur, et al. ∙

research

∙ 07/02/2020

Verifiably Safe Exploration for End-to-End Reinforcement Learning

Deploying deep reinforcement learning in safety-critical settings requir...

22 Nathan Hunt, et al. ∙

research

∙ 06/15/2020

DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning

Expert problem-solving is driven by powerful languages for thinking abou...

0 Kevin Ellis, et al. ∙

research

∙ 03/12/2020

Learning Compositional Rules via Neural Program Synthesis

Many aspects of human reasoning, including language, require learning ru...

13 Maxwell I. Nye, et al. ∙

research

∙ 12/29/2019

Synthesizing Queries via Interactive Sketching

We propose a novel approach to program synthesis, focusing on synthesizi...

0 Osbert Bastani, et al. ∙

research

∙ 06/09/2019

Write, Execute, Assess: Program Synthesis with a REPL

We present a neural program synthesis approach integrating components wh...

0 Kevin Ellis, et al. ∙

research

∙ 03/25/2019

The Random Conditional Distribution for Higher-Order Probabilistic Inference

The need to condition distributional properties such as expectation, var...

0 Zenna Tavares, et al. ∙

research

∙ 03/08/2019

Deductive Optimization of Relational Data Storage

Optimizing the physical data storage and retrieval of data are two key d...

0 John K. Feser, et al. ∙

research

∙ 02/17/2019

Learning to Infer Program Sketches

Our goal is to build systems which write code automatically from the kin...

4 Maxwell Nye, et al. ∙

research

∙ 01/16/2019

Soft Constraints for Inference with Declarative Knowledge

We develop a likelihood free inference procedure for conditioning a prob...

2 Zenna Tavares, et al. ∙

research

∙ 12/02/2018

Verifying Fairness Properties via Concentration

As machine learning systems are increasingly used to make real world leg...

0 Osbert Bastani, et al. ∙

research

∙ 07/21/2018

Delta-Decision Procedures for Exists-Forall Problems over the Reals

Solving nonlinear SMT problems over real numbers has wide applications i...

0 Soonho Kong, et al. ∙

research

∙ 05/22/2018

Verifiable Reinforcement Learning via Policy Extraction

While deep reinforcement learning has successfully solved many challengi...

0 Osbert Bastani, et al. ∙

research

∙ 03/20/2018

The Three Pillars of Machine Programming

In this position paper, we describe our vision of the future of machine ...

0 Justin Gottschlich, et al. ∙

research

∙ 03/20/2018

The Three Pillars of Machine-Based Programming

In this position paper, we describe our vision of the future of machine-...

0 Justin Gottschlich, et al. ∙

research

∙ 02/21/2018

Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections

The paper describes a new algorithm to generate minimal, stable, and sym...

0 Xin Zhang, et al. ∙

research

∙ 02/13/2018

REAS: Combining Numerical Optimization with SAT Solving

In this paper, we present ReaS, a technique that combines numerical opti...

0 Jeevana Priya Inala, et al. ∙

research

∙ 11/29/2017

SyGuS-Comp 2017: Results and Analysis

Syntax-Guided Synthesis (SyGuS) is the computational problem of finding ...

0 Rajeev Alur, et al. ∙

research

∙ 11/09/2017

Learning to select examples for program synthesis

Program synthesis is a class of regression problems where one seeks a so...

0 Yewen Pu, et al. ∙

research

∙ 10/28/2017

Capturing the Future by Replaying the Past

Delimited continuations are the mother of all monads! So goes the slogan...

0 James Koppel, et al. ∙

research

∙ 07/30/2017

Learning to Infer Graphics Programs from Hand-Drawn Images

We introduce a model that learns to convert simple hand drawings into gr...

0 Kevin Ellis, et al. ∙

research

∙ 07/14/2017

Incremental Parametric Syntax for Multi-Language Transformation

We present a new approach for building source-to-source transformations ...

0 James Koppel, et al. ∙

research

∙ 04/20/2017

Learning to Acquire Information

We consider the problem of diagnosis where a set of simple observations ...

0 Yewen Pu, et al. ∙

research

∙ 07/11/2016

sk_p: a neural program corrector for MOOCs

We present a novel technique for automatic program correction in MOOCs, ...

0 Yewen Pu, et al. ∙

research

∙ 04/08/2012

Automated Feedback Generation for Introductory Programming Assignments

We present a new method for automatically providing feedback for introdu...

0 Rishabh Singh, et al. ∙

Armando Solar-Lezama

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