Search Results for author:
Found 22 papers, 5 papers with code
WorldCoder, a Model-Based LLM Agent: Building World Models by Writing Code and Interacting with the Environment
We give a model-based agent that builds a Python program representing its knowledge of the world based on its interactions with the environment.
Doing Experiments and Revising Rules with Natural Language and Probabilistic Reasoning
We build a computational model of how humans actively infer hidden rules by doing experiments.
Active Preference Inference using Language Models and Probabilistic Reasoning
To enable this ability for instruction-tuned large language models (LLMs), one may prompt them to ask users questions to infer their preferences, transforming the language models into more robust, interactive systems.
Rapid Motor Adaptation for Robotic Manipulator Arms
Drawing inspiration from RMA in locomotion and in-hand rotation, we use depth perception to develop agents tailored for rapid motor adaptation in a variety of manipulation tasks.
Top-Down Synthesis for Library Learning
This paper introduces corpus-guided top-down synthesis as a mechanism for synthesizing library functions that capture common functionality from a corpus of programs in a domain specific language (DSL).
Toward Trustworthy Neural Program Synthesis
We develop an approach to estimate the probability that a program sampled from a large language model is correct.
From Perception to Programs: Regularize, Overparameterize, and Amortize
Toward combining inductive reasoning with perception abilities, we develop techniques for neurosymbolic program synthesis where perceptual input is first parsed by neural nets into a low-dimensional interpretable representation, which is then processed by a synthesized program.
Efficient Pragmatic Program Synthesis with Informative Specifications
Surprisingly, we find that the synthesizer assuming a factored approximation performs better than a synthesizer assuming an exact joint distribution when evaluated on natural human inputs.
CrossBeam: Learning to Search in Bottom-Up Program Synthesis
Many approaches to program synthesis perform a search within an enormous space of programs to find one that satisfies a given specification.
Scaling Neural Program Synthesis with Distribution-based Search
We investigate how to augment probabilistic and neural program synthesis methods with new search algorithms, proposing a framework called distribution-based search.
Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface
We build on a recent approach, Memoised Wake-Sleep (MWS), which alleviates part of the problem by memoising discrete variables, and extend it to allow for a principled and effective way to handle continuous variables by learning a separate recognition model used for importance-sampling based approximate inference and marginalization.
Leveraging Language to Learn Program Abstractions and Search Heuristics
Inductive program synthesis, or inferring programs from examples of desired behavior, offers a general paradigm for building interpretable, robust, and generalizable machine learning systems.
Learning abstract structure for drawing by efficient motor program induction
Humans flexibly solve new problems that differ qualitatively from those they were trained on.
Program Synthesis with Pragmatic Communication
Given a specification, we score a candidate program both on its consistency with the specification, and also whether a rational speaker would chose this particular specification to communicate that program.
DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning
It builds expertise by creating programming languages for expressing domain concepts, together with neural networks to guide the search for programs within these languages.
Write, Execute, Assess: Program Synthesis with a REPL
We present a neural program synthesis approach integrating components which write, execute, and assess code to navigate the search space of possible programs.
Learning to Infer and Execute 3D Shape Programs
Human perception of 3D shapes goes beyond reconstructing them as a set of points or a composition of geometric primitives: we also effortlessly understand higher-level shape structure such as the repetition and reflective symmetry of object parts.
Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Induction
Successful approaches to program induction require a hand-engineered domain-specific language (DSL), constraining the space of allowed programs and imparting prior knowledge of the domain.
Library Learning for Neurally-Guided Bayesian Program Induction
Successful approaches to program induction require a hand-engineered domain-specific language (DSL), constraining the space of allowed programs and imparting prior knowledge of the domain.
Learning to Infer Graphics Programs from Hand-Drawn Images
These drawing primitives are like a trace of the set of primitive commands issued by a graphics program.
Sampling for Bayesian Program Learning
Towards learning programs from data, we introduce the problem of sampling programs from posterior distributions conditioned on that data.
Unsupervised Learning by Program Synthesis
We introduce an unsupervised learning algorithmthat combines probabilistic modeling with solver-based techniques for program synthesis. We apply our techniques to both a visual learning domain and a language learning problem, showing that our algorithm can learn many visual concepts from only a few examplesand that it can recover some English inflectional morphology. Taken together, these results give both a new approach to unsupervised learning of symbolic compositional structures, and a technique for applying program synthesis tools to noisy data.
