Search Results for author:
Found 18 papers, 7 papers with code
Synapse: Learning Preferential Concepts from Visual Demonstrations
This paper addresses the problem of preference learning, which aims to learn user-specific preferences (e. g., "good parking spot", "convenient drop-off location") from visual input.
On a Foundation Model for Operating Systems
This paper lays down the research agenda for a domain-specific foundation model for operating systems (OSes).
Coeditor: Leveraging Contextual Changes for Multi-round Code Auto-editing
In this work, we explore a multi-round code auto-editing setting, aiming to predict edits to a code region based on recent changes within the same codebase.
SatLM: Satisfiability-Aided Language Models Using Declarative Prompting
In this paper, we propose a new satisfiability-aided language modeling (SatLM) approach for improving the reasoning capabilities of LLMs.
ImageEye: Batch Image Processing Using Program Synthesis
This paper presents a new synthesis-based approach for batch image processing.
TypeT5: Seq2seq Type Inference using Static Analysis
There has been growing interest in automatically predicting missing type annotations in programs written in Python and JavaScript.
Guiding Safe Exploration with Weakest Preconditions
In reinforcement learning for safety-critical settings, it is often desirable for the agent to obey safety constraints at all points in time, including during training.
STEADY: Simultaneous State Estimation and Dynamics Learning from Indirect Observations
Accurate kinodynamic models play a crucial role in many robotics applications such as off-road navigation and high-speed driving.
Counterfactual Explanations for Models of Code
We integrate counterfactual explanation generation to models of source code in a real-world setting.
Making Table Understanding Work in Practice
However, we observe that there exists a gap between the performance of these models on these benchmarks and their applicability in practice.
Falx: Synthesis-Powered Visualization Authoring
Modern visualization tools aim to allow data analysts to easily create exploratory visualizations.
Human-Computer Interaction Programming Languages
Optimal Neural Program Synthesis from Multimodal Specifications
Multimodal program synthesis, which leverages different types of user input to synthesize a desired program, is an attractive way to scale program synthesis to challenging settings; however, it requires integrating noisy signals from the user, like natural language, with hard constraints on the program's behavior.
Neurosymbolic Reinforcement Learning with Formally Verified Exploration
We present Revel, a partially neural reinforcement learning (RL) framework for provably safe exploration in continuous state and action spaces.
Benchmarking Multimodal Regex Synthesis with Complex Structures
Existing datasets for regular expression (regex) generation from natural language are limited in complexity; compared to regex tasks that users post on StackOverflow, the regexes in these datasets are simple, and the language used to describe them is not diverse.
LambdaNet: Probabilistic Type Inference using Graph Neural Networks
Given this program abstraction, we then use a graph neural network to propagate information between related type variables and eventually make type predictions.
Sketch-Driven Regular Expression Generation from Natural Language and Examples
Our system achieves state-of-the-art performance on the prior datasets and solves 57% of the real-world dataset, which existing neural systems completely fail on.
Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness
In recent years, the notion of local robustness (or robustness for short) has emerged as a desirable property of deep neural networks.
Formal Specification and Verification of Smart Contracts for Azure Blockchain
In this paper, we describe the formal verification of Smart Contracts offered as part of the Azure Blockchain Content and Samples on github.
Programming Languages F.3.1
