b'Martin Rinard'

research

∙ 08/15/2023

Correlated vs. Uncorrelated Randomness in Adversarial Congestion Team Games

We consider team zero-sum network congestion games with n senders playin...

0 Idan Orzech, et al. ∙

research

∙ 06/08/2023

Sound Explanation for Trustworthy Machine Learning

We take a formal approach to the explainability problem of machine learn...

0 Kai Jia, et al. ∙

research

∙ 05/18/2023

Evidence of Meaning in Language Models Trained on Programs

We present evidence that language models can learn meaning despite being...

0 Charles Jin, et al. ∙

research

∙ 05/15/2023

Depth-bounded epistemic logic

Epistemic logics model how agents reason about their beliefs and the bel...

0 Farid Arthaud, et al. ∙

research

∙ 04/27/2023

Decentralized Inference via Capability Type Structures in Cooperative Multi-Agent Systems

This work studies the problem of ad hoc teamwork in teams composed of ag...

0 Charles Jin, et al. ∙

research

∙ 04/21/2023

Effective Neural Network L_0 Regularization With BinMask

L_0 regularization of neural networks is a fundamental problem. In addit...

0 Kai Jia, et al. ∙

research

∙ 03/29/2023

Emergence of Locally Suboptimal Behavior in Finitely Repeated Games

We study the emergence of locally suboptimal behavior in finitely repeat...

0 Yichen Yang, et al. ∙

research

∙ 08/09/2022

Mixed Capability Games

We present a new class of strategic games, mixed capability games, as a ...

0 Kai Jia, et al. ∙

research

∙ 05/20/2022

On the Impact of Player Capability on Congestion Games

We study the impact of player capability on social welfare in congestion...

0 Yichen Yang, et al. ∙

research

∙ 08/18/2021

Verifying Low-dimensional Input Neural Networks via Input Quantization

Deep neural networks are an attractive tool for compressing the control ...

0 Kai Jia, et al. ∙

research

∙ 05/08/2021

Provable Guarantees against Data Poisoning Using Self-Expansion and Compatibility

A recent line of work has shown that deep networks are highly susceptibl...

0 Charles Jin, et al. ∙

research

∙ 04/27/2021

Inductive Program Synthesis over Noisy Datasets using Abstraction Refinement Based Optimization

We present a new synthesis algorithm to solve program synthesis over noi...

0 Shivam Handa, et al. ∙

research

∙ 03/08/2021

Program Synthesis Over Noisy Data with Guarantees

We explore and formalize the task of synthesizing programs over noisy da...

0 Shivam Handa, 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

∙ 12/31/2020

Automatic Synthesis of Parallel and Distributed Unix Commands with KumQuat

We present KumQuat, a system for automatically synthesizing parallel and...

0 Nikos Vasilakis, et al. ∙

research

∙ 12/31/2020

An Order-aware Dataflow Model for Extracting Shell Script Parallelism

We present a dataflow model for extracting data parallelism latent in Un...

0 Shivam Handa, et al. ∙

research

∙ 09/22/2020

Inductive Program Synthesis Over Noisy Data

We present a new framework and associated synthesis algorithms for progr...

0 Shivam Handa, et al. ∙

research

∙ 07/23/2020

Dataflow Analysis With Prophecy and History Variables

Leveraging concepts from state machine refinement proofs, we use prophec...

0 Martin Rinard, et al. ∙

research

∙ 05/29/2020

Learning From Context-Agnostic Synthetic Data

We present a new approach for synthesizing training data given only a si...

0 Charles Jin, et al. ∙

research

∙ 05/07/2020

Efficient Exact Verification of Binarized Neural Networks

Concerned with the reliability of neural networks, researchers have deve...

0 Kai Jia, et al. ∙

research

∙ 03/09/2020

Manifold Regularization for Adversarial Robustness

Manifold regularization is a technique that penalizes the complexity of ...

0 Charles Jin, et al. ∙

research

∙ 03/06/2020

Exploiting Verified Neural Networks via Floating Point Numerical Error

We show how to construct adversarial examples for neural networks with e...

0 Kai Jia, et al. ∙

research

∙ 07/15/2019

Characterizing Developer Use of Automatically Generated Patches

We present a study that characterizes the way developers use automatical...

0 José Pablo Cambronero, et al. ∙

research

∙ 07/11/2019

Compositional Inference Metaprogramming with Convergence Guarantees

Inference metaprogramming enables effective probabilistic programming by...

0 Shivam Handa, et al. ∙

research

∙ 06/03/2019

Correctness Verification of Neural Networks

We present the first verification that a neural network produces a corre...

0 Yichen Yang, et al. ∙

research

∙ 07/04/2018

Cimple: Instruction and Memory Level Parallelism

Modern out-of-order processors have increased capacity to exploit instru...

0 Vladimir Kiriansky, et al. ∙

research

∙ 04/06/2018

A Hardware Platform for Efficient Multi-Modal Sensing with Adaptive Approximation

We present Warp, a hardware platform to support research in approximate ...

0 Phillip Stanley-Marbell, et al. ∙

research

∙ 03/22/2018

Incremental Color Quantization for Color-Vision-Deficient Observers Using Mobile Gaming Data

The sizes of compressed images depend on their spatial resolution (numbe...

0 Jose Cambronero, 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

∙ 06/20/2016

Unanimous Prediction for 100 Semantic Mappings

Can we train a system that, on any new input, either says "don't know" o...

0 Fereshte Khani, et al. ∙

Martin Rinard

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