Search Results for author: Sarfraz Khurshid

Found 9 papers, 3 papers with code

SapientML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions

no code implementations18 Feb 2022 Ripon K. Saha, Akira Ura, Sonal Mahajan, Chenguang Zhu, Linyi Li, Yang Hu, Hiroaki Yoshida, Sarfraz Khurshid, Mukul R. Prasad

In this work we propose an AutoML technique SapientML, that can learn from a corpus of existing datasets and their human-written pipelines, and efficiently generate a high-quality pipeline for a predictive task on a new dataset.

AutoML BIG-bench Machine Learning +1

NeuroComb: Improving SAT Solving with Graph Neural Networks

no code implementations26 Oct 2021 Wenxi Wang, Yang Hu, Mohit Tiwari, Sarfraz Khurshid, Kenneth McMillan, Risto Miikkulainen

Aiming to make GNN improvements practical, this paper proposes an approach called NeuroComb, which builds on two insights: (1) predictions of important variables and clauses can be combined with dynamic branching into a more effective hybrid branching strategy, and (2) it is sufficient to query the neural model only once for the predictions before the SAT solving starts.

Programming and Training Rate-Independent Chemical Reaction Networks

no code implementations20 Sep 2021 Marko Vasic, Cameron Chalk, Austin Luchsinger, Sarfraz Khurshid, David Soloveichik

Embedding computation in biochemical environments incompatible with traditional electronics is expected to have wide-ranging impact in synthetic biology, medicine, nanofabrication and other fields.

Translation

Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks

no code implementations ICML 2020 Marko Vasic, Cameron Chalk, Sarfraz Khurshid, David Soloveichik

Embedding computation in molecular contexts incompatible with traditional electronics is expected to have wide ranging impact in synthetic biology, medicine, nanofabrication and other fields.

Transfer Learning Translation

A Study of the Learnability of Relational Properties: Model Counting Meets Machine Learning (MCML)

no code implementations25 Dec 2019 Muhammad Usman, Wenxi Wang, Kaiyuan Wang, Marko Vasic, Haris Vikalo, Sarfraz Khurshid

However, MCML metrics based on model counting show that the performance can degrade substantially when tested against the entire (bounded) input space, indicating the high complexity of precisely learning these properties, and the usefulness of model counting in quantifying the true performance.

BIG-bench Machine Learning

MoET: Interpretable and Verifiable Reinforcement Learning via Mixture of Expert Trees

no code implementations25 Sep 2019 Marko Vasic, Andrija Petrovic, Kaiyuan Wang, Mladen Nikolic, Rishabh Singh, Sarfraz Khurshid

We propose MoET, a more expressive, yet still interpretable model based on Mixture of Experts, consisting of a gating function that partitions the state space, and multiple decision tree experts that specialize on different partitions.

Game of Go Imitation Learning +3

MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning

2 code implementations16 Jun 2019 Marko Vasic, Andrija Petrovic, Kaiyuan Wang, Mladen Nikolic, Rishabh Singh, Sarfraz Khurshid

By training Mo\"ET models using an imitation learning procedure on deep RL agents we outperform the previous state-of-the-art technique based on decision trees while preserving the verifiability of the models.

Game of Go Imitation Learning +4

DeepRoad: GAN-based Metamorphic Autonomous Driving System Testing

1 code implementation7 Feb 2018 Mengshi Zhang, Yuqun Zhang, Lingming Zhang, Cong Liu, Sarfraz Khurshid

In this paper, we propose DeepRoad, an unsupervised framework to automatically generate large amounts of accurate driving scenes to test the consistency of DNN-based autonomous driving systems across different scenes.

Software Engineering

Effectiveness of Anonymization in Double-Blind Review

1 code implementation5 Sep 2017 Claire Le Goues, Yuriy Brun, Sven Apel, Emery Berger, Sarfraz Khurshid, Yannis Smaragdakis

Double-blind review relies on the authors' ability and willingness to effectively anonymize their submissions.

Digital Libraries General Literature Software Engineering

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