Predicting User Experience on Laptops from Hardware Specifications

no code implementations • 14 Feb 2024 • Saswat Padhi, Sunil K. Bhasin, Udaya K. Ammu, Alex Bergman, Allan Knies

Estimating the overall user experience (UX) on a device is a common challenge faced by manufacturers.

Oasis: ILP-Guided Synthesis of Loop Invariants

no code implementations • NeurIPS Workshop CAP 2020 • Sahil Bhatia, Saswat Padhi, Nagarajan Natarajan, Rahul Sharma, Prateek Jain

Automated synthesis of inductive invariants is an important problem in software verification.

On Scaling Data-Driven Loop Invariant Inference

no code implementations • 26 Nov 2019 • Sahil Bhatia, Saswat Padhi, Nagarajan Natarajan, Rahul Sharma, Prateek Jain

Automated synthesis of inductive invariants is an important problem in software verification.

Overfitting in Synthesis: Theory and Practice (Extended Version)

no code implementations • 17 May 2019 • Saswat Padhi, Todd Millstein, Aditya Nori, Rahul Sharma

A standard approach to mitigate overfitting in machine learning is to run multiple learners with varying expressiveness in parallel.

FlashProfile: A Framework for Synthesizing Data Profiles

no code implementations • 17 Sep 2017 • Saswat Padhi, Prateek Jain, Daniel Perelman, Oleksandr Polozov, Sumit Gulwani, Todd Millstein

However, manual inspection of data to identify the different formats is infeasible in standard big-data scenarios.

Clustering

LoopInvGen: A Loop Invariant Generator based on Precondition Inference

no code implementations • 7 Jul 2017 • Saswat Padhi, Rahul Sharma, Todd Millstein

We describe the LoopInvGen tool for generating loop invariants that can provably guarantee correctness of a program with respect to a given specification.

Program Synthesis