BibSLEIGH — Sanjay

Travelled to:

1 × Taiwan
2 × USA

Collaborated with:

K.Goldberg M.J.Franklin J.Wang L.Sun R.S.Xin M.Laskey T.Kraska R.Fox J.Patel D.Haas E.Wu Carolyn Chen Jessica J. Ji Vatsal Patel Danyal Fer T.Milo Caleb Chuck Ruta Joshi Zisu Dong Sona Dolasia Ashwin Balakrishna Michael Danielczuk M.Zhou A.Cliff B.Nonnecke C.Crittenden K.Uchino J.Mahler S.Sen A.Murali B.Kehoe S.Patil M.Franklin P.Abbeel K.Y.Goldberg

Talks about:

data (6) clean (5) learn (4) demonstr (3) framework (2) process (2) aggress (2) surgic (2) partit (2) stale (2)

Person: Sanjay Krishnan

DBLP: Krishnan:Sanjay

Contributed to:

2015

2014

2017

2018

2019

Wrote 12 papers:

SIGITE-2015-ZhouCKNCUG #collaboration #feedback #scalability #student #using: M-CAFE 1.0: Motivating and Prioritizing Ongoing Student Feedback During MOOCs and Large on-Campus Courses using Collaborative Filtering (MZ, AC, SK, BN, CC, KU, KG), pp. 153–158.
VLDB-2015-HaasKWF0 #framework #named #scalability: Wisteria: Nurturing Scalable Data Cleaning Infrastructure (DH, SK, JW, MJF, EW), pp. 2004–2015.
VLDB-2015-KrishnanWFGK: Stale View Cleaning: Getting Fresh Answers from Stale Materialized Views (SK, JW, MJF, KG, TK), pp. 1370–1381.
CASE-2014-MahlerKLSMKPWFAG #learning #process #using: Learning accurate kinematic control of cable-driven surgical robots using data cleaning and Gaussian Process Regression (JM, SK, ML, SS, AM, BK, SP, JW, MF, PA, KYG), pp. 532–539.
RecSys-2014-KrishnanPFG #bias #learning #recommendation #social: A methodology for learning, analyzing, and mitigating social influence bias in recommender systems (SK, JP, MJF, KG), pp. 137–144.
SIGMOD-2014-SunFKX #clustering #fine-grained: Fine-grained partitioning for aggressive data skipping (LS, MJF, SK, RSX), pp. 1115–1126.
SIGMOD-2014-WangKFGKM #framework #performance #query: A sample-and-clean framework for fast and accurate query processing on dirty data (JW, SK, MJF, KG, TK, TM), pp. 469–480.
VLDB-2014-SunKXF #clustering #framework: A Partitioning Framework for Aggressive Data Skipping (LS, SK, RSX, MJF), pp. 1617–1620.
CASE-2017-ChenKLFG #algorithm #case study #education #user study: An algorithm and user study for teaching bilateral manipulation via iterated best response demonstrations (CC, SK, ML, RF, KG), pp. 151–158.
CASE-2017-ChuckLKJFG #automation #learning #statistics: Statistical data cleaning for deep learning of automation tasks from demonstrations (CC, ML, SK, RJ, RF, KG), pp. 1142–1149.
CASE-2018-JiKPFG #2d #learning: Learning 2D Surgical Camera Motion From Demonstrations (JJJ, SK, VP, DF, KG), pp. 35–42.
CASE-2019-DongKDBDG #automation #linear #multi: Automating Planar Object Singulation by Linear Pushing with Single-point and Multi-point Contacts (ZD, SK, SD, AB, MD, KG), pp. 1429–1436.