Kalyan Veeramachaneni

Publication Activity (10 Years)

Years Active: 2003-2024
Publications (10 Years): 84

Top Venues

CoRR

DSAA

IEEE BigData

CSCML

Publications

Sarah Alnegheimish, Linh Nguyen, Laure Berti-Équille, Kalyan Veeramachaneni
Large language models can be zero-shot anomaly detectors for time series? CoRR (2024)
Lei Xu, Sarah Alnegheimish, Laure Berti-Équille, Alfredo Cuesta-Infante, Kalyan Veeramachaneni
Single Word Change is All You Need: Designing Attacks and Defenses for Text Classifiers. CoRR (2024)
Alexandra Zytek, Sara Pidò, Kalyan Veeramachaneni
LLMs for XAI: Future Directions for Explaining Explanations. CoRR (2024)
Sarah Alnegheimish, Laure Berti-Équille, Kalyan Veeramachaneni
Making the End-User a Priority in Benchmarking: OrionBench for Unsupervised Time Series Anomaly Detection. CoRR (2023)
Alexandra Zytek, Wei-En Wang, Sofia Koukoura, Kalyan Veeramachaneni
Lessons from Usable ML Deployments and Application to Wind Turbine Monitoring. CoRR (2023)
Alexandra Zytek, Wei-En Wang, Dongyu Liu, Laure Berti-Équille, Kalyan Veeramachaneni
Pyreal: A Framework for Interpretable ML Explanations. CoRR (2023)
Shubhra Kanti Karmaker Santu, Md. Mahadi Hassan, Micah J. Smith, Lei Xu, Chengxiang Zhai, Kalyan Veeramachaneni
AutoML to Date and Beyond: Challenges and Opportunities. ACM Comput. Surv. 54 (8) (2022)
Sarah Alnegheimish, Dongyu Liu, Carles Sala, Laure Berti-Équille, Kalyan Veeramachaneni
Sintel: A Machine Learning Framework to Extract Insights from Signals. CoRR (2022)
Furui Cheng, Dongyu Liu, Fan Du, Yanna Lin, Alexandra Zytek, Haomin Li, Huamin Qu, Kalyan Veeramachaneni
VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models. IEEE Trans. Vis. Comput. Graph. 28 (1) (2022)
Alexandra Zytek, Ignacio Arnaldo, Dongyu Liu, Laure Berti-Équille, Kalyan Veeramachaneni
The Need for Interpretable Features: Motivation and Taxonomy. CoRR (2022)
Lei Xu, Laure Berti-Équille, Alfredo Cuesta-Infante, Kalyan Veeramachaneni
In Situ Augmentation for Defending Against Adversarial Attacks on Text Classifiers. ICONIP (3) (2022)
Lei Xu, Alfredo Cuesta-Infante, Laure Berti-Équille, Kalyan Veeramachaneni
R&R: Metric-guided Adversarial Sentence Generation. AACL/IJCNLP (Findings) (2022)
Alexandra Zytek, Ignacio Arnaldo, Dongyu Liu, Laure Berti-Équille, Kalyan Veeramachaneni
The Need for Interpretable Features: Motivation and Taxonomy. SIGKDD Explor. 24 (1) (2022)
Sarah Alnegheimish, Dongyu Liu, Carles Sala, Laure Berti-Équille, Kalyan Veeramachaneni
Sintel: A Machine Learning Framework to Extract Insights from Signals. SIGMOD Conference (2022)
Kevin Alex Zhang, Neha Patki, Kalyan Veeramachaneni
Sequential Models in the Synthetic Data Vault. CoRR (2022)
Lawrence Wong, Dongyu Liu, Laure Berti-Équille, Sarah Alnegheimish, Kalyan Veeramachaneni
AER: Auto-Encoder with Regression for Time Series Anomaly Detection. CoRR (2022)
Alexandra Zytek, Dongyu Liu, Rhema Vaithianathan, Kalyan Veeramachaneni
Sibyl: Understanding and Addressing the Usability Challenges of Machine Learning In High-Stakes Decision Making. IEEE Trans. Vis. Comput. Graph. 28 (1) (2022)
Dongyu Liu, Sarah Alnegheimish, Alexandra Zytek, Kalyan Veeramachaneni
MTV: Visual Analytics for Detecting, Investigating, and Annotating Anomalies in Multivariate Time Series. Proc. ACM Hum. Comput. Interact. 6 (CSCW1) (2022)
Lawrence Wong, Dongyu Liu, Laure Berti-Équille, Sarah Alnegheimish, Kalyan Veeramachaneni
AER: Auto-Encoder with Regression for Time Series Anomaly Detection. Big Data (2022)
Dongyu Liu, Kalyan Veeramachaneni, Alexander Geiger, Victor O. K. Li, Huamin Qu
AQEyes: Visual Analytics for Anomaly Detection and Examination of Air Quality Data. CoRR (2021)
Alexandra Zytek, Dongyu Liu, Rhema Vaithianathan, Kalyan Veeramachaneni
Sibyl: Explaining Machine Learning Models for High-Stakes Decision Making. CHI Extended Abstracts (2021)
Micah J. Smith, Jürgen Cito, Kelvin Lu, Kalyan Veeramachaneni
Enabling Collaborative Data Science Development with the Ballet Framework. Proc. ACM Hum. Comput. Interact. 5 (CSCW2) (2021)
Yi Sun, Iván Ramírez Díaz, Alfredo Cuesta-Infante, Kalyan Veeramachaneni
Towards Reducing Biases in Combining Multiple Experts Online. IJCAI (2021)
Dongyu Liu, Sarah Alnegheimish, Alexandra Zytek, Kalyan Veeramachaneni
MTV: Visual Analytics for Detecting, Investigating, and Annotating Anomalies in Multivariate Time Series. CoRR (2021)
Furui Cheng, Dongyu Liu, Fan Du, Yanna Lin, Alexandra Zytek, Haomin Li, Huamin Qu, Kalyan Veeramachaneni
VBridge: Connecting the Dots Between Features, Explanations, and Data for Healthcare Models. CoRR (2021)
Alexandra Zytek, Dongyu Liu, Rhema Vaithianathan, Kalyan Veeramachaneni
Understanding the Usability Challenges of Machine Learning In High-Stakes Decision Making. CoRR (2021)
Micah J. Smith, Jürgen Cito, Kalyan Veeramachaneni
Meeting in the notebook: a notebook-based environment for micro-submissions in data science collaborations. CoRR (2021)
Lei Xu, Kalyan Veeramachaneni
Attacking Text Classifiers via Sentence Rewriting Sampler. CoRR (2021)
Alexander Geiger, Dongyu Liu, Sarah Alnegheimish, Alfredo Cuesta-Infante, Kalyan Veeramachaneni
TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks. CoRR (2020)
Sarah Alnegheimish, Najat Alrashed, Faisal Aleissa, Shahad Althobaiti, Dongyu Liu, Mansour Alsaleh, Kalyan Veeramachaneni
Cardea: An Open Automated Machine Learning Framework for Electronic Health Records. CoRR (2020)
Lei Xu, Iván Ramírez, Kalyan Veeramachaneni
Rewriting Meaningful Sentences via Conditional BERT Sampling and an application on fooling text classifiers. CoRR (2020)
Sarah Alnegheimish, Najat Alrashed, Faisal Aleissa, Shahad Althobaiti, Dongyu Liu, Mansour Alsaleh, Kalyan Veeramachaneni
Cardea: An Open Automated Machine Learning Framework for Electronic Health Records. DSAA (2020)
Alexander Geiger, Dongyu Liu, Sarah Alnegheimish, Alfredo Cuesta-Infante, Kalyan Veeramachaneni
TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks. IEEE BigData (2020)
Micah J. Smith, Carles Sala, James Max Kanter, Kalyan Veeramachaneni
The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development. SIGMOD Conference (2020)
Dongyu Liu, Micah J. Smith, Kalyan Veeramachaneni
Understanding User-Bot Interactions for Small-Scale Automation in Open-Source Development. CHI Extended Abstracts (2020)
Micah J. Smith, Jürgen Cito, Kelvin Lu, Kalyan Veeramachaneni
Enabling collaborative data science development with the Ballet framework. CoRR (2020)
Shubhra Kanti Karmaker Santu, Md. Mahadi Hassan, Micah J. Smith, Lei Xu, ChengXiang Zhai, Kalyan Veeramachaneni
A Level-wise Taxonomic Perspective on Automated Machine Learning to Date and Beyond: Challenges and Opportunities. CoRR (2020)
Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, Kalyan Veeramachaneni
Modeling Tabular data using Conditional GAN. CoRR (2019)
Kevin Alex Zhang, Alfredo Cuesta-Infante, Lei Xu, Kalyan Veeramachaneni
SteganoGAN: High Capacity Image Steganography with GANs. CoRR (2019)
Shubhra Kanti Karmaker Santu, Kalyan Veeramachaneni, Chengxiang Zhai
TILM: Neural Language Models with Evolving Topical Influence. CoNLL (2019)
Yi Sun, Alfredo Cuesta-Infante, Kalyan Veeramachaneni
Learning Vine Copula Models for Synthetic Data Generation. AAAI (2019)
Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, Kalyan Veeramachaneni
Modeling Tabular data using Conditional GAN. NeurIPS (2019)
Kevin Alex Zhang, Kalyan Veeramachaneni
Enhancing Image Steganalysis with Adversarially Generated Examples. CSCML (2019)
Kevin Alex Zhang, Lei Xu, Alfredo Cuesta-Infante, Kalyan Veeramachaneni
Robust Invisible Video Watermarking with Attention. CoRR (2019)
Ignacio Arnaldo, Kalyan Veeramachaneni, Mei Lam
eX2: a framework for interactive anomaly detection. IUI Workshops (2019)
Yi Sun, Iván Ramírez, Alfredo Cuesta-Infante, Kalyan Veeramachaneni
Learning Fair Classifiers in Online Stochastic Settings. CoRR (2019)
Qianwen Wang, Yao Ming, Zhihua Jin, Qiaomu Shen, Dongyu Liu, Micah J. Smith, Kalyan Veeramachaneni, Huamin Qu
ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning. CHI (2019)
Ignacio Arnaldo, Kalyan Veeramachaneni
The Holy Grail of "Systems for Machine Learning": Teaming humans and machine learning for detecting cyber threats. SIGKDD Explor. 21 (2) (2019)
Micah J. Smith, Carles Sala, James Max Kanter, Kalyan Veeramachaneni
The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development. CoRR (2019)
Lei Xu, Shubhra Kanti Karmaker Santu, Kalyan Veeramachaneni
MLFriend: Interactive Prediction Task Recommendation for Event-Driven Time-Series Data. CoRR (2019)
Qianwen Wang, Yao Ming, Zhihua Jin, Qiaomu Shen, Dongyu Liu, Micah J. Smith, Kalyan Veeramachaneni, Huamin Qu
ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning. CoRR (2019)
James Max Kanter, Benjamin Schreck, Kalyan Veeramachaneni
Machine learning 2.0 : Engineering Data Driven AI Products. CoRR (2018)
Lei Xu, Kalyan Veeramachaneni
Synthesizing Tabular Data using Generative Adversarial Networks. CoRR (2018)
Ignacio Arnaldo, Ankit Arun, Sumeeth Kyathanahalli, Kalyan Veeramachaneni
Acquire, adapt, and anticipate: continuous learning to block malicious domains. IEEE BigData (2018)
Zara Perumal, Kalyan Veeramachaneni
Towards Building Active Defense Systems for Software Applications. CSCML (2018)
Yi Sun, Alfredo Cuesta-Infante, Kalyan Veeramachaneni
Learning Vine Copula Models For Synthetic Data Generation. CoRR (2018)
Roy Wedge, James Max Kanter, Kalyan Veeramachaneni, Santiago Moral-Rubio, Sergio Iglesias Perez
Solving the False Positives Problem in Fraud Prediction Using Automated Feature Engineering. ECML/PKDD (3) (2018)
Dennis G. Wilson, Silvio Rodrigues, Carlos Segura, Ilya Loshchilov, Frank Hutter, Guillermo López Buenfil, Ahmed Kheiri, Ed Keedwell, Mario Ocampo-Pineda, Ender Özcan, Sergio Iwan Valdez Pea, Brian Goldman, Salvador Botello Rionda, Arturo Hernández Aguirre, Kalyan Veeramachaneni, Sylvain Cussat-Blanc
Summary of evolutionary computation for wind farm layout optimization. GECCO (Companion) (2018)
Gaurav Sheni, Benjamin Schreck, Roy Wedge, James Max Kanter, Kalyan Veeramachaneni
Prediction Factory: automated development and collaborative evaluation of predictive models. CoRR (2018)
Benjamin Schreck, Shankar Mallapur, Sarvesh Damle, Nitin John James, Sanjeev Vohra, Rajendra Prasad, Kalyan Veeramachaneni
Augmenting Software Project Managers with Predictions from Machine Learning. IEEE BigData (2018)
Alec Anderson, Sébastien Dubois, Alfredo Cuesta-Infante, Kalyan Veeramachaneni
Sample, Estimate, Tune: Scaling Bayesian Auto-Tuning of Data Science Pipelines. DSAA (2017)
Micah J. Smith, Roy Wedge, Kalyan Veeramachaneni
FeatureHub: Towards Collaborative Data Science. DSAA (2017)
Roy Wedge, James Max Kanter, Santiago Moral-Rubio, Sergio Iglesias Perez, Kalyan Veeramachaneni
Solving the "false positives" problem in fraud prediction. CoRR (2017)
Ignacio Arnaldo, Alfredo Cuesta-Infante, Ankit Arun, Mei Lam, Costas Bassias, Kalyan Veeramachaneni
Learning Representations for Log Data in Cybersecurity. CSCML (2017)
Thomas Swearingen, Will Drevo, Bennett Cyphers, Alfredo Cuesta-Infante, Arun Ross, Kalyan Veeramachaneni
ATM: A distributed, collaborative, scalable system for automated machine learning. IEEE BigData (2017)
Bennett Cyphers, Kalyan Veeramachaneni
AnonML: Locally Private Machine Learning over a Network of Peers. DSAA (2017)
Yuanzhe Chen, Qing Chen, Mingqian Zhao, Sebastien Boyer, Kalyan Veeramachaneni, Huamin Qu
DropoutSeer: Visualizing learning patterns in Massive Open Online Courses for dropout reasoning and prediction. VAST (2016)
Ben U. Gelman, Matt Revelle, Carlotta Domeniconi, Kalyan Veeramachaneni, Aditya Johri
Acting the Same Differently: A Cross-Course Comparison of User Behavior in MOOCs. EDM (2016)
James Max Kanter, Owen Gillespie, Kalyan Veeramachaneni
Label, Segment, Featurize: A Cross Domain Framework for Prediction Engineering. DSAA (2016)
Benjamin Schreck, Kalyan Veeramachaneni
What Would a Data Scientist Ask? Automatically Formulating and Solving Predictive Problems. DSAA (2016)
Alfredo Cuesta-Infante, Kalyan Veeramachaneni
Markov Switching Copula Models for Longitudinal Data. ICDM Workshops (2016)
Neha Patki, Roy Wedge, Kalyan Veeramachaneni
The Synthetic Data Vault. DSAA (2016)
Kalyan Veeramachaneni, Ignacio Arnaldo, Vamsi Korrapati, Constantinos Bassias, Ke Li
AI^2: Training a Big Data Machine to Defend. BigDataSecurity/HPSC/IDS (2016)
Sebastien Boyer, Kalyan Veeramachaneni
Robust Predictive Models on MOOCs : Transferring Knowledge across Courses. EDM (2016)
Kalyan Veeramachaneni, Ignacio Arnaldo, Owen Derby, Una-May O'Reilly
FlexGP - Cloud-Based Ensemble Learning with Genetic Programming for Large Regression Problems. J. Grid Comput. 13 (3) (2015)
Ignacio Arnaldo, Una-May O'Reilly, Kalyan Veeramachaneni
Building Predictive Models via Feature Synthesis. GECCO (2015)
Yufei Ding, Jason Ansel, Kalyan Veeramachaneni, Xipeng Shen, Una-May O'Reilly, Saman P. Amarasinghe
Autotuning algorithmic choice for input sensitivity. PLDI (2015)
Kalyan Veeramachaneni, Alfredo Cuesta-Infante, Una-May O'Reilly
Copula Graphical Models for Wind Resource Estimation. IJCAI (2015)
Sebastien Boyer, Ben U. Gelman, Benjamin Schreck, Kalyan Veeramachaneni
Data science foundry for MOOCs. DSAA (2015)
Ignacio Arnaldo, Kalyan Veeramachaneni, Andrew Song, Una-May O'Reilly
Bring Your Own Learner: A Cloud-Based, Data-Parallel Commons for Machine Learning. IEEE Comput. Intell. Mag. 10 (1) (2015)
Kalyan Veeramachaneni, Kiarash Adl, Una-May O'Reilly
Feature Factory: Crowd Sourced Feature Discovery. L@S (2015)
James Max Kanter, Kalyan Veeramachaneni
Deep feature synthesis: Towards automating data science endeavors. DSAA (2015)
Franck Dernoncourt, Kalyan Veeramachaneni, Una-May O'Reilly
Gaussian Process-Based Feature Selection for Wavelet Parameters: Predicting Acute Hypotensive Episodes from Physiological Signals. CBMS (2015)
Sebastien Boyer, Kalyan Veeramachaneni
Transfer Learning for Predictive Models in Massive Open Online Courses. AIED (2015)
Pedro Fazenda, Kalyan Veeramachaneni, Pedro U. Lima, Una-May O'Reilly
Using reinforcement learning to optimize occupant comfort and energy usage in HVAC systems. J. Ambient Intell. Smart Environ. 6 (6) (2014)
Ignacio Arnaldo, Kalyan Veeramachaneni, Una-May O'Reilly
Flash: A GP-GPU Ensemble Learning System for Handling Large Datasets. EuroGP (2014)
Kalyan Veeramachaneni, Una-May O'Reilly, Colin Taylor
Towards Feature Engineering at Scale for Data from Massive Open Online Courses. CoRR (2014)
Kalyan Veeramachaneni, Sherif A. Halawa, Franck Dernoncourt, Una-May O'Reilly, Colin Taylor, Chuong B. Do
MOOCdb: Developing Standards and Systems to Support MOOC Data Science. CoRR (2014)
Colin Taylor, Kalyan Veeramachaneni, Una-May O'Reilly
Likely to stop? Predicting Stopout in Massive Open Online Courses. CoRR (2014)
Jason Ansel, Shoaib Kamil, Kalyan Veeramachaneni, Jonathan Ragan-Kelley, Jeffrey Bosboom, Una-May O'Reilly, Saman P. Amarasinghe
OpenTuner: an extensible framework for program autotuning. PACT (2014)
Dennis Wilson, Sylvain Cussat-Blanc, Kalyan Veeramachaneni, Una-May O'Reilly, Hervé Luga
A continuous developmental model for wind farm layout optimization. GECCO (2014)
Monica Vitali, Una-May O'Reilly, Kalyan Veeramachaneni
Modeling Service Execution on Data Centers for Energy Efficiency and Quality of Service Monitoring. SMC (2013)
Kalyan Veeramachaneni, Owen Derby, Dylan Sherry, Una-May O'Reilly
Learning regression ensembles with genetic programming at scale. GECCO (2013)
Erik Hemberg, Constantin Berzan, Kalyan Veeramachaneni, Una-May O'Reilly
Introducing graphical models to analyze genetic programming dynamics. FOGA (2013)
Owen Derby, Kalyan Veeramachaneni, Una-May O'Reilly
Cloud Driven Design of a Distributed Genetic Programming Platform. EvoApplications (2013)
Erik Hemberg, Kalyan Veeramachaneni, Franck Dernoncourt, Mark Wagy, Una-May O'Reilly
Efficient training set use for blood pressure prediction in a large scale learning classifier system. GECCO (Companion) (2013)
Dennis Wilson, Emmanuel Awa, Sylvain Cussat-Blanc, Kalyan Veeramachaneni, Una-May O'Reilly
On learning to generate wind farm layouts. GECCO (2013)
Dennis Wilson, Kalyan Veeramachaneni, Una-May O'Reilly
Cloud Scale Distributed Evolutionary Strategies for High Dimensional Problems. EvoApplications (2013)
Kalyan Veeramachaneni, Franck Dernoncourt, Colin Taylor, Zachary A. Pardos, Una-May O'Reilly
Developing Data Standards and Systems for MOOC Data Science. AIED Workshops (2013)
Erik Hemberg, Kalyan Veeramachaneni, Franck Dernoncourt, Mark Wagy, Una-May O'Reilly
Imprecise selection and fitness approximation in a large-scale evolutionary rule based system for blood pressure prediction. GECCO (Companion) (2013)

Kalyan Veeramachaneni.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Kalyan Veeramachaneni