Search Results for author: Kalyan Veeramachaneni

Found 33 papers, 19 papers with code

Single Word Change is All You Need: Designing Attacks and Defenses for Text Classifiers

no code implementations30 Jan 2024 Lei Xu, Sarah Alnegheimish, Laure Berti-Equille, Alfredo Cuesta-Infante, Kalyan Veeramachaneni

Experimental results on 4 datasets and BERT and distilBERT classifiers show that SP-Defense improves \r{ho} by 14. 6% and 13. 9% and decreases the attack success rate of SP-Attack by 30. 4% and 21. 2% on two classifiers respectively, and decreases the attack success rate of existing attack methods that involve multiple-word perturbations.

Data Augmentation Sentence +2

Lessons from Usable ML Deployments and Application to Wind Turbine Monitoring

no code implementations5 Dec 2023 Alexandra Zytek, Wei-En Wang, Sofia Koukoura, Kalyan Veeramachaneni

Through the applications of our lessons to this task, we hope to demonstrate the potential real-world impact of usable ML in the renewable energy domain.

Decision Making

Making the End-User a Priority in Benchmarking: OrionBench for Unsupervised Time Series Anomaly Detection

1 code implementation26 Oct 2023 Sarah Alnegheimish, Laure Berti-Equille, Kalyan Veeramachaneni

The framework provides universal abstractions to represent models, extensibility to add new pipelines and datasets, hyperparameter standardization, pipeline verification, and frequent releases with published benchmarks.

Anomaly Detection Benchmarking +2

AER: Auto-Encoder with Regression for Time Series Anomaly Detection

3 code implementations27 Dec 2022 Lawrence Wong, Dongyu Liu, Laure Berti-Equille, Sarah Alnegheimish, Kalyan Veeramachaneni

We then propose AER (Auto-encoder with Regression), a joint model that combines a vanilla auto-encoder and an LSTM regressor to incorporate the successes and address the limitations of each method.

Anomaly Detection Benchmarking +3

Sequential Models in the Synthetic Data Vault

1 code implementation28 Jul 2022 Kevin Zhang, Neha Patki, Kalyan Veeramachaneni

After building the Sequential SDV, we used it to generate synthetic data and compared its quality against an existing, non-sequential generative adversarial network based model called CTGAN.

Generative Adversarial Network

The Need for Interpretable Features: Motivation and Taxonomy

no code implementations23 Feb 2022 Alexandra Zytek, Ignacio Arnaldo, Dongyu Liu, Laure Berti-Equille, Kalyan Veeramachaneni

Through extensive experience developing and explaining machine learning (ML) applications for real-world domains, we have learned that ML models are only as interpretable as their features.

Decision Making

VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models

1 code implementation4 Aug 2021 Furui Cheng, Dongyu Liu, Fan Du, Yanna Lin, Alexandra Zytek, Haomin Li, Huamin Qu, Kalyan Veeramachaneni

Machine learning (ML) is increasingly applied to Electronic Health Records (EHRs) to solve clinical prediction tasks.

Decision Making

R&R: Metric-guided Adversarial Sentence Generation

1 code implementation17 Apr 2021 Lei Xu, Alfredo Cuesta-Infante, Laure Berti-Equille, Kalyan Veeramachaneni

It improves the quality of adversarial examples by optimizing a critique score which combines the fluency, similarity, and misclassification metrics.

Adversarial Attack General Classification +6

Enabling Collaborative Data Science Development with the Ballet Framework

3 code implementations14 Dec 2020 Micah J. Smith, Jürgen Cito, Kelvin Lu, Kalyan Veeramachaneni

While the open-source software development model has led to successful large-scale collaborations in building software systems, data science projects are frequently developed by individuals or small teams.

Feature Engineering

Rewriting Meaningful Sentences via Conditional BERT Sampling and an application on fooling text classifiers

no code implementations22 Oct 2020 Lei Xu, Ivan Ramirez, Kalyan Veeramachaneni

Most adversarial attack methods that are designed to deceive a text classifier change the text classifier's prediction by modifying a few words or characters.

Adversarial Attack Semantic Similarity +2

AutoML to Date and Beyond: Challenges and Opportunities

no code implementations21 Oct 2020 Shubhra Kanti Karmaker Santu, Md. Mahadi Hassan, Micah J. Smith, Lei Xu, ChengXiang Zhai, Kalyan Veeramachaneni

AutoML tools aim to make machine learning accessible for non-machine learning experts (domain experts), to improve the efficiency of machine learning, and to accelerate machine learning research.

AutoML BIG-bench Machine Learning

TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks

5 code implementations16 Sep 2020 Alexander Geiger, Dongyu Liu, Sarah Alnegheimish, Alfredo Cuesta-Infante, Kalyan Veeramachaneni

However, detecting anomalies in time series data is particularly challenging due to the vague definition of anomalies and said data's frequent lack of labels and highly complex temporal correlations.

Benchmarking Time Series +2

TILM: Neural Language Models with Evolving Topical Influence

no code implementations CONLL 2019 Shubhra Kanti Karmaker Santu, Kalyan Veeramachaneni, ChengXiang Zhai

Specifically, we propose a novel language model called Topical Influence Language Model (TILM), which is a novel extension of a neural language model to capture the influences on the contents in one text stream by the evolving topics in another related (or possibly same) text stream.

Language Modelling

Adversarially learned anomaly detection for time series data

no code implementations25 Sep 2019 Alexander Geiger, Alfredo Cuesta-Infante, Kalyan Veeramachaneni

Based on the recent developments in adversarially learned models, we propose a new approach for anomaly detection in time series data.

Anomaly Detection Time Series +1

Robust Invisible Video Watermarking with Attention

2 code implementations3 Sep 2019 Kevin Alex Zhang, Lei Xu, Alfredo Cuesta-Infante, Kalyan Veeramachaneni

The goal of video watermarking is to embed a message within a video file in a way such that it minimally impacts the viewing experience but can be recovered even if the video is redistributed and modified, allowing media producers to assert ownership over their content.

Towards Reducing Biases in Combining Multiple Experts Online

no code implementations19 Aug 2019 Yi Sun, Ivan Ramirez, Alfredo Cuesta-Infante, Kalyan Veeramachaneni

In many real life situations, including job and loan applications, gatekeepers must make justified and fair real-time decisions about a person's fitness for a particular opportunity.

Decision Making Fairness

The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development

8 code implementations22 May 2019 Micah J. Smith, Carles Sala, James Max Kanter, Kalyan Veeramachaneni

To address these problems, we introduce the Machine Learning Bazaar, a new framework for developing machine learning and automated machine learning software systems.

AutoML Bayesian Optimization +1

Learning Vine Copula Models For Synthetic Data Generation

no code implementations4 Dec 2018 Yi Sun, Alfredo Cuesta-Infante, Kalyan Veeramachaneni

Moreover, we demonstrate that the model is able to generate high-quality samples in a variety of applications, making it a good candidate for synthetic data generation.

Model Selection Reinforcement Learning (RL) +1

Prediction Factory: automated development and collaborative evaluation of predictive models

no code implementations29 Nov 2018 Gaurav Sheni, Benjamin Schreck, Roy Wedge, James Max Kanter, Kalyan Veeramachaneni

In a head-to-head trial, reports generated utilizing full data science automation interface reports were funded 57. 5% of the time, while the ones that used baseline automation were only funded 42. 5% of the time.

Synthesizing Tabular Data using Generative Adversarial Networks

7 code implementations27 Nov 2018 Lei Xu, Kalyan Veeramachaneni

Generative adversarial networks (GANs) implicitly learn the probability distribution of a dataset and can draw samples from the distribution.

Generative Adversarial Network

Machine learning 2.0 : Engineering Data Driven AI Products

1 code implementation1 Jul 2018 James Max Kanter, Benjamin Schreck, Kalyan Veeramachaneni

ML 2. 0: In this paper, we propose a paradigm shift from the current practice of creating machine learning models - which requires months-long discovery, exploration and "feasibility report" generation, followed by re-engineering for deployment - in favor of a rapid, 8-week process of development, understanding, validation and deployment that can executed by developers or subject matter experts (non-ML experts) using reusable APIs.

BIG-bench Machine Learning

Solving the "false positives" problem in fraud prediction

1 code implementation20 Oct 2017 Roy Wedge, James Max Kanter, Santiago Moral Rubio, Sergio Iglesias Perez, Kalyan Veeramachaneni

In this paper, we present an automated feature engineering based approach to dramatically reduce false positives in fraud prediction.

Automated Feature Engineering Feature Engineering +1

Deep Feature Synthesis: Towards Automating Data Science Endeavors

1 code implementation DSAA 2015 2015 James Max Kanter, Kalyan Veeramachaneni

In this paper, we develop the Data Science Machine, which is able to derive predictive models from raw data automatically.

Automated Feature Engineering

Likely to stop? Predicting Stopout in Massive Open Online Courses

no code implementations14 Aug 2014 Colin Taylor, Kalyan Veeramachaneni, Una-May O'Reilly

Even with more difficult prediction problems, such as predicting stop out at the end of the course with only one weeks' data, the models attained AUCs of 0. 7.

Feature Importance

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