Search Results for author: Sanjay Chawla

Found 41 papers, 11 papers with code

Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Survey

no code implementations8 Apr 2024 Naveen Karunanayake, Ravin Gunawardena, Suranga Seneviratne, Sanjay Chawla

Finally, we highlight the limitations of the existing work and propose promising research directions that explore adversarial and OOD inputs within a unified framework.

Adversarial Robustness Anomaly Detection +1

T-RAG: Lessons from the LLM Trenches

no code implementations12 Feb 2024 Masoomali Fatehkia, Ji Kim Lucas, Sanjay Chawla

Finally, we share some lessons learned based on our experiences building an LLM application for real-world use.

Question Answering

ExCeL : Combined Extreme and Collective Logit Information for Enhancing Out-of-Distribution Detection

no code implementations23 Nov 2023 Naveen Karunanayake, Suranga Seneviratne, Sanjay Chawla

Deep learning models often exhibit overconfidence in predicting out-of-distribution (OOD) data, underscoring the crucial role of OOD detection in ensuring reliability in predictions.

Out-of-Distribution Detection

A3T: Accuracy Aware Adversarial Training

no code implementations29 Nov 2022 Enes Altinisik, Safa Messaoud, Husrev Taha Sencar, Sanjay Chawla

Adversarial training has been empirically shown to be more prone to overfitting than standard training.

Interpretable Scientific Discovery with Symbolic Regression: A Review

no code implementations20 Nov 2022 Nour Makke, Sanjay Chawla

Symbolic regression is emerging as a promising machine learning method for learning succinct underlying interpretable mathematical expressions directly from data.

Model Discovery regression +1

Impact of Adversarial Training on Robustness and Generalizability of Language Models

no code implementations10 Nov 2022 Enes Altinisik, Hassan Sajjad, Husrev Taha Sencar, Safa Messaoud, Sanjay Chawla

Specifically, we study the effect of pre-training data augmentation as well as training time input perturbations vs. embedding space perturbations on the robustness and generalization of transformer-based language models.

Data Augmentation

Ten Years after ImageNet: A 360° Perspective on AI

no code implementations1 Oct 2022 Sanjay Chawla, Preslav Nakov, Ahmed Ali, Wendy Hall, Issa Khalil, Xiaosong Ma, Husrev Taha Sencar, Ingmar Weber, Michael Wooldridge, Ting Yu

The rise of attention networks, self-supervised learning, generative modeling, and graph neural networks has widened the application space of AI.

Decision Making Fairness +1

Offline Reinforcement Learning for Road Traffic Control

no code implementations7 Jan 2022 Mayuresh Kunjir, Sanjay Chawla

We build a model-based learning framework which infers a Markov Decision Process (MDP) from a dataset collected using a cyclic traffic signal control policy that is both commonplace and easy to gather.

Offline RL reinforcement-learning +1

Updating Street Maps using Changes Detected in Satellite Imagery

no code implementations13 Oct 2021 Favyen Bastani, Songtao He, Satvat Jagwani, Mohammad Alizadeh, Hari Balakrishnan, Sanjay Chawla, Sam Madden, Mohammad Amin Sadeghi

To address this challenge, much work has studied automatically processing geospatial data sources such as GPS trajectories and satellite images to reduce the cost of maintaining digital maps.

Permutation-Invariant Subgraph Discovery

1 code implementation2 Apr 2021 Raghvendra Mall, Shameem A. Parambath, Han Yufei, Ting Yu, Sanjay Chawla

PSPI can be viewed as a robust formulation of the permutation inference or graph matching, where the objective is to find a permutation between two graphs under the assumption that a set of edges may have undergone a perturbation due to an underlying cause.

Graph Matching

Probabilistic Outlier Detection and Generation

no code implementations22 Dec 2020 Stefano Giovanni Rizzo, Linsey Pang, Yixian Chen, Sanjay Chawla

A new method for outlier detection and generation is introduced by lifting data into the space of probability distributions which are not analytically expressible, but from which samples can be drawn using a neural generator.

Outlier Detection

How-to Present News on Social Media: A Causal Analysis of Editing News Headlines for Boosting User Engagement

1 code implementation17 Sep 2020 Kunwoo Park, Haewoon Kwak, Jisun An, Sanjay Chawla

To reach a broader audience and optimize traffic toward news articles, media outlets commonly run social media accounts and share their content with a short text summary.

Causal Inference counterfactual

Unravelling the Architecture of Membrane Proteins with Conditional Random Fields

no code implementations6 Aug 2020 Lior Lukov, Sanjay Chawla, Wei Liu, Brett Church, Gaurav Pandey

In this paper, we will show that the recently introduced graphical model: Conditional Random Fields (CRF) provides a template to integrate micro-level information about biological entities into a mathematical model to understand their macro-level behavior.

RoadTagger: Robust Road Attribute Inference with Graph Neural Networks

1 code implementation28 Dec 2019 Songtao He, Favyen Bastani, Satvat Jagwani, Edward Park, Sofiane Abbar, Mohammad Alizadeh, Hari Balakrishnan, Sanjay Chawla, Samuel Madden, Mohammad Amin Sadeghi

The usage of graph neural networks allows information propagation on the road network graph and eliminates the receptive field limitation of image classifiers.


Inferring and Improving Street Maps with Data-Driven Automation

no code implementations2 Oct 2019 Favyen Bastani, Songtao He, Satvat Jagwani, Edward Park, Sofiane Abbar, Mohammad Alizadeh, Hari Balakrishnan, Sanjay Chawla, Sam Madden, Mohammad Amin Sadeghi

Through an evaluation on a large-scale dataset including satellite imagery, GPS trajectories, and ground-truth map data in forty cities, we show that Mapster makes automation practical for map editing, and enables the curation of map datasets that are more complete and up-to-date at less cost.

Machine-Assisted Map Editing

no code implementations17 Jun 2019 Favyen Bastani, Songtao He, Sofiane Abbar, Mohammad Alizadeh, Hari Balakrishnan, Sanjay Chawla, Sam Madden

Systems to automatically infer road network graphs from aerial imagery and GPS trajectories have been proposed to improve coverage of road maps.

graph construction

AI-CARGO: A Data-Driven Air-Cargo Revenue Management System

no code implementations22 May 2019 Stefano Giovanni Rizzo, Ji Lucas, Zoi Kaoudi, Jorge-Arnulfo Quiane-Ruiz, Sanjay Chawla

The discrepancy results in sub-optimal and inefficient behavior by both the shipper and the airline resulting in the overall loss of potential revenue for the airline.

Decision Making Management +1

Risk Aware Ranking for Top-$k$ Recommendations

no code implementations17 Mar 2019 Shameem A Puthiya Parambath, Nishant Vijayakumar, Sanjay Chawla

Given an incomplete ratings data over a set of users and items, the preference completion problem aims to estimate a personalized total preference order over a subset of the items.

Deep Learning for Anomaly Detection: A Survey

2 code implementations10 Jan 2019 Raghavendra Chalapathy, Sanjay Chawla

For each category, we present we also present the advantages and limitations and discuss the computational complexity of the techniques in real application domains.

Anomaly Detection

Group Anomaly Detection using Deep Generative Models

1 code implementation13 Apr 2018 Raghavendra Chalapathy, Edward Toth, Sanjay Chawla

Unlike conventional anomaly detection research that focuses on point anomalies, our goal is to detect anomalous collections of individual data points.

Group Anomaly Detection

Anomaly Detection using One-Class Neural Networks

4 code implementations18 Feb 2018 Raghavendra Chalapathy, Aditya Krishna Menon, Sanjay Chawla

We propose a one-class neural network (OC-NN) model to detect anomalies in complex data sets.

Anomaly Detection

Optimizing Non-decomposable Measures with Deep Networks

no code implementations31 Jan 2018 Amartya Sanyal, Pawan Kumar, Purushottam Kar, Sanjay Chawla, Fabrizio Sebastiani

We present a class of algorithms capable of directly training deep neural networks with respect to large families of task-specific performance measures such as the F-measure and the Kullback-Leibler divergence that are structured and non-decomposable.

SAGA: A Submodular Greedy Algorithm For Group Recommendation

no code implementations25 Dec 2017 Shameem A Puthiya Parambath, Nishant Vijayakumar, Sanjay Chawla

In this paper, we propose a unified framework and an algorithm for the problem of group recommendation where a fixed number of items or alternatives can be recommended to a group of users.

SPARK: Static Program Analysis Reasoning and Retrieving Knowledge

no code implementations3 Nov 2017 Wasuwee Sodsong, Bernhard Scholz, Sanjay Chawla

In this work, we present a machine learning pipeline that induces a security analyzer for programs by example.

BIG-bench Machine Learning

Robust, Deep and Inductive Anomaly Detection

5 code implementations22 Apr 2017 Raghavendra Chalapathy, Aditya Krishna Menon, Sanjay Chawla

PCA is a classical statistical technique whose simplicity and maturity has seen it find widespread use as an anomaly detection technique.

Anomaly Detection

Kharita: Robust Map Inference using Graph Spanners

1 code implementation20 Feb 2017 Rade Stanojevic, Sofiane Abbar, Saravanan Thirumuruganathan, Sanjay Chawla, Fethi Filali, Ahid Aleimat

Our algorithms utilize techniques from graph spanners so that they produce maps can effectively handle a wide variety of road and intersection shapes.

Other Computer Science

A Robust Framework for Classifying Evolving Document Streams in an Expert-Machine-Crowd Setting

no code implementations6 Oct 2016 Muhammad Imran, Sanjay Chawla, Carlos Castillo

An emerging challenge in the online classification of social media data streams is to keep the categories used for classification up-to-date.

Constrained Clustering General Classification +2

Online Optimization Methods for the Quantification Problem

no code implementations13 May 2016 Purushottam Kar, Shuai Li, Harikrishna Narasimhan, Sanjay Chawla, Fabrizio Sebastiani

The estimation of class prevalence, i. e., the fraction of a population that belongs to a certain class, is a very useful tool in data analytics and learning, and finds applications in many domains such as sentiment analysis, epidemiology, etc.

Epidemiology Sentiment Analysis

Parameter Database : Data-centric Synchronization for Scalable Machine Learning

no code implementations4 Aug 2015 Naman Goel, Divyakant Agrawal, Sanjay Chawla, Ahmed Elmagarmid

We propose a new data-centric synchronization framework for carrying out of machine learning (ML) tasks in a distributed environment.

BIG-bench Machine Learning

On Integrated Clustering and Outlier Detection

no code implementations NeurIPS 2014 Lionel Ott, Linsey Pang, Fabio T. Ramos, Sanjay Chawla

We model the joint clustering and outlier detection problem using an extension of the facility location formulation.

Clustering Outlier Detection

Classification of Passes in Football Matches using Spatiotemporal Data

no code implementations18 Jul 2014 Michael Horton, Joachim Gudmundsson, Sanjay Chawla, Joël Estephan

Experimental results show that we are able to produce a classifier with 85. 8% accuracy on classifying passes as Good, OK or Bad, and that the predictor variables computed using complex methods from computational geometry are of moderate importance to the learned classifiers.

Classification General Classification +1

Integer Programming Relaxations for Integrated Clustering and Outlier Detection

no code implementations6 Mar 2014 Lionel Ott, Linsey Pang, Fabio Ramos, David Howe, Sanjay Chawla

We present and contrast three relaxations to the integer program formulation: (i) a linear programming formulation (LP) (ii) an extension of affinity propagation to outlier detection (APOC) and (iii) a Lagrangian duality based formulation (LD).

Clustering Outlier Detection

Network Traffic Decomposition for Anomaly Detection

no code implementations2 Mar 2014 Tahereh Babaie, Sanjay Chawla, Sebastien Ardon

In this paper we focus on the detection of network anomalies like Denial of Service (DoS) attacks and port scans in a unified manner.

Anomaly Detection

Sleep Analytics and Online Selective Anomaly Detection

no code implementations2 Mar 2014 Tahereh Babaie, Sanjay Chawla, Romesh Abeysuriya

We introduce a new problem, the Online Selective Anomaly Detection (OSAD), to model a specific scenario emerging from research in sleep science.

Anomaly Detection EEG +2

Feature Graph Architectures

no code implementations15 Dec 2013 Richard Davis, Sanjay Chawla, Philip Leong

In this article we propose feature graph architectures (FGA), which are deep learning systems employing a structured initialisation and training method based on a feature graph which facilitates improved generalisation performance compared with a standard shallow architecture.

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