no code implementations • 29 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.
no code implementations • 20 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.
no code implementations • 10 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 BERT-like language models.
no code implementations • 1 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.
no code implementations • 7 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.
no code implementations • 13 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.
1 code implementation • 2 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.
no code implementations • ICCV 2021 • Songtao He, Mohammad Amin Sadeghi, Sanjay Chawla, Mohammad Alizadeh, Hari Balakrishnan, Samuel Madden
Traffic accidents cost about 3% of the world's GDP and are the leading cause of death in children and young adults.
no code implementations • 1 Jan 2021 • Mohammad Amin Sadeghi, Shameem Parambath, Ji Lucas, Youssef Meguebli, Maguette Toure, Fawaz Al Qahtani, Ting Yu, Sanjay Chawla
Log files are files that record events, messages, or transactions.
no code implementations • 22 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.
1 code implementation • 17 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.
no code implementations • 6 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.
1 code implementation • ECCV 2020 • Songtao He, Favyen Bastani, Satvat Jagwani, Mohammad Alizadeh, Hari Balakrishnan, Sanjay Chawla, Mohamed M. Elshrif, Samuel Madden, Amin Sadeghi
Inferring road graphs from satellite imagery is a challenging computer vision task.
1 code implementation • 28 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.
no code implementations • 2 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.
no code implementations • 17 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.
no code implementations • 22 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.
no code implementations • 17 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.
2 code implementations • 10 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.
1 code implementation • 13 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.
4 code implementations • 18 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.
1 code implementation • CVPR 2018 • Favyen Bastani, Songtao He, Sofiane Abbar, Mohammad Alizadeh, Hari Balakrishnan, Sanjay Chawla, Sam Madden, David DeWitt
Mapping road networks is currently both expensive and labor-intensive.
no code implementations • 31 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.
no code implementations • 25 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.
no code implementations • 3 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.
5 code implementations • 22 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.
1 code implementation • 20 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
no code implementations • 6 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.
no code implementations • 13 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.
no code implementations • NeurIPS 2015 • Jaya Kawale, Hung H. Bui, Branislav Kveton, Long Tran-Thanh, Sanjay Chawla
Matrix factorization (MF) collaborative filtering is an effective and widely used method in recommendation systems.
no code implementations • 4 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.
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.
no code implementations • 18 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.
no code implementations • 6 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).
no code implementations • 2 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.
no code implementations • 2 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.
no code implementations • 15 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.