arXiv subjects

Sanjay Chawla

Publications and source records attributed to Sanjay Chawla.

At least 37 records · Page 2Linked to original sources

arXiv2023

Impact of Adversarial Training on Robustness and Generalizability of Language Models

Adversarial training is widely acknowledged as the most effective defense against adversarial attacks. However, it is also well established that achieving both robustness and generalization in adversarially trained models involves a trade-off. The goal of this work is to provide an in depth comparison of different approaches for adversarial training in language models. 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. Our findings suggest that better robustness can be achieved by pre-training data augmentation or by training with input space perturbation. However, training with embedding space perturbation significantly improves generalization. A linguistic correlation analysis of neurons of the learned models reveals that the improved generalization is due to 'more specialized' neurons. To the best of our knowledge, this is the first work to carry out a deep qualitative analysis of different methods of generating adversarial examples in adversarial training of language models.

cs.CL↗

arXiv2023

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

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. Among various OOD detection approaches, post-hoc detectors have gained significant popularity, primarily due to their ease of use and implementation. However, the effectiveness of most post-hoc OOD detectors has been constrained as they rely solely either on extreme information, such as the maximum logit, or on the collective information (i.e., information spanned across classes or training samples) embedded within the output layer. In this paper, we propose ExCeL that combines both extreme and collective information within the output layer for enhanced accuracy in OOD detection. We leverage the logit of the top predicted class as the extreme information (i.e., the maximum logit), while the collective information is derived in a novel approach that involves assessing the likelihood of other classes appearing in subsequent ranks across various training samples. Our idea is motivated by the observation that, for in-distribution (ID) data, the ranking of classes beyond the predicted class is more deterministic compared to that in OOD data. Experiments conducted on CIFAR100 and ImageNet-200 datasets demonstrate that ExCeL consistently is among the five top-performing methods out of twenty-one existing post-hoc baselines when the joint performance on near-OOD and far-OOD is considered (i.e., in terms of AUROC and FPR95). Furthermore, ExCeL shows the best overall performance across both datasets, unlike other baselines that work best on one dataset but has a performance drop in the other.

cs.LG↗

arXiv2023

Interpretable Scientific Discovery with Symbolic Regression: A Review

Symbolic regression is emerging as a promising machine learning method for learning succinct underlying interpretable mathematical expressions directly from data. Whereas it has been traditionally tackled with genetic programming, it has recently gained a growing interest in deep learning as a data-driven model discovery method, achieving significant advances in various application domains ranging from fundamental to applied sciences. This survey presents a structured and comprehensive overview of symbolic regression methods and discusses their strengths and limitations.

cs.LG↗

arXiv2022

Offline Reinforcement Learning for Road Traffic Control

Traffic signal control is an important problem in urban mobility with a significant potential of economic and environmental impact. While there is a growing interest in Reinforcement Learning (RL) for traffic signal control, the work so far has focussed on learning through simulations which could lead to inaccuracies due to simplifying assumptions. Instead, real experience data on traffic is available and could be exploited at minimal costs. Recent progress in offline or batch RL has enabled just that. Model-based offline RL methods, in particular, have been shown to generalize from the experience data much better than others. 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. The MDP is built with pessimistic costs to manage out-of-distribution scenarios using an adaptive shaping of rewards which is shown to provide better regularization compared to the prior related work in addition to being PAC-optimal. Our model is evaluated on a complex signalized roundabout showing that it is possible to build highly performant traffic control policies in a data efficient manner.

cs.AI↗

arXiv2022

A3T: Accuracy Aware Adversarial Training

Adversarial training has been empirically shown to be more prone to overfitting than standard training. The exact underlying reasons still need to be fully understood. In this paper, we identify one cause of overfitting related to current practices of generating adversarial samples from misclassified samples. To address this, we propose an alternative approach that leverages the misclassified samples to mitigate the overfitting problem. We show that our approach achieves better generalization while having comparable robustness to state-of-the-art adversarial training methods on a wide range of computer vision, natural language processing, and tabular tasks.

cs.LG↗

arXiv2022

Ten Years after ImageNet: A 360° Perspective on AI

It is ten years since neural networks made their spectacular comeback. Prompted by this anniversary, we take a holistic perspective on Artificial Intelligence (AI). Supervised Learning for cognitive tasks is effectively solved - provided we have enough high-quality labeled data. However, deep neural network models are not easily interpretable, and thus the debate between blackbox and whitebox modeling has come to the fore. The rise of attention networks, self-supervised learning, generative modeling, and graph neural networks has widened the application space of AI. Deep Learning has also propelled the return of reinforcement learning as a core building block of autonomous decision making systems. The possible harms made possible by new AI technologies have raised socio-technical issues such as transparency, fairness, and accountability. The dominance of AI by Big-Tech who control talent, computing resources, and most importantly, data may lead to an extreme AI divide. Failure to meet high expectations in high profile, and much heralded flagship projects like self-driving vehicles could trigger another AI winter.

cs.LG↗

arXiv2021

Updating Street Maps using Changes Detected in Satellite Imagery

Accurately maintaining digital street maps is labor-intensive. 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. An end-to-end map update system would first process geospatial data sources to extract insights, and second leverage those insights to update and improve the map. However, prior work largely focuses on the first step of this pipeline: these map extraction methods infer road networks from scratch given geospatial data sources (in effect creating entirely new maps), but do not address the second step of leveraging this extracted information to update the existing digital map data. In this paper, we first explain why current map extraction techniques yield low accuracy when extended to update existing maps. We then propose a novel method that leverages the progression of satellite imagery over time to substantially improve accuracy. Our approach first compares satellite images captured at different times to identify portions of the physical road network that have visibly changed, and then updates the existing map accordingly. We show that our change-based approach reduces map update error rates four-fold.

cs.CV↗

arXiv2021

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

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. Despite its importance of writing a compelling message in sharing articles, the research community does not own a sufficient understanding of what kinds of editing strategies effectively promote audience engagement. In this study, we aim to fill the gap by analyzing media outlets' current practices using a data-driven approach. We first build a parallel corpus of original news articles and their corresponding tweets that eight media outlets shared. Then, we explore how those media edited tweets against original headlines and the effects of such changes. To estimate the effects of editing news headlines for social media sharing in audience engagement, we present a systematic analysis that incorporates a causal inference technique with deep learning; using propensity score matching, it allows for estimating potential (dis-)advantages of an editing style compared to counterfactual cases where a similar news article is shared with a different style. According to the analyses of various editing styles, we report common and differing effects of the styles across the outlets. To understand the effects of various editing styles, media outlets could apply our easy-to-use tool by themselves.

cs.SI↗

arXiv2021

Permutation-Invariant Subgraph Discovery

We introduce Permutation and Structured Perturbation Inference (PSPI), a new problem formulation that abstracts many graph matching tasks that arise in systems biology. 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. For example, suppose there are two gene regulatory networks X and Y from a diseased and normal tissue respectively. Then, the PSPI problem can be used to detect if there has been a structural change between the two networks which can serve as a signature of the disease. Besides the new problem formulation, we propose an ADMM algorithm (STEPD) to solve a relaxed version of the PSPI problem. An extensive case study on comparative gene regulatory networks (GRNs) is used to demonstrate that STEPD is able to accurately infer structured perturbations and thus provides a tool for computational biologists to identify novel prognostic signatures. A spectral analysis confirms that STEPD can recover small clique-like perturbations making it a useful tool for detecting permutation-invariant changes in graphs.

cs.AI↗

arXiv2020

Probabilistic Outlier Detection and Generation

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. Given a mixture of unknown latent inlier and outlier distributions, a Wasserstein double autoencoder is used to both detect and generate inliers and outliers. The proposed method, named WALDO (Wasserstein Autoencoder for Learning the Distribution of Outliers), is evaluated on classical data sets including MNIST, CIFAR10 and KDD99 for detection accuracy and robustness. We give an example of outlier detection on a real retail sales data set and an example of outlier generation for simulating intrusion attacks. However we foresee many application scenarios where WALDO can be used. To the best of our knowledge this is the first work that studies both outlier detection and generation together.

cs.LG↗

arXiv2020

QarSUMO: A Parallel, Congestion-optimized Traffic Simulator

Traffic simulators are important tools for tasks such as urban planning and transportation management. Microscopic simulators allow per-vehicle movement simulation, but require longer simulation time. The simulation overhead is exacerbated when there is traffic congestion and most vehicles move slowly. This in particular hurts the productivity of emerging urban computing studies based on reinforcement learning, where traffic simulations are heavily and repeatedly used for designing policies to optimize traffic related tasks. In this paper, we develop QarSUMO, a parallel, congestion-optimized version of the popular SUMO open-source traffic simulator. QarSUMO performs high-level parallelization on top of SUMO, to utilize powerful multi-core servers and enables future extension to multi-node parallel simulation if necessary. The proposed design, while partly sacrificing speedup, makes QarSUMO compatible with future SUMO improvements. We further contribute such an improvement by modifying the SUMO simulation engine for congestion scenarios where the update computation of consecutive and slow-moving vehicles can be simplified. We evaluate QarSUMO with both real-world and synthetic road network and traffic data, and examine its execution time as well as simulation accuracy relative to the original, sequential SUMO.

cs.DC↗

arXiv2020

RHEEMix in the Data Jungle: A Cost-based Optimizer for Cross-platform Systems

In pursuit of efficient and scalable data analytics, the insight that "one size does not fit all" has given rise to a plethora of specialized data processing platforms and today's complex data analytics are moving beyond the limits of a single platform. In this paper, we present the cost-based optimizer of Rheem, an open-source cross-platform system that copes with these new requirements. The optimizer allocates the subtasks of data analytic tasks to the most suitable platforms. Our main contributions are: (i)~a mechanism based on graph transformations to explore alternative execution strategies; (ii)~a novel graph-based approach to efficiently plan data movement among subtasks and platforms; and (iii)~an efficient plan enumeration algorithm, based on a novel enumeration algebra. We extensively evaluate our optimizer under diverse real tasks. The results show that our optimizer is capable of selecting the most efficient platform combination for a given task, freeing data analysts from the need to choose and orchestrate platforms. In addition, our optimizer allows tasks to run more than one order of magnitude faster by using multiple platforms instead of a single platform.

cs.DB↗

arXiv2020

Unravelling the Architecture of Membrane Proteins with Conditional Random Fields

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. More specifically, we will apply the CRF model to an important classification problem in protein science, namely the secondary structure prediction of proteins based on the observed primary structure. A comparison on benchmark data sets against twenty-eight other methods shows that not only does the CRF model lead to extremely accurate predictions but the modular nature of the model and the freedom to integrate disparate, overlapping and non-independent sources of information, makes the model an extremely versatile tool to potentially solve many other problems in bioinformatics.

cs.LG↗

arXiv2020

Sat2Graph: Road Graph Extraction through Graph-Tensor Encoding

Inferring road graphs from satellite imagery is a challenging computer vision task. Prior solutions fall into two categories: (1) pixel-wise segmentation-based approaches, which predict whether each pixel is on a road, and (2) graph-based approaches, which predict the road graph iteratively. We find that these two approaches have complementary strengths while suffering from their own inherent limitations. In this paper, we propose a new method, Sat2Graph, which combines the advantages of the two prior categories into a unified framework. The key idea in Sat2Graph is a novel encoding scheme, graph-tensor encoding (GTE), which encodes the road graph into a tensor representation. GTE makes it possible to train a simple, non-recurrent, supervised model to predict a rich set of features that capture the graph structure directly from an image. We evaluate Sat2Graph using two large datasets. We find that Sat2Graph surpasses prior methods on two widely used metrics, TOPO and APLS. Furthermore, whereas prior work only infers planar road graphs, our approach is capable of inferring stacked roads (e.g., overpasses), and does so robustly.

cs.CV↗

arXiv2019

RoadTagger: Robust Road Attribute Inference with Graph Neural Networks

Inferring road attributes such as lane count and road type from satellite imagery is challenging. Often, due to the occlusion in satellite imagery and the spatial correlation of road attributes, a road attribute at one position on a road may only be apparent when considering far-away segments of the road. Thus, to robustly infer road attributes, the model must integrate scattered information and capture the spatial correlation of features along roads. Existing solutions that rely on image classifiers fail to capture this correlation, resulting in poor accuracy. We find this failure is caused by a fundamental limitation -- the limited effective receptive field of image classifiers. To overcome this limitation, we propose RoadTagger, an end-to-end architecture which combines both Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs) to infer road attributes. The usage of graph neural networks allows information propagation on the road network graph and eliminates the receptive field limitation of image classifiers. We evaluate RoadTagger on both a large real-world dataset covering 688 km^2 area in 20 U.S. cities and a synthesized micro-dataset. In the evaluation, RoadTagger improves inference accuracy over the CNN image classifier based approaches. RoadTagger also demonstrates strong robustness against different disruptions in the satellite imagery and the ability to learn complicated inductive rules for aggregating scattered information along the road network.

cs.CV↗

arXiv2019

Inferring and Improving Street Maps with Data-Driven Automation

Street maps are a crucial data source that help to inform a wide range of decisions, from navigating a city to disaster relief and urban planning. However, in many parts of the world, street maps are incomplete or lag behind new construction. Editing maps today involves a tedious process of manually tracing and annotating roads, buildings, and other map features. Over the past decade, many automatic map inference systems have been proposed to automatically extract street map data from satellite imagery, aerial imagery, and GPS trajectory datasets. However, automatic map inference has failed to gain traction in practice due to two key limitations: high error rates (low precision), which manifest in noisy inference outputs, and a lack of end-to-end system design to leverage inferred data to update existing street maps. At MIT and QCRI, we have developed a number of algorithms and approaches to address these challenges, which we combined into a new system we call Mapster. Mapster is a human-in-the-loop street map editing system that incorporates three components to robustly accelerate the mapping process over traditional tools and workflows: high-precision automatic map inference, data refinement, and machine-assisted map editing. 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.

cs.CV↗

arXiv2019

Machine-Assisted Map Editing

Mapping road networks today is labor-intensive. As a result, road maps have poor coverage outside urban centers in many countries. Systems to automatically infer road network graphs from aerial imagery and GPS trajectories have been proposed to improve coverage of road maps. However, because of high error rates, these systems have not been adopted by mapping communities. We propose machine-assisted map editing, where automatic map inference is integrated into existing, human-centric map editing workflows. To realize this, we build Machine-Assisted iD (MAiD), where we extend the web-based OpenStreetMap editor, iD, with machine-assistance functionality. We complement MAiD with a novel approach for inferring road topology from aerial imagery that combines the speed of prior segmentation approaches with the accuracy of prior iterative graph construction methods. We design MAiD to tackle the addition of major, arterial roads in regions where existing maps have poor coverage, and the incremental improvement of coverage in regions where major roads are already mapped. We conduct two user studies and find that, when participants are given a fixed time to map roads, they are able to add as much as 3.5x more roads with MAiD.

cs.CV↗

arXiv2019

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

We propose AI-CARGO, a revenue management system for air-cargo that combines machine learning prediction with decision-making using mathematical optimization methods. AI-CARGO addresses a problem that is unique to the air-cargo business, namely the wide discrepancy between the quantity (weight or volume) that a shipper will book and the actual received amount at departure time by the airline. 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. AI-CARGO also includes a data cleaning component to deal with the heterogeneous forms in which booking data is transmitted to the airline cargo system. AI-CARGO is deployed in the production environment of a large commercial airline company. We have validated the benefits of AI-CARGO using real and synthetic datasets. Especially, we have carried out simulations using dynamic programming techniques to elicit the impact on offloading costs and revenue generation of our proposed system. Our results suggest that combining prediction within a decision-making framework can help dramatically to reduce offloading costs and optimize revenue generation.

cs.AI↗