arXiv subjects

Gautam Das

Publications and source records attributed to Gautam Das.

At least 19 recordsLinked to original sources

arXiv2026

Bayesian-Optimized Superpixel-GrabCut for Traceable Optic Disc Segmentation

Optic disc (OD) segmentation is essential for diagnosing ophthalmic pathologies from retinal fundus images. However, prevailing deep learning approaches operate as opaque black boxes, lacking the inference-stage mathematical traceability--a critical requirement for algorithmic auditing and failure analysis in clinical workflows. This paper presents a fully algorithmically traceable and trainable segmentation pipeline that jointly combines superpixel decomposition, hybrid brightness-proximity superpixel scoring, morphological regularization, iterative GrabCut refinement, and elliptical shape fitting. The hyperparameter optimization is formulated as an objective function and solved via Bayesian optimization to eliminate manual parameter tuning. A quantitative evaluation on the Drishti-GS dataset demonstrates that our method achieves a Dice coefficient of 0.9536, matching state-of-the-art performance. By maintaining explicit mathematical transparency across all processing stages, our framework offers a deterministic, traceable alternative to black-box architectures for medical review and debugging.

cs.CV↗

arXiv2026

HNSW with Accuracy Guarantees Using Graph Spanners

Hierarchical Navigable Small World (HNSW) graphs serve as the industry standard due to their logarithmic complexity and strong empirical performance. However, HNSW relies on greedy graph traversal, a heuristic that provides no theoretical guarantees of correctness. In this paper, we propose a novel "Certify-then-Rectify" framework that bridges the gap between the speed of heuristic search and the rigor of exact retrieval. Rather than discarding HNSW, our approach first employs a distribution-free statistical certifier to dynamically evaluate the quality of a standard HNSW search with minimal overhead. If certification indicates that the retrieved neighbors are of low quality, the framework safely escalates to a rigorous exact recovery algorithm. To make this exact recovery computationally feasible, we reinterpret the HNSW graph as a geometric spanner and utilize Extreme Value Theory to stochastically estimate its maximum empirical stretch factor. This allows us to mathematically bound the maximum distance of true nearest neighbors. Extensive evaluations on benchmark datasets demonstrate that our tiered framework delivers the average-case speed of HNSW while ensuring the worst-case correctness of exact search and outperforming other applicable approaches.

cs.DB↗

arXiv2026

A Parameterized YSO Accretion Disk Model with Increasing Accretion Rate: Predicted Outburst Lightcurves

A sub-class among Young Stellar Objects (YSOs), known as FU Ori type stars, undergo sudden rises in luminosity by several orders of magnitude on timescales of a few months to a few years, and decay back to quiescence on timescales of a few decades. Modelling the light curves of these objects is crucial to understanding how different components of these accretion disk systems evolve during outburst. For this purpose, we use a parametric model that couples the stellar photospheric emission, magnetospheric accretion shocks, an irradiated dust disk, and a viscously heated gas disk. We adopt time-dependent accretion rate profiles that mimic the observed morphologies of FU Ori outburst light curves, and we use the accretion model infrastructure to simulate multi-band light curves, as well as color curves. The model enables us to study how different components dominate the flux in each band over the course of an outburst, providing insight into star-magnetosphere-disk interactions throughout the outburst cycle. We find that throughout an accretion outburst, red optical and near-infrared lightcurves generally follow the same or very similar form as the input accretion profile, being sensitive to heating in the accretion shocks and inner gas disk, while mid-infrared lightcurves are more responsive to the location and heating of the innermost dust disk.

astro-ph.SR↗

arXiv2025

Random-Access Ranked Retrieval and Similarity Search

We extend Random Access, a fundamental operation that enables efficient search and exploration algorithms, to the modern interactive data systems based on Ranked Retrieval and Similarity Search, where orderings are dynamically defined over a high-dimensional feature space. This extension enables efficient solutions for a wide range of applications, from data analytics tools and database systems to recommendation systems and machine learning. We formalize the Random-Access Ranked Retrieval (RAR) problem, and extend it to Similarity Search. Our algorithmic innovations include the development of a theoretically efficient algorithm based on geometric arrangements, achieving logarithmic query time. However, this method suffers from exponential space complexity in high dimensions. Therefore, we develop a second class of algorithms based on $\varepsilon$-sampling, which consume a linear space. Since exactly locating the tuple at a specific rank is challenging due to its connection to the range counting problem, we introduce a relaxed variant called $\kappa$-Random-Access Ranked Retrieval, which returns a small subset of size $\kappa$ guaranteed to contain the target tuple. To solve this problem efficiently, we define an intermediate problem, Stripe Range Retrieval (SRR), and design a hierarchical sampling data structure tailored for narrow stripe range queries. Our method achieves practical scalability in both data size and dimensionality. We prove near-optimal bounds on the efficiency of our algorithms and validate their performance through extensive experiments on real and synthetic datasets, demonstrating scalability to millions of tuples and hundreds of dimensions.

cs.DS↗

arXiv2025

FairDeFace: Evaluating the Fairness and Adversarial Robustness of Face Obfuscation Methods

The lack of a common platform and benchmark datasets for evaluating face obfuscation methods has been a challenge, with every method being tested using arbitrary experiments, datasets, and metrics. While prior work has demonstrated that face recognition systems exhibit bias against some demographic groups, there exists a substantial gap in our understanding regarding the fairness of face obfuscation methods. Providing fair face obfuscation methods can ensure equitable protection across diverse demographic groups, especially since they can be used to preserve the privacy of vulnerable populations. To address these gaps, this paper introduces a comprehensive framework, named FairDeFace, designed to assess the adversarial robustness and fairness of face obfuscation methods. The framework introduces a set of modules encompassing data benchmarks, face detection and recognition algorithms, adversarial models, utility detection models, and fairness metrics. FairDeFace serves as a versatile platform where any face obfuscation method can be integrated, allowing for rigorous testing and comparison with other state-of-the-art methods. In its current implementation, FairDeFace incorporates 6 attacks, and several privacy, utility and fairness metrics. Using FairDeFace, and by conducting more than 500 experiments, we evaluated and compared the adversarial robustness of seven face obfuscation methods. This extensive analysis led to many interesting findings both in terms of the degree of robustness of existing methods and their biases against some gender or racial groups. FairDeFace also uses visualization of focused areas for both obfuscation and verification attacks to show not only which areas are mostly changed in the obfuscation process for some demographics, but also why they failed through focus area comparison of obfuscation and verification.

cs.CV↗

arXiv2025

Auditing Yelp's Business Ranking and Review Recommendation Through the Lens of Fairness

Auditing is critical to ensuring the fairness and reliability of decision-making systems. However, auditing a black-box system for bias can be challenging due to the lack of transparency in the model's internal workings. In many web applications, such as Yelp, it is challenging, if not impossible, to manipulate their inputs systematically to identify bias in the output. Yelp connects users and businesses, where users identify new businesses and simultaneously express their experiences through reviews. Yelp recommendation software moderates user-provided content by categorizing it into recommended and not-recommended sections. The recommended reviews, among other attributes, are used by Yelp's ranking algorithm to rank businesses in a neighborhood. Due to Yelp's substantial popularity and its high impact on local businesses' success, understanding the bias of its algorithms is crucial. This data-driven study, for the first time, investigates the bias of Yelp's business ranking and review recommendation system. We examine three hypotheses to assess if Yelp's recommendation software shows bias against reviews of less established users with fewer friends and reviews and if Yelp's business ranking algorithm shows bias against restaurants located in specific neighborhoods, particularly in hotspot regions, with specific demographic compositions. Our findings show that reviews of less-established users are disproportionately categorized as not-recommended. We also find a positive association between restaurants' location in hotspot regions and their average exposure. Furthermore, we observed some cases of severe disparity bias in cities where the hotspots are in neighborhoods with less demographic diversity or higher affluence and education levels.

cs.CY↗

arXiv2024

AXOLOTL: Fairness through Assisted Self-Debiasing of Large Language Model Outputs

Pre-trained Large Language Models (LLMs) have significantly advanced natural language processing capabilities but are susceptible to biases present in their training data, leading to unfair outcomes in various applications. While numerous strategies have been proposed to mitigate bias, they often require extensive computational resources and may compromise model performance. In this work, we introduce AXOLOTL, a novel post-processing framework, which operates agnostically across tasks and models, leveraging public APIs to interact with LLMs without direct access to internal parameters. Through a three-step process resembling zero-shot learning, AXOLOTL identifies biases, proposes resolutions, and guides the model to self-debias its outputs. This approach minimizes computational costs and preserves model performance, making AXOLOTL a promising tool for debiasing LLM outputs with broad applicability and ease of use.

cs.CL↗

arXiv2023

Efficient Strongly Polynomial Algorithms for Quantile Regression

Linear Regression is a seminal technique in statistics and machine learning, where the objective is to build linear predictive models between a response (i.e., dependent) variable and one or more predictor (i.e., independent) variables. In this paper, we revisit the classical technique of Quantile Regression (QR), which is statistically a more robust alternative to the other classical technique of Ordinary Least Square Regression (OLS). However, while there exist efficient algorithms for OLS, almost all of the known results for QR are only weakly polynomial. Towards filling this gap, this paper proposes several efficient strongly polynomial algorithms for QR for various settings. For two dimensional QR, making a connection to the geometric concept of $k$-set, we propose an algorithm with a deterministic worst-case time complexity of $\mathcal{O}(n^{4/3} polylog(n))$ and an expected time complexity of $\mathcal{O}(n^{4/3})$ for the randomized version. We also propose a randomized divide-and-conquer algorithm -- RandomizedQR with an expected time complexity of $\mathcal{O}(n\log^2{(n)})$ for two dimensional QR problem. For the general case with more than two dimensions, our RandomizedQR algorithm has an expected time complexity of $\mathcal{O}(n^{d-1}\log^2{(n)})$.

cs.CG↗

arXiv2019

Approximate Query Processing using Deep Generative Models

Data is generated at an unprecedented rate surpassing our ability to analyze them. The database community has pioneered many novel techniques for Approximate Query Processing (AQP) that could give approximate results in a fraction of time needed for computing exact results. In this work, we explore the usage of deep learning (DL) for answering aggregate queries specifically for interactive applications such as data exploration and visualization. We use deep generative models, an unsupervised learning based approach, to learn the data distribution faithfully such that aggregate queries could be answered approximately by generating samples from the learned model. The model is often compact - few hundred KBs - so that arbitrary AQP queries could be answered on the client side without contacting the database server. Our other contributions include identifying model bias and minimizing it through a rejection sampling based approach and an algorithm to build model ensembles for AQP for improved accuracy. Our extensive experiments show that our proposed approach can provide answers with high accuracy and low latency.

cs.DB↗

arXiv2019

Multi-Attribute Selectivity Estimation Using Deep Learning

Selectivity estimation - the problem of estimating the result size of queries - is a fundamental problem in databases. Accurate estimation of query selectivity involving multiple correlated attributes is especially challenging. Poor cardinality estimates could result in the selection of bad plans by the query optimizer. We investigate the feasibility of using deep learning based approaches for both point and range queries and propose two complementary approaches. Our first approach considers selectivity as an unsupervised deep density estimation problem. We successfully introduce techniques from neural density estimation for this purpose. The key idea is to decompose the joint distribution into a set of tractable conditional probability distributions such that they satisfy the autoregressive property. Our second approach formulates selectivity estimation as a supervised deep learning problem that predicts the selectivity of a given query. We also introduce and address a number of practical challenges arising when adapting deep learning for relational data. These include query/data featurization, incorporating query workload information in a deep learning framework and the dynamic scenario where both data and workload queries could be updated. Our extensive experiments with a special emphasis on queries with a large number of predicates and/or small result sizes demonstrates that our proposed techniques provide fast and accurate selective estimates with minimal space overhead.

cs.DB↗

arXiv2018

QR2: A Third-party Query Reranking Service Over Web Databases

The ranked retrieval model has rapidly become the de-facto way for search query processing in web databases. Despite the extensive efforts on designing better ranking mechanisms, in practice, many such databases fail to address the diverse and sometimes contradicting preferences of users. In this paper, we present QR2, a third-party service that uses nothing but the public search interface of a web database and enables the on-the-fly processing of queries with any user-specified ranking functions, no matter if the ranking function is supported by the database or not.

cs.DB↗

arXiv2018

Malware in the Future? Forecasting of Analyst Detection of Cyber Events

There have been extensive efforts in government, academia, and industry to anticipate, forecast, and mitigate cyber attacks. A common approach is time-series forecasting of cyber attacks based on data from network telescopes, honeypots, and automated intrusion detection/prevention systems. This research has uncovered key insights such as systematicity in cyber attacks. Here, we propose an alternate perspective of this problem by performing forecasting of attacks that are analyst-detected and -verified occurrences of malware. We call these instances of malware cyber event data. Specifically, our dataset was analyst-detected incidents from a large operational Computer Security Service Provider (CSSP) for the U.S. Department of Defense, which rarely relies only on automated systems. Our data set consists of weekly counts of cyber events over approximately seven years. Since all cyber events were validated by analysts, our dataset is unlikely to have false positives which are often endemic in other sources of data. Further, the higher-quality data could be used for a number for resource allocation, estimation of security resources, and the development of effective risk-management strategies. We used a Bayesian State Space Model for forecasting and found that events one week ahead could be predicted. To quantify bursts, we used a Markov model. Our findings of systematicity in analyst-detected cyber attacks are consistent with previous work using other sources. The advanced information provided by a forecast may help with threat awareness by providing a probable value and range for future cyber events one week ahead. Other potential applications for cyber event forecasting include proactive allocation of resources and capabilities for cyber defense (e.g., analyst staffing and sensor configuration) in CSSPs. Enhanced threat awareness may improve cybersecurity.

cs.CR↗

arXiv2018

RRR: Rank-Regret Representative

Selecting the best items in a dataset is a common task in data exploration. However, the concept of "best" lies in the eyes of the beholder: different users may consider different attributes more important, and hence arrive at different rankings. Nevertheless, one can remove "dominated" items and create a "representative" subset of the data set, comprising the "best items" in it. A Pareto-optimal representative is guaranteed to contain the best item of each possible ranking, but it can be almost as big as the full data. Representative can be found if we relax the requirement to include the best item for every possible user, and instead just limit the users' "regret". Existing work defines regret as the loss in score by limiting consideration to the representative instead of the full data set, for any chosen ranking function. However, the score is often not a meaningful number and users may not understand its absolute value. Sometimes small ranges in score can include large fractions of the data set. In contrast, users do understand the notion of rank ordering. Therefore, alternatively, we consider the position of the items in the ranked list for defining the regret and propose the {\em rank-regret representative} as the minimal subset of the data containing at least one of the top-$k$ of any possible ranking function. This problem is NP-complete. We use the geometric interpretation of items to bound their ranks on ranges of functions and to utilize combinatorial geometry notions for developing effective and efficient approximation algorithms for the problem. Experiments on real datasets demonstrate that we can efficiently find small subsets with small rank-regrets.

cs.DB↗

arXiv2018

Designing Fair Ranking Schemes

Items from a database are often ranked based on a combination of multiple criteria. A user may have the flexibility to accept combinations that weigh these criteria differently, within limits. On the other hand, this choice of weights can greatly affect the fairness of the produced ranking. In this paper, we develop a system that helps users choose criterion weights that lead to greater fairness. We consider ranking functions that compute the score of each item as a weighted sum of (numeric) attribute values, and then sort items on their score. Each ranking function can be expressed as a vector of weights, or as a point in a multi-dimensional space. For a broad range of fairness criteria, we show how to efficiently identify regions in this space that satisfy these criteria. Using this identification method, our system is able to tell users whether their proposed ranking function satisfies the desired fairness criteria and, if it does not, to suggest the smallest modification that does. We develop user-controllable approximation that and indexing techniques that are applied during preprocessing, and support sub-second response times during the online phase. Our extensive experiments on real datasets demonstrate that our methods are able to find solutions that satisfy fairness criteria effectively and efficiently.

cs.DB↗

arXiv2017

Assisting Service Providers In Peer-to-peer Marketplaces: Maximizing Gain Over Flexible Attributes

Peer to peer marketplaces such as AirBnB enable transactional exchange of services directly between people. In such platforms, those providing a service (hosts in AirBnB) are faced with various choices. For example in AirBnB, although some amenities in a property (attributes of the property) are fixed, others are relatively flexible and can be provided without significant effort. Providing an amenity is usually associated with a cost. Naturally different sets of amenities may have a different "gains" for a host. Consequently, given a limited budget, deciding which amenities (attributes) to offer is challenging. In this paper, we formally introduce and define the problem of Gain Maximization over Flexible Attributes (GMFA). We first prove that the problem is NP-hard and show that identifying an approximate algorithm with a constant approximate ratio is unlikely. We then provide a practically efficient exact algorithm to the GMFA problem for the general class of monotonic gain functions, which quantify the benefit of sets of attributes. As the next part of our contribution, we focus on the design of a practical gain function for GMFA. We introduce the notion of frequent-item based count (FBC), which utilizes the existing tuples in the database to define the notion of gain, and propose an efficient algorithm for computing it. We present the results of a comprehensive experimental evaluation of the proposed techniques on real dataset from AirBnB and demonstrate the practical relevance and utility of our proposal.

cs.DB↗

arXiv2017

Efficient Computation of Subspace Skyline over Categorical Domains

Platforms such as AirBnB, Zillow, Yelp, and related sites have transformed the way we search for accommodation, restaurants, etc. The underlying datasets in such applications have numerous attributes that are mostly Boolean or Categorical. Discovering the skyline of such datasets over a subset of attributes would identify entries that stand out while enabling numerous applications. There are only a few algorithms designed to compute the skyline over categorical attributes, yet are applicable only when the number of attributes is small. In this paper, we place the problem of skyline discovery over categorical attributes into perspective and design efficient algorithms for two cases. (i) In the absence of indices, we propose two algorithms, ST-S and ST-P, that exploits the categorical characteristics of the datasets, organizing tuples in a tree data structure, supporting efficient dominance tests over the candidate set. (ii) We then consider the existence of widely used precomputed sorted lists. After discussing several approaches, and studying their limitations, we propose TA-SKY, a novel threshold style algorithm that utilizes sorted lists. Moreover, we further optimize TA-SKY and explore its progressive nature, making it suitable for applications with strict interactive requirements. In addition to the extensive theoretical analysis of the proposed algorithms, we conduct a comprehensive experimental evaluation of the combination of real (including the entire AirBnB data collection) and synthetic datasets to study the practicality of the proposed algorithms. The results showcase the superior performance of our techniques, outperforming applicable approaches by orders of magnitude.

cs.DB↗

arXiv2016

Discovering the Skyline of Web Databases

Many web databases are "hidden" behind proprietary search interfaces that enforce the top-$k$ output constraint, i.e., each query returns at most $k$ of all matching tuples, preferentially selected and returned according to a proprietary ranking function. In this paper, we initiate research into the novel problem of skyline discovery over top-$k$ hidden web databases. Since skyline tuples provide critical insights into the database and include the top-ranked tuple for every possible ranking function following the monotonic order of attribute values, skyline discovery from a hidden web database can enable a wide variety of innovative third-party applications over one or multiple web databases. Our research in the paper shows that the critical factor affecting the cost of skyline discovery is the type of search interface controls provided by the website. As such, we develop efficient algorithms for three most popular types, i.e., one-ended range, free range and point predicates, and then combine them to support web databases that feature a mixture of these types. Rigorous theoretical analysis and extensive real-world online and offline experiments demonstrate the effectiveness of our proposed techniques and their superiority over baseline solutions.

cs.DB↗

arXiv2016

Web Item Reviewing Made Easy By Leveraging Available User Feedback

The widespread use of online review sites over the past decade has motivated businesses of all types to possess an expansive arsenal of user feedback to mark their reputation. Though a significant proportion of purchasing decisions are driven by average rating, detailed reviews are critical for activities like buying expensive digital SLR camera. Since writing a detailed review for an item is usually time-consuming, the number of reviews available in the Web is far from many. Given a user and an item our goal is to identify the top-$k$ meaningful phrases/tags to help her review the item easily. We propose general-constrained optimization framework based on three measures - relevance (how well the result set of tags describes an item), coverage (how well the result set of tags covers the different aspects of an item), and polarity (how well sentiment is attached to the result set of tags). By adopting different definitions of coverage, we identify two concrete problem instances that enable a wide range of real-world scenarios. We develop practical algorithms with theoretical bounds to solve these problems efficiently. We conduct experiments on synthetic and real data crawled from the web to validate the effectiveness of our solutions.

cs.SI↗