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Researcher profile

Subhabrata Majumdar

Subhabrata Majumdar contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Machine Learningcs.CYMethodologyArtificial Intelligenceastro-ph.COApplicationsastro-ph.GAastro-ph.HECryptography and Securitymath.STSocial and Information NetworksStatistics Theory

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Published work

10 published item(s)

preprint2026arXiv

Consistency as a Testable Property: Statistical Methods to Evaluate AI Agent Reliability

This paper establishes a rigorous measurement science for AI agent reliability, providing a foundational framework for quantifying consistency under semantically preserving perturbations. By leveraging $U$-statistics for output-level reliability and kernel-based metrics for trajectory-level stability, we offer a principled approach to evaluating agents across diverse operating conditions. Our proposal highlights the important distinction between the core capability and execution robustness of an agent, showing that minor task-level variations can induce complete strategy breakdowns despite the agent possessing the requisite knowledge for the task. We validate our framework through extensive experiments on three agentic benchmarks, demonstrating that trajectory-level consistency metrics provide far greater diagnostic sensitivity than traditional pass@1 rates. By providing the mathematical tools to isolate where and why agents deviate, we enable the identification and rectification of architectural concerns that hinder the deployment of agents in high-stakes, real-world environments.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Deriving accurate galaxy cluster masses using X-ray thermodynamic profiles and graph neural networks

Precise determination of galaxy cluster masses is crucial for establishing reliable mass-observable scaling relations in cluster cosmology. We employ graph neural networks (GNNs) to estimate galaxy cluster masses from radially sampled profiles of the intra-cluster medium (ICM) inferred from X-ray observations. GNNs naturally handle inputs of variable length and resolution by representing each ICM profile as a graph, enabling accurate and flexible modeling across diverse observational conditions. We trained and tested GNN model using state-of-the-art hydrodynamical simulations of galaxy clusters from The Three Hundred Project. The mass estimates using our method exhibit no systematic bias compared to the true cluster masses in the simulations. Additionally, we achieve a scatter in recovered mass versus true mass of about 6%, which is a factor of six smaller than obtained from a standard hydrostatic equilibrium approach. Our algorithm is robust to both data quality and cluster morphology and it is capable of incorporating model uncertainties alongside observational uncertainties. Finally, we apply our technique to XMM-Newton observed galaxy cluster samples and compare the GNN derived mass estimates with those obtained with $Y_{\rm SZ}$-M$_{500}$ scaling relations. Our results provide strong evidence, at 5$σ$ level, for a mass-dependent bias in SZ derived masses, with higher mass clusters exhibiting a greater degree of deviation. Furthermore, we find the median bias to be $(1-b)=0.85_{-0.14}^{+0.34}$, albeit with significant dispersion due to its mass dependence. This work takes a significant step towards establishing unbiased observable mass scaling relations by integrating X-ray, SZ and optical datasets using deep learning techniques, thereby enhancing the role of galaxy clusters in precision cosmology.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Detecting Bias in the Presence of Spatial Autocorrelation

In spite of considerable practical importance, current algorithmic fairness literature lacks technical methods to account for underlying geographic dependency while evaluating or mitigating bias issues for spatial data. We initiate the study of bias in spatial applications in this paper, taking the first step towards formalizing this line of quantitative methods. Bias in spatial data applications often gets confounded by underlying spatial autocorrelation. We propose hypothesis testing methodology to detect the presence and strength of this effect, then account for it by using a spatial filtering-based approach -- in order to enable application of existing bias detection metrics. We evaluate our proposed methodology through numerical experiments on real and synthetic datasets, demonstrating that in the presence of several types of confounding effects due to the underlying spatial structure our testing methods perform well in maintaining low type-II errors and nominal type-I errors.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Feature Selection using e-values

In the context of supervised parametric models, we introduce the concept of e-values. An e-value is a scalar quantity that represents the proximity of the sampling distribution of parameter estimates in a model trained on a subset of features to that of the model trained on all features (i.e. the full model). Under general conditions, a rank ordering of e-values separates models that contain all essential features from those that do not. The e-values are applicable to a wide range of parametric models. We use data depths and a fast resampling-based algorithm to implement a feature selection procedure using e-values, providing consistency results. For a $p$-dimensional feature space, this procedure requires fitting only the full model and evaluating $p+1$ models, as opposed to the traditional requirement of fitting and evaluating $2^p$ models. Through experiments across several model settings and synthetic and real datasets, we establish that the e-values method as a promising general alternative to existing model-specific methods of feature selection.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Joint Estimation and Inference for Data Integration Problems based on Multiple Multi-layered Gaussian Graphical Models

The rapid development of high-throughput technologies has enabled the generation of data from biological or disease processes that span multiple layers, like genomic, proteomic or metabolomic data, and further pertain to multiple sources, like disease subtypes or experimental conditions. In this work, we propose a general statistical framework based on Gaussian graphical models for horizontal (i.e. across conditions or subtypes) and vertical (i.e. across different layers containing data on molecular compartments) integration of information in such datasets. We start with decomposing the multi-layer problem into a series of two-layer problems. For each two-layer problem, we model the outcomes at a node in the lower layer as dependent on those of other nodes in that layer, as well as all nodes in the upper layer. We use a combination of neighborhood selection and group-penalized regression to obtain sparse estimates of all model parameters. Following this, we develop a debiasing technique and asymptotic distributions of inter-layer directed edge weights that utilize already computed neighborhood selection coefficients for nodes in the upper layer. Subsequently, we establish global and simultaneous testing procedures for these edge weights. Performance of the proposed methodology is evaluated on synthetic and real data.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Local Dampening: Differential Privacy for Non-numeric Queries via Local Sensitivity

Differential privacy is the state-of-the-art formal definition for data release under strong privacy guarantees. A variety of mechanisms have been proposed in the literature for releasing the output of numeric queries (e.g., the Laplace mechanism and smooth sensitivity mechanism). Those mechanisms guarantee differential privacy by adding noise to the true query's output. The amount of noise added is calibrated by the notions of global sensitivity and local sensitivity of the query that measure the impact of the addition or removal of an individual on the query's output. Mechanisms that use local sensitivity add less noise and, consequently, have a more accurate answer. However, although there has been some work on generic mechanisms for releasing the output of non-numeric queries using global sensitivity (e.g., the Exponential mechanism), the literature lacks generic mechanisms for releasing the output of non-numeric queries using local sensitivity to reduce the noise in the query's output. In this work, we remedy this shortcoming and present the local dampening mechanism. We adapt the notion of local sensitivity for the non-numeric setting and leverage it to design a generic non-numeric mechanism. We provide theoretical comparisons to the exponential mechanism and show under which conditions the local dampening mechanism is more accurate than the exponential mechanism. We illustrate the effectiveness of the local dampening mechanism by applying it to three diverse problems: (i) percentile selection problem. We report the p-th element in the database; (ii) Influential node analysis. Given an influence metric, we release the top-k most influential nodes while preserving the privacy of the relationship between nodes in the network; (iii) Decision tree induction. We provide a private adaptation to the ID3 algorithm to build decision trees from a given tabular dataset.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Non-thermal Sunyaev-Zeldovich signal from radio galaxy lobes

Energetic electrons in the lobes of radio galaxies make them potential sources for not only radio and X-rays but also Sunyaev-Zeldovich (SZ) distortions in the cosmic microwave background (CMB) radiation. Previous works have discussed the energetics of radio galaxy lobes, but assuming thermal SZ effect, coming from the non-thermal electron population. We use an improved evolutionary model for radio galaxy lobes to estimate the observed parameters such as the radio luminosity and intensity of SZ-distortions at the redshifts of observation. We, further, quantify the effects of various relevant physical parameters of the radio galaxies, such as the jet power, the time scale over which the jet is active, the evolutionary time scale for the lobe, etc on the observed parameters. For current SZ observations towards galaxy clusters, we find that the non-thermal SZ distortions from radio lobes embedded in galaxy clusters can be non-negligible compared to the amount of thermal SZ distortion from the intra-cluster medium and, hence, can not be neglected. We show that small and young (and preferably residing in a cluster environment) radio galaxies offer better prospects for the detection of the non-thermal SZ signal from these sources. We further discuss the limits on different physical parameters for some sources for which SZ effect has been either detected or upper limits are available. The evolutionary models enable us to obtain limits, previously unavailable, on the low energy cut-off of electron spectrum ($p_{min} \sim 1\hbox{--}2$) in order to explain the recent non-thermal SZ detection. Finally, we discuss how future CMB experiments, which would cover higher frequency bands ($>$400 GHz), may provide clear signatures for non-thermal SZ effect.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

An Interpretable Graph-based Mapping of Trustworthy Machine Learning Research

There is an increasing interest in ensuring machine learning (ML) frameworks behave in a socially responsible manner and are deemed trustworthy. Although considerable progress has been made in the field of Trustworthy ML (TwML) in the recent past, much of the current characterization of this progress is qualitative. Consequently, decisions about how to address issues of trustworthiness and future research goals are often left to the interested researcher. In this paper, we present the first quantitative approach to characterize the comprehension of TwML research. We build a co-occurrence network of words using a web-scraped corpus of more than 7,000 peer-reviewed recent ML papers -- consisting of papers both related and unrelated to TwML. We use community detection to obtain semantic clusters of words in this network that can infer relative positions of TwML topics. We propose an innovative fingerprinting algorithm to obtain probabilistic similarity scores for individual words, then combine them to give a paper-level relevance score. The outcomes of our analysis inform a number of interesting insights on advancing the field of TwML research.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Towards Integrating Fairness Transparently in Industrial Applications

Numerous Machine Learning (ML) bias-related failures in recent years have led to scrutiny of how companies incorporate aspects of transparency and accountability in their ML lifecycles. Companies have a responsibility to monitor ML processes for bias and mitigate any bias detected, ensure business product integrity, preserve customer loyalty, and protect brand image. Challenges specific to industry ML projects can be broadly categorized into principled documentation, human oversight, and need for mechanisms that enable information reuse and improve cost efficiency. We highlight specific roadblocks and propose conceptual solutions on a per-category basis for ML practitioners and organizational subject matter experts. Our systematic approach tackles these challenges by integrating mechanized and human-in-the-loop components in bias detection, mitigation, and documentation of projects at various stages of the ML lifecycle. To motivate the implementation of our system -- SIFT (System to Integrate Fairness Transparently) -- we present its structural primitives with an example real-world use case on how it can be used to identify potential biases and determine appropriate mitigation strategies in a participatory manner.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

On Weighted Multivariate Sign Functions

Multivariate sign functions are often used for robust estimation and inference. We propose using data dependent weights in association with such functions. The proposed weighted sign functions retain desirable robustness properties, while significantly improving efficiency in estimation and inference compared to unweighted multivariate sign-based methods. Using weighted signs, we demonstrate methods of robust location estimation and robust principal component analysis. We extend the scope of using robust multivariate methods to include robust sufficient dimension reduction and functional outlier detection. Several numerical studies and real data applications demonstrate the efficacy of the proposed methodology.

0 citations0 reviewsNo code linkedOpen access