Theses

Permanent URI for this collectionhttp://164.52.219.250:4000/handle/10263/2744

Browse

Start date: 2010Has files: Yes

Search Results

Now showing 1 - 10 of 308
  • No Thumbnail Available
    Item
    Flexible Modeling of non-Gaussian Longitudinal Data: Some Approaches using Copula
    (Indian Statistical Institute, Kolkata, 2026-03-16) Chattopadhyay, Subhajit
    Longitudinal data are common in medical and biological sciences, where measurements are gathered from subjects over time to explore relationships with explanatory variables (covariates) and to uncover the underlying mechanisms of dependence among these measurements. The responses observed at each instance can be either discrete or continuous. One of the primary challenges in longitudinal data analysis lies in the non-Gaussian nature of the response variables. As a result, there are relatively few multivariate models in the literature that effectively address the specific characteristics observed in such datasets. In this dissertation, we address four problems concerning longitudinal data analysis by developing new statistical models. These models specifically address the time-related relationships found in various types of non-Gaussian longitudinal data by employing suitable classes of parametric copulas. In the third chapter of this dissertation, we examine a motivating dataset from a recent HIV-AIDS study conducted in Livingstone district, Zambia. The histogram plots of the repeated measurements at each time point reveal asymmetry in the marginal distributions, and pairwise scatter plots uncover nonelliptical dependence patterns. Traditional linear mixed models, typically used for longitudinal data, struggle to capture these complexities effectively. We introduced skew-elliptical copula based mixed models to analyze this continuous data, where we use generalized linear mixed models (GLMM) for the marginals (e.g., Gamma mixed model), and address the temporal dependence of repeated measurements by utilizing copulas associated with skew-elliptical distributions (such as skew-normal/skew-t). The proposed class of copula-based mixed models addresses asymmetry, between-subject variability, and non-standard temporal dependence simultaneously, thereby extending beyond the limitations of standard linear mixed models based on multivariate normality. We estimate the model parameters using the IFM (inference function of margins) method, and outline the procedure for obtaining standard errors of the parameter estimates. To evaluate the performance of this approach under finite sample conditions, rigorous simulation studies are conducted, encompassing skewed and symmetric marginal distributions along with various copula selections. Finally, we apply these models to the HIV dataset and present the insight gained from the analysis. In the fourth chapter of this dissertation, we introduce factor copula models tailored for unbalanced non-Gaussian longitudinal data. Modeling the joint distribution of such data, where subjects may have varying numbers of repeated measurements and responses can be continuous or discrete, poses practical challenges, especially with numerous measurements per subject. Factor copula models, which are canonical vine copulas, leverage latent variables to elucidate the underlying dependence structure of multivariate data. This approach aids in interpretation and implementation for unbalanced longitudinal datasets, enhancing our ability to model complex dependencies effectively. We develop regression models for continuous, binary and ordinal longitudinal data, incorporating covariates, using factor copula constructions with subject-specific latent variables. With consideration for homogeneous within-subject dependence, the proposed models enable feasible parametric inference in moderate to high dimensional scenarios, employing a two-stage (IFM) estimation method. We also present a method for evaluating the residuals of factor copula models to visually assess the goodness of fit. The performance of the proposed models in finite samples is assessed through extensive simulation studies. In empirical analyses, we apply these models to analyze various longitudinal responses from two real-world datasets. Furthermore, we compare the performance of these models with widely used random effects models using standard selection techniques, revealing significant improvements. Our findings suggest that factor copula models can serve as viable alternatives to random effect models, offering deeper insights into the temporal dependence of longitudinal data across diverse contexts. In the fifth chapter of this dissertation, we address the issue of modeling complex and hidden temporal dependence of count longitudinal data. Multivariate elliptical copulas are typically preferred in statistical literature to analyze dependence between repeated measurements of longitudinal data since they allow for different choices of the correlation structure. But these copulas lack in flexibility to model dependence and inference is only feasible under parametric restrictions. In this chapter, we propose the use of finite mixtures of elliptical copulas to enhance the modeling of temporal dependence in discrete longitudinal data. This approach enables the utilization of distinct correlation matrices within each component of the mixture copula. We theoretically explore the dependence properties of finite mixtures of copulas before employing them to construct regression models for count longitudinal data. Inference for this proposed class of models is based on a composite likelihood approach, and we evaluate the finite sample performance of parameter estimates through extensive simulation studies. To validate the fitting of the proposed models, we extend traditional techniques and introduce the t-plot method to accommodate finite mixtures of elliptical copulas. Finally we apply the proposed models to analyze the temporal dependence within two real-world count longitudinal datasets and demonstrate their superiority over standard elliptical copulas. In the final contributing chapter of this dissertation, we introduce a novel multivariate copula based on the multivariate geometric skew-normal (GSN) distribution. This asymmetric copula serves as an alternative to the skew-normal copula proposed by Azzalini. Unlike the standard skew-normal copula, the multivariate GSN copula retains closure properties under marginalization, which offers computational advantages for modeling multivariate discrete data. In this chapter, we outline the construction of the geometric skew-normal copula and its application in modeling the temporal dependence observed in non-Gaussian longitudinal data. We begin by exploring the theoretical properties of the proposed multivariate copula. Subsequently, we develop regression models tailored for both continuous and discrete longitudinal data using this innovative framework. Notably, the quantile function of this copula remains independent of the correlation matrix of its respective multivariate distribution, offering computational advantages in likelihood inference compared to copulas derived from skew-elliptical distributions proposed by Azzalini. Furthermore, composite likelihood inference becomes feasible for this multivariate copula, allowing for parameter estimation from ordered probit models with the same dependence structure as the geometric skew-normal distribution. We conduct extensive simulation studies to validate the geometric skew-normal copula based models and apply them to analyze the longitudinal dependence of two real-world data sets. Finally, We present our findings in terms of the improvements over regression models based on multivariate Gaussian copulas.
  • No Thumbnail Available
    Item
    Development of Some Scalable Pattern Recognition Algorithms for Real Life Data Analysis
    (2017-11-20) Garai, Partha
    A huge amount of data is being generated continuously as a result of recent advancement and wide use of high-throughput technologies. With the rapid increase in size of data distributed worldwide, understanding the data has become critical. In this regard, dimensionality reduction and clustering have become the necessary preprocessing steps of multiple research areas and applications. One of the important problems of real life large data sets is uncertainty. Some of the sources of this uncertainty include imprecision in computation and vagueness in class denitions. The uncertainty may also be present in the denition of class membership function. In this background, the thesis addresses the problem of dimensionality reduction and clustering of real life data sets, in the presence of noise and uncertainty. The thesis rst presents the problem of feature selection using both type-1 and interval type-2 fuzzyrough sets, which are eective for dimensionality reduction of real life data sets when uncertainty is present in the data set. The properties of fuzzy-rough sets allow greater exibility in handling noisy and real valued data. While the concept of lower approximation and boundary region of rough sets deals with uncertainty, incompleteness, and vagueness in class denition, the use of either type-1 or interval type-2 fuzzy sets enables ecient handling of overlapping classes in uncertain environment. Moreover, a new concept of \simultaneous attribute selection and feature extraction" is introduced for dimensionality reduction, integrating judiciously the merits of both feature selection and extraction. A scalable rough-fuzzy clustering algorithm is introduced for large real life data sets, where the theory of rough hypercuboid approach, interval type-2 fuzzy sets, and c-means algorithm are integrated judiciously to handle the uncertainty present in a data set. While the concept of rough hypercuboid approach deals with uncertainty, incompleteness, and vagueness in cluster denition, the use of fuzzy membership of interval type-2 fuzzy sets in the boundary region of a cluster enables ecient handling of overlapping partitions in uncertain environment. Finally, the application of both clustering and feature selection algorithms is demonstrated by grouping functionally similar microRNAs from microarray data. The proposed approach can automatically select the optimum set of features while clustering the microRNAs, making the complexity of the algorithm lower.
  • No Thumbnail Available
    Item
    Projective corepresentations and cohomology of compact quantum groups
    (Indian Statistical Institute, Kolkata, 2026-01-22) Maity, Kiran
    In this thesis, we briefly review various types of projective corepresentations of compact quantum groups and prove the existence of suitable envelopes for them. We also study the associated invariant (dual) 2-cohomology and calculate it in a few concrete examples.
  • No Thumbnail Available
    Item
    Generalization under Sub-population Shift: Equitable Models for Imbalanced, Long-tailed, and Fair Representation Learning
    (Indian Statistical Institute, Kolkata, 2026-02-09) Ansari, Faizanuddin
    Machine learning systems often experience performance degradation in real-world scenarios due to subpopulation shift defined as mismatches in the distribution of classes or attributes within datasets. This thesis investigates generalization failures arising from class imbalance, long-tailed distributions, and attribute-level biases (specifically, attribute-level biases that originate from demographic imbalances in sensitive domains, such as medical imaging). It proposes principled strategies to mitigate these effects in both classical and deep learning frameworks. Class imbalance and long-tailed distributions pose significant challenges, especially in real-world applications where minority classes are underrepresented yet critically important. To address these challenges, this work develops novel algorithms and frameworks that enhance model generalization on imbalanced and long-tailed datasets. The contributions encompass data-level, model-level, and loss-level innovations, each designed to mitigate bias and improve performance in minority classes while maintaining accuracy in majority classes. First, we propose a data-level solution for classical class imbalance in tabular data through a novel oversampling technique that estimates minority class statistics using neighborhood-based distributional calibration. Unlike existing methods that rely on synthetic interpolation without accounting for class-specific geometry, the proposed approach preserves the fidelity of minority class distributions, leading to significant gains in both binary and multi-label imbalanced settings. Next, we introduce STTP-Net, a two-pronged framework for long-tailed learning in vision tasks. It integrates hybrid augmentation and sampling strategies with a newly proposed Effective Balanced Softmax (EBS) loss to correct label distribution shifts, enabling robust feature learning and improved accuracy across head, medium, and tail classes. Extensive evaluations on benchmark datasets such as CIFAR-LT, ImageNet-LT, and NIH-CXR-LT confirm its superiority over state-of-the-art methods. We address decision boundary distortion under class imbalance by introducing the Goldilocks principle to achieve ``just-right'' boundary fidelity. Our approach leverages this concept to design a training pipeline that produces smoother, more adaptive decision boundaries for tail classes. Specifically, we propose a Dual-Branch Sampler-Guided Mixup (DBSGM) strategy combined with an Adaptive Class-Aware Feature Regularization (ACFR) mechanism. These components jointly enhance intra-class compactness and inter-class separability, improving generalization, especially under extreme imbalance. By dynamically adjusting boundaries and applying adaptive regularization, our method achieves optimal fidelity for minority classes without compromising the performance of majority classes. Extensive experiments validate its effectiveness across a range of imbalance ratios. Furthermore, we extend these ideas to medical imaging, addressing both class imbalance and demographic fairness. This includes the Mixture of Two Experts (Mo2E) framework and fairness-aware lesion classification strategies that ensure equitable performance across subgroups. Mo2E combines asymmetric sampling with adaptive mixup to improve the detection of rare disease classes and is validated across tasks such as Gastrointestinal (GI) Tract Classification of Endoscopic Images and Diabetic Retinopathy (DR) grading. Additionally, we introduce a bias-aware training method to mitigate both \emph{class imbalance and skin tone bias}, achieving fair performance across demographic subgroups, as demonstrated on the ASAN and ISIC-2018 datasets. These results lay the groundwork for demographically fair model design in high-stakes medical applications. Collectively, these contributions advance the field of imbalanced learning by offering scalable, practical solutions grounded in theoretical insight and empirical validation. This thesis provides a comprehensive toolkit for researchers and practitioners confronting the challenges of subpopulation shift, integrating principled data synthesis, loss rebalancing, and fairness constraints. It pushes the frontiers of robust, fair, and generalizable deep learning, particularly in domains where class rarity and demographic underrepresentation have tangible real-world consequences.
  • No Thumbnail Available
    Item
    On Robust Estimation of Multivariate Location and Scale with Applications
    (Indian Statistical Institute, Kolkata, 2026-02-04) Chakraborty, Soumya
    The principal objective of this thesis is, in a nutshell, to provide robust estimators of multivariate location and scale which have reasonable to high model efficiency but avoid high computational complexity so as to be practically useful in real problems. We utilize the minimum density power divergence (DPD) and the related philosophy to invoke robustness. There are some computational issues while minimizing the DPD in different multivariate set-ups. We will work on this problem rigorously and come up with three types of estimation procedures which are explicitly or implicitly related to the minimum DPD methodology, keeping the computational issue in mind each time. In particular, we develop a robust clustering algorithm based on mixture normal models in the first work where the component mean vectors and covariance matrices are estimated by minimizing the DPD with a suitable iteratively reweighted least squares (IRLS) algorithm. The second work proposes a sequential approach to minimize the DPD for location-scale estimation in case of elliptically symmetric probability models. The third work studies the one-step minimization of the DPD with various highly robust initializations and iterative procedures. We derive the theoretical properties (asymptotic and robustness features) of these methods, empirically validate them with extensive simulation studies in various set-ups and apply them in different problems in the domains of pattern recognition and machine learning.
  • No Thumbnail Available
    Item
    Statistical Guarantees of Deep Generative Models Involving Diverse Spaces: Generation Consistency and Robustness
    (Indian Statistical Institute, Kolkata, 2026-02-04) Chakrabarty, Anish
    Generative modeling focuses on the task of producing new data samples that closely resemble those drawn from an original, unknown distribution. Despite being well-known in statistical estimation theory, the approach has gained substantial traction in recent years, driven by groundbreaking results in areas such as image synthesis, natural language generation, and network modeling. The complexity of modern-era data domains and the ensuing adaptations that suitable models must undergo have presented new challenges. These advances raise several fundamental questions, the first of which is: When do generative models accurately approximate the true data distribution? One may also ask: How well do these models perform under contaminated data? This work explores these questions through the lens of generative modeling frameworks that, by design, involve distinct data spaces. We focus on two major classes of such models that blend optimal transport and representation learning in their objectives: Wasserstein autoencoders (WAE) and Cycle-consistent cross-domain translators. WAE, on its way to regeneration, learns a latent code, which in turn aids the simulation of newer pseudo-random replicates. By providing statistical characterizations of the latent distribution and the transforms inducing a dimensionality reduction in the process, we present a detailed error analysis underlying WAEs. From a non-parametric density estimation perspective, we establish deterministic bounds on the latent and reconstruction errors that adapt to the intrinsic dimensions of input data. We also study the extent of distortion that WAE-generated samples suffer when learned using contaminated data. Key takeaways for practitioners from our analysis include specific architectural suggestions that foster near-perfect sampling. The framework developed thus far fittingly extends to unpaired cycle-consistent cross-domain models. We show that the sufficient conditions for successful data translation under Sobolev and H¨older-smooth distributions resemble those in the case of WAEs. Our analysis also suggests error upper bounds due to ill-posed transformations and validates the choice of divergences used in objectives. Finally, in search of a consolidated solution to the robustification problem, we present parallel formulations based on the Gromov-Wasserstein (GW) distance. Due to the equivalence of Gromov-Monge samplers (GW), following GW, and cross-domain translation models, including WAE and GWAE, this answers the second question. We study the robust recovery guarantees, concentration, and tractable computational properties of the newly introduced distance measures under diverse contamination scenarios. We substantiate all our findings based on real-world data in varying generative tasks.
  • No Thumbnail Available
    Item
    Developing Deep Neural Network based Brain Computational Models from Psychophysics Data of some Simple Perceptual Phenomena: Visual as well as Auditory
    (Indian Statistical Institute, Kolkata, 2026-02-03) Chandran, Keerthi S
    The thesis, consisting of nine chapters, explores the methodology of building testable brain computational models using Deep Neural Networks (DNN), which are trained by psychophysics data. Psychophysics is the quantitative study of perception of physical stimuli. In psychophysics experiments, one or more parameters associated with the stimuli are changed, and the human subject’s responses to the stimuli are recorded. This thesis encompasses both experimental psychophysics works, as well as computational models of the phenomena involved. The contributory chapters of the thesis start in Chapter 2 with the perspective building of the novel methodology followed throughout this research involving psychophysics on one hand, and deep neural network based brain modeling on the other. This chapter also focuses on the possible reasons for the discrepancies that exist between the functioning of existing deep neural networks, and psychological findings. The present thesis explores three very simple, yet intriguing perceptual phenomena viz. flicker fusion, flicker wheel illusion and sound symbolism. While the first two are purely based on visual perception, the third, i.e. sound symbolism, as the name suggests, involves both visual and auditory perception. The experiments involved in this thesis concerning all these three perceptual phenomena share one common aspect. Not only do they involve very simple stimuli for conducting the psychological experiments, but also the subject response in all the three cases is binary. For the flicker (a flicker stimulus is a visual stimulus with intermittent illumination) fusion experiment, the subject reports whether the stimulus appears flickering or steady; for the flicker wheel illusion, subjects report whether the wheel is perceived as static or flickering (illusory), while in the sound symbolism experiments, the subject assigns a sound stimuli to one of the two visual inputs, provided. In Chapter 3 of the thesis, a Convolutional Recurrent Neural Network (CRNN) for modeling the flicker fusion phenomenon has been proposed. It is shown that the model is trainable with psychophysics data, and testable with a wide variety of flicker patterns. Next, in Chapter 4, a DNN model that takes into account the microsaccades in the eye is presented for the Flicker Wheel illusion while also building a novel dataset for this illusion. The sound symbolism phenomena is investigated in Chapter 5, for the difference in words for round and sharp objects across several natural languages. Here again, both behavioral experiments and DNN based modeling are performed. Thus, by establishing the efficacy of DNN based brain computational models in explaining these psychological phenomena, the present thesis goes on to further investigate the flicker stimulus, already discussed in Chapter 3, to better explore the strengths and limitations of the present brain computational modeling approach. To this end, first in Chapter 6, the CRNN model is used to probe the relation between the psychophysics and brain electrophysiology involving the flicker stimulus. The work in this chapter, interestingly, demonstrates that many of the reported features of the human electroencephalogram (EEG) response to flicker can actually be explained as being the convolution response to the stimulus, despite the fact that the model is trained with behavioral data only. Next, to further generate more flicker data, and subsequently put the proposed DNN model to more stringent testing, Chapter 7 of the thesis describes the construction of an indigenous low-cost device that can generate mass psychophysics data on flicker fusion to train and test DNNs. Subsequently, in Chapter 8, the psychophysics data generated from this device was used to train a CRNN. The training yielded symmetric filters, as often found in biological visual systems. The predictions made by the CRNN model on complex flicker patterns were then tested through psychophysics experiments with the device, demonstrating that the model is falsifiable with scopes of further improvement through future research.
  • No Thumbnail Available
    Item
    Statistically Consistent and Novel Image Registration Methods for Various Transformations in Presence of Zooming
    (Indian Statistical Institute, Kolkata, 2026-02-03) Das, Sujay
    In image processing literature, image registration is the process of spatially matching two or more images of the same scene or object obtained at various times, from different perspectives, or by separate sensors in order to simplify comparison, fusion, or analysis. Its purpose is to identify the spatial transformation that minimizes the differences between related characteristics or intensities in the reference and moved images. Image registration techniques find a wide range of applications in several imaging fields, namely, medical imaging, satellite imaging, fingerprint matching, remote sensing, and image comparison, to name a few. The growing popularity of this imaging field motivates numerous researchers to develop novel and improved methods of image registration. In this dissertation, we develop several image registration techniques under various image transformations, especially when zooming or scaling is involved. In our first research-work, we develop an intensity-based image registration technique that registers two images efficiently, where one is a zoomed-in version of the other. In order to improve the performance and also to reduce the computational complexity of the method at the same time, we opt for a feature-based approach for the same problem in the second research-work of this dissertation. Motivated by the success of the feature-based approach, we use similar approach to image registration for a more complex image transformation where translation, rotation, and zooming are all involved in our final research-work. All our proposed methods are backed by extensive numerical studies and comparisons with several state-of-the-art methods, which show our methods' superiority over them. We also provide theoretical justifications when the image resolutions are high.
  • No Thumbnail Available
    Item
    Exploring Conflicts and Protests Through the Lens of Game Theory
    (Indian Statistical Institute, Kolkata, 2026-01-20) Mukherjee, Puja
    This dissertation aims to study conflicts and protests using a game-theoretic setup. It contains six chapters. Chapter 1 is the introduction. Chapter 2 gives a brief review of the related literature. Chapter 3 explores conflicts in the presence of revenge and third party intervention using a game theoretic setup. In chapter 4, I propose a revengecapability function to study the strategic behaviour of the conflicting parties. In chapter 5, I develop a signaling game between the government and protesters to study the phenomenon of protests in the presence of an external shock like the pandemic. Chapter 6 concludes the dissertation. Chapter 3 aims to study conflicts in the presence of revenge and third-party interventions using a game theoretic setup where the intervention decision of the third party is endogeneous. This model explores parametric restrictions under which a third party decides to intervene (either as an ally of one of the conflicting parties or as an ‘idealist’ aiming to reduce overall conflict levels) and its repercussions on associated conflict levels. This chapter also presents narrative evidences of some real-life conflicts that amply exhibit the two forces of third-party intervention and revenge. In chapter 4, I propose a revenge-capability function that endogenously incorporates the incapacitation effect and study the strategic behaviour of the conflicting parties. Using a two-period game of conflict this chapter tries to show how desire and capabilities of the combatants to exact revenge can influence the intensity of the conflict. This chapter shows the following: how the strategies of the conflicting parties are influenced by the different effects of revenge; how the stronger combatant is in a favourable position in the conflict and can prevent its opponent from going into second period conflict out of revenge; when the combatants are equally strong the intensity of the conflict starts falling with time. It also lays out some real-life conflicts and existing empirical work to support the results. In chapter 5, I develop a signaling game where the protesters’ type is imperfectly observed by the government to study when it will be optimal for the protesters to protest in response to a government action and for the government to use a repression strategy when there is an external shock like the pandemic. It shows the following; how the virus spread influences the strategies of the players; how the intensity of protests changes with the level of the virus spread; compares the no-pandemic equilibria with the pandemic equilibria and lastly analyses the parametric conditions under which different separating and pooling equilibria holds.
  • No Thumbnail Available
    Item
    Identifying and Overcoming Some Operational Limitations of Reconfigurable Intelligent Surfaces in 5G and Beyond Wireless Networks
    (Indian Statistical Institute, Kolkata, 2026-01-20) Deb, Souvik
    Reconfigurable intelligent surfaces (RIS) can dynamically reshape the propagation environment to enhance signal strength, spectral efficiency and reliability in 5th generation (5G) cellular as well as device to device (D2D) communications. However, to reap such benefits, a range of practical and operational challenges need to be addressed for effectively utilizing RIS in realistic urban environments. This includes maintaining line of sight (LoS) between the RIS and the communicating devices for reliable signal reflection in millimeter wave (mmWave) communication, reducing high channel estimation overhead for communication using multipath rich channels and preventing violation of strict latency constraints due to high complexity of optimal RIS configuration, among others. We begin by examining the limitations of RIS in mmWave D2D networks, where stationary RISs require a fixed LoS and strategic placement to accommodate user mobility. To overcome this, we first formulate the RIS placement task as a set cover problem and place the fewest possible number of RIS by using a greedy approximation algorithm in a preprocessing stage. Then, once devices are deployed, we select an optimal RIS subset to provide indirect LoS to D2D pairs that have their direct LoS link blocked. Here we have considered that the RIS will be allowed to be placed in any of the desired locations. Next, the mmWave D2D network scenario where optimal RIS locations may be inaccessible is considered. This is due to third party building ownership, and prohibition from deploying dedicated support structures by regulatory authorities. In these situations, one must leverage the existing RIS deployment which may not be optimal. Moreover, user mobility results in continuous change in RIS-user LoS status. To overcome this challenge, a novel visibility polygon based deterministic RIS selection algorithm is proposed. Next, the focus of the study moves to 5G cellular networks. In obstacle rich dense urban environments, multipath propagation results in high channel estimation overhead for both sub-$6$ GHz and mmWave channels, leading to outdated channel state information (CSI). Therefore, performing channel estimation at the beginning of every coherence time interval incurs massive pilot overhead. To reduce the pilot overhead, an algorithm is proposed that dynamically schedules the next channel estimation time based on outdated CSI. First, RIS phase shifts are computed based on current CSI. Next, user transmit powers and bandwidth are allocated based on outdated CSI to maximise the aggregate throughput. Using this, the proposed algorithm dynamically adjusts the duration between consecutive channel estimation instants such that pilot overhead is reduced without harming the throughput performance. The primary focus of this study is the enhanced mobile broadband (eMBB) users whose objective is to maximize the throughput. Pilot overhead and high complexity of RIS configuration increase latency, which is detrimental to the performance of ultra reliable low latency (URLLC) users requiring strict delay constraints. Moreover, mobility of such users results in a frequent change of channel conditions and handovers between networks. To maintain seamless connectivity and strict latency constraints during handovers, a joint base station (BS) and RIS selection algorithm based on contextual multi-armed bandits (C-MAB) has been proposed. The algorithm learns when to take the assistance of RIS while communicating with the BS to maintain latency without losing reliability. Both resource block (RB) allocation and RIS selection in RIS assisted cellular networks are dependent on the CSI of direct BS-user as well as RIS assisted channels, thereby being affected by channel estimation overhead. This is established by proposing a MAB based RB allocation algorithm to allocate RBs in an RIS assisted network which utilizes both direct and RIS assisted channel information as opposed to using only the direct channel information. After establishing this, an efficient RIS-RB pair allocation algorithm based on adversarial bandit and bipartite graph matching has been proposed for mmWave non orthogonal multiple access networks. This algorithm addresses the challenge of high CSI overhead to find the optimal RIS in the presence of dynamic obstacles and allocate RBs optimally to user groups to maximize throughput while ensuring that users with worse channel conditions are not ignored.