42 citations · 95 across the 22 of their papers we have counts for
26 papers
ClaimScope: Label-Blind Witness Verification for Mathematical Claim Auditing
Mushir Akhtar
Mathematical agents are commonly evaluated by answer accuracy or proof acceptance. We introduce ClaimScope, a 72-task benchmark for auditing proposed claims through a verdict, scop…
When Does Muon Actually Win? A Paired Data-, Time-, and Tuning-Budget Benchmark
Mushir Akhtar, Hazem N. Nounou, Majdi Mansouri +1
Muon's gains in data efficiency motivate evaluating its performance under explicit data, time, and tuning budgets. On a single A100 GPU, we compare a reference single-device Muon i…
SAGE-CP: When Marginal Coverage Hides Route-Conditional Failures in Adaptive VLM Ensembles
Mushir Akhtar, M. Tanveer
Adaptive VLM ensembles save computation by stopping early on easy images and escalating harder ones. Although a single conformal threshold can make the final predictor marginally v…
Beyond Benchmark Scores: Auditing Medical Vision-Language Models for Chest X-Ray Tuberculosis Screening
Mushir Akhtar, M. Tanveer, Mohd. Arshad
A medical model's benchmark score does not establish that the same conclusion holds under a different evaluation. This study tests whether claims about model ranking, score reliabi…
Bounded Adjustment with Reliability-Guided Embedding for Imbalanced Learning with Noisy Labels
Mushir Akhtar, Akarsh J., M. Tanveer +1
Class-balanced learning and label noise create a coupled failure mode: frequency correction prevents majority classes from dominating the decision rule, but can amplify incorrectly…
IFW-BLS: Dual-Robust Broad Learning System with Intuitionistic Fuzzy Wave Loss
Mushir Akhtar, M. Tanveer
Broad Learning System is an efficient randomized learning model that expands network width through feature and enhancement nodes and estimates the output weights without deep backp…
Robust Broad Learning System with Wave Loss for Classification under Data Uncertainty
Mushir Akhtar, A. Varshney, A. Quadir +3
Broad Learning System (BLS) offers an efficient alternative to deep architectures by enabling fast learning through randomized feature mapping and closed-form solutions. However, i…
ECA-BLS: An Efficient Complex-Augmented Broad Learning System
A. Rahaman, A. Quadir, M. Sajid +2
Broad Learning System (BLS) is an efficient alternative to deep architectures due to its fast training, analytical learning, and strong generalization under limited data. However,…
Residual-Guided Randomized Neural Networks
Mushir Akhtar, M. Tanveer, Mohd. Arshad
Randomized neural networks enable fast and analytically tractable training by fixing the input to hidden layer parameters at random and learning the output weights in closed form;…
Robust Dual-Model Collaborative Random Vector Functional Link Network
A. Quadir, A. Rahaman, Mushir Akhtar +1
Random vector functional link (RVFL) networks are lightweight and fast neural models that offer efficient training and strong generalization through randomized hidden-layer weights…
Mitigating Class-Tail Undercoverage in Medical Vision-Language Models under Clinical Shift
Mushir Akhtar, M. Tanveer
Medical vision-language models (VLMs) can retain high observed marginal coverage after clinical shift while substantially under-covering an individual disease class. The affected c…
CAWI: Copula-Aligned Weight Initialization for Randomized Neural Networks
Mushir Akhtar, M. Tanveer, Mohd. Arshad
Randomized neural networks (RdNNs) enable efficient, backpropagation-free training by freezing randomly initialized input-to-hidden weights, which permits a closed-form solution fo…
Twin Restricted Kernel Machines for Multiview Classification
A. Quadir, M. Sajid, Mushir Akhtar +1
Multi-view learning (MVL) is an emerging field in machine learning that focuses on improving generalization performance by leveraging complementary information from multiple perspe…
RVFL-X: A Novel Randomized Network Based on Complex Transformed Real-Valued Tabular Datasets
M. Sajid, Mushir Akhtar, A. Quadir +1
Recent advancements in neural networks, supported by foundational theoretical insights, emphasize the superior representational power of complex numbers. However, their adoption in…
R^2VFL: A Robust Random Vector Functional Link Network with Huber-Weighted Framework
Anuradha Kumari, Mushir Akhtar, P. N. Suganthan +1
The random vector functional link (RVFL) neural network has shown significant potential in overcoming the constraints of traditional artificial neural networks, such as excessive c…
CI-RKM: A Class-Informed Approach to Robust Restricted Kernel Machines
Ritik Mishra, Mushir Akhtar, M. Tanveer
Restricted kernel machines (RKMs) represent a versatile and powerful framework within the kernel machine family, leveraging conjugate feature duality to address a wide range of mac…
Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach
M. Tanveer, Anushka Tiwari, Mushir Akhtar +1
In real-world applications, class-imbalanced datasets pose significant challenges for machine learning algorithms, such as support vector machines (SVMs), particularly in effective…
Flexi-Fuzz least squares SVM for Alzheimer's diagnosis: Tackling noise, outliers, and class imbalance
Mushir Akhtar, A. Quadir, M. Tanveer +1
Alzheimer's disease (AD) is a leading neurodegenerative condition and the primary cause of dementia, characterized by progressive cognitive decline and memory loss. Its progression…
Advancing RVFL networks: Robust classification with the HawkEye loss function
Mushir Akhtar, Ritik Mishra, M. Tanveer +1
Random vector functional link (RVFL), a variant of single-layer feedforward neural network (SLFN), has garnered significant attention due to its lower computational cost and robust…
Enhanced Feature Based Granular Ball Twin Support Vector Machine
A. Quadir, M. Sajid, M. Tanveer +1
In this paper, we propose enhanced feature based granular ball twin support vector machine (EF-GBTSVM). EF-GBTSVM employs the coarse granularity of granular balls (GBs) as input ra…
GL-TSVM: A robust and smooth twin support vector machine with guardian loss function
Mushir Akhtar, M. Tanveer, Mohd. Arshad
Twin support vector machine (TSVM), a variant of support vector machine (SVM), has garnered significant attention due to its times lower computational complexity compared to…
Enhancing Multiview Synergy: Robust Learning by Exploiting the Wave Loss Function with Consensus and Complementarity Principles
A. Quadir, Mushir Akhtar, M. Tanveer
Multiview learning (MvL) is an advancing domain in machine learning, leveraging multiple data perspectives to enhance model performance through view-consistency and view-discrepanc…
Advancing Supervised Learning with the Wave Loss Function: A Robust and Smooth Approach
Mushir Akhtar, M. Tanveer, Mohd. Arshad
Loss function plays a vital role in supervised learning frameworks. The selection of the appropriate loss function holds the potential to have a substantial impact on the proficien…
HawkEye: Advancing Robust Regression with Bounded, Smooth, and Insensitive Loss Function
Mushir Akhtar, M. Tanveer, Mohd. Arshad
Support vector regression (SVR) has garnered significant popularity over the past two decades owing to its wide range of applications across various fields. Despite its versatility…