1 citations · 3 across the 45 of their papers we have counts for
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BLADE: Distilled LLM Regularization for Calibrated Knowledge Graph Completion
Ibne Farabi Shihab, Rabeya Bosri Tamanna, Abdo El Karaky +2
Knowledge graph completion models optimize ranking, although many downstream applications require calibrated probabilities. We present BLADE, a variational model that separates lat…
Spectral-Guided Diffusion: Accelerating Inference via Static Spectral Layer Scheduling
Ibne Farabi Shihab, Abu Sa-Adat Mohamed Moon-Im Al Ahsan, Anuj Sharma
Diffusion inference repeatedly evaluates the same large network. We ask whether pretrained weights alone can identify residual branches that need not be recomputed throughout the t…
Task-Aware Spectral Pruning: A Mixture-of-Masks Framework for Efficient LLM Inference
Ibne Farabi Shihab, Fariya Afrin, Sanjeda Akter +1
Static pruning imposes one sparse structure on every prompt, even though reasoning, retrieval, generation, coding, and translation can depend on different parts of a language model…
Candidate-Label Saliency for Efficient Conformal Pruning of Text Classifiers
Ibne Farabi Shihab, Adria Binte Habib, Anuj Sharma +1
Recalibration can preserve conformal coverage after pruning while enlarging prediction sets and leaving more alternatives for downstream decisions. Directly searching for deletions…
Spectral Tail Interventions in Decoder-Only Language Models: Reasoning-Sensitive Weight Structure from Controlled Surgery
Ibne Farabi Shihab, Sanjida Akhter, Md Najmus Swaqeeb +2
Weight-space structure often correlates with language-model behavior, but correlation alone does not establish computational involvement. We study concentrated upper spectral tails…
CODS: Iterative Bellman-Residual Data Selection for Reusable Offline Reinforcement Learning
Ibne Farabi Shihab, Sanjeda Akter, Abu Sa-Adat Mohamed Moon-Im Al Ahsan +2
Offline reinforcement learning repeatedly trains policies from a fixed transition pool, making redundant data costly across seeds and hyperparameters, while naive subsampling can r…
Discrepancy-Rounded Fair Bandits with Static and Time-Varying Exposure Floors
Ibne Farabi Shihab, Joyanta Jyoti Mondal, Anuj Sharma
Minimum-exposure constraints arise in recommendation, content curation, and regulated allocation when each provider, arm, or group must receive guaranteed exposure inside a period…
Graph Dimensionality Reduction for Contextual Bandits: Structure-Specific Regret Bounds under Approximate Smoothness and Noisy Eigenspaces
Joyanta Jyoti Mondal, Ibne Farabi Shihab, Anuj Sharma
Contextual bandits with graph-structured arms arise in recommendation, citation retrieval, and social advertising, where arms connected on a graph tend to share reward signal. Stan…
EST-PRM: Stress-Testing Process Reward Models Before They Become Load-Bearing
Ibne Farabi Shihab, Fariya Afrin, Sanjeda Akter +1
Process reward models (PRMs) are widely used in language-model training with dense step-level supervision. They assume PRM scores are stable proxies for step correctness under labe…
Grounded Decoding: Retrieval-Anchored Probability Fusion for Faithful RAG
Ibne Farabi Shihab, Fariya Afrin, Sanjeda Akter +1
As retrieval-augmented generation (RAG) systems scale, it becomes increasingly challenging to ensure faithful grounding in external evidence. Large language models may still priori…
Topology-Aware State Abstraction with Tangle Cores for Markov Decision Processes
Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma
State abstraction in reinforcement learning is usually formulated as a partition of states based on reward and transition similarity. This excludes a common structural pattern in n…
Canonicalized Stable-List Replay for Private Federated Continual Learning over Language-Model Embeddings
Ibne Farabi Shihab, Abu Sa-Adat Mohamed Moon-Im Al Ahsan, Anuj Sharma
Federated continual learning (FCL) lets distributed clients adapt language-model heads to evolving NLP tasks without sharing raw text. Under user-level differential privacy (DP), r…
Auditing Near-Optimal Policies Can Be Exponentially Hard: Conditional Query Lower Bounds via Occupancy Rashomon Capacity
Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma
When many reinforcement-learning policies achieve near-optimal return, a post-hoc auditor may have to distinguish among many behaviorally distinct but return-equivalent policies. W…
Dynamic Proxy-Mixing: Transferring Replay Controllers from Small to Large Models for Continual Instruction Tuning
Ibne Farabi Shihab, Fariya Afrin, Anuj Sharma
Continual instruction tuning updates a language model through a sequence of new domains, yet each update can progressively erode previously learned capabilities and alignment behav…
Coverage-Based Calibration for Post-Training Quantization via Weighted Set Cover over Outlier Channels
Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma
Post-Training Quantization (PTQ) compresses large language models to low bit-widths using a small calibration set, and its quality depends strongly on which samples are chosen. We…
Continual Calibration: Coverage Can Collapse Before Accuracy in Lifelong LLM Fine-Tuning
Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma
Continual learning for large language models is typically evaluated through accuracy retention under sequential fine-tuning. We argue that this perspective is incomplete, because u…
Why Colors Make Clustering Harder:Global Integrality Gaps, the Price of Fairness, and Color-Coupled Algorithms in Chromatic Correlation Clustering
Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma
Chromatic Correlation Clustering (CCC) extends Correlation Clustering by assigning semantic colors to edges and requiring each cluster to receive a single color label. Unlike stand…
Minimax Optimality and Spectral Routing for Majority-Vote Ensembles under Markov Dependence
Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma
Majority-vote ensembles achieve variance reduction by averaging over diverse, approximately independent base learners. When training data exhibits Markov dependence, as in time-ser…
Beyond Accuracy: A Unified Random Matrix Theory Diagnostic Framework for Crash Classification Models
Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma
Crash classification models in transportation safety are typically evaluated using accuracy, F1, or AUC, metrics that cannot reveal whether a model is silently overfitting. We intr…
Calibrated Adaptation: Bayesian Stiefel Manifold Priors for Reliable Parameter-Efficient Fine-Tuning
Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma
Parameter-efficient fine-tuning methods such as LoRA enable practical adaptation of large language models but provide no principled uncertainty estimates, leading to poorly calibra…
Grassmannian Mixture-of-Experts: Concentration-Controlled Routing on Subspace Manifolds
Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma
Mixture-of-Experts models rely on learned routers to assign tokens to experts, yet standard softmax gating provides no principled mechanism to control the tradeoff between sparsity…
On the Sparsifiability of Correlation Clustering: Approximation Guarantees under Edge Sampling
Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma
Correlation Clustering (CC) is a fundamental unsupervised learning primitive whose strongest LP-based approximation guarantees require triangle inequality constraints and…
Learning to Forget Attention: Memory Consolidation for Adaptive Compute Reduction
Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma
Hybrid architectures combining state-space models with attention have achieved strong efficiency-quality tradeoffs, yet existing approaches either apply attention uniformly or lear…
Certificate-Guided Pruning for Stochastic Lipschitz Optimization
Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma
We study black-box optimization of Lipschitz functions under noisy evaluations. Existing adaptive discretization methods implicitly avoid suboptimal regions but do not provide expl…