3/31/2026
A Deep Reinforcement Learning Framework for Closed-loop Guidance of Fish Schools via Virtual Agents
Deep reinforcement learning framework using PPO to train virtual agents for guiding fish school collective motion.
3/31/2026
Differentiable Power-Flow Optimization
Differentiable power-flow optimization using neural networks to replace Newton-Raphson methods for scalable grid simulation.
3/31/2026
Learning from imperfect quantum data via unsupervised domain adaptation with classical shadows
Domain adaptation method for quantum machine learning using classical shadows to handle distribution shift in quantum data.
3/31/2026
Prototype-Enhanced Multi-View Learning for Thyroid Nodule Ultrasound Classification
Multi-view learning approach with prototypes for thyroid ultrasound classification with improved robustness across devices.
3/31/2026
A Multi-Agent Rhizomatic Pipeline for Non-Linear Literature Analysis
Multi-agent pipeline for literature analysis using Deleuzian ontology to identify non-linear patterns in research landscapes.
3/31/2026
Kernel-Smith: A Unified Recipe for Evolutionary Kernel Optimization
Framework for evolutionary GPU kernel optimization using evaluation-driven agent and evolutionary techniques for operator generation.
3/31/2026
Optimized Weighted Voting System for Brain Tumor Classification Using MRI Images
Ensemble learning system combining deep learning and traditional classifiers for brain tumor MRI classification.
3/31/2026
The Scaffold Effect: How Prompt Framing Drives Apparent Multimodal Gains in Clinical VLM Evaluation
Analysis of prompt framing artifacts in vision-language model evaluation on clinical neuroimaging tasks.
3/31/2026
From Simulation to Deep Learning: Survey on Network Performance Modeling Approaches
Survey of network performance modeling approaches comparing traditional simulation with deep learning methods.
3/31/2026
Yau's Affine Normal Descent: Algorithmic Framework and Convergence Analysis
Novel geometric optimization framework using affine normal descent with invariance under volume-preserving transformations.
3/31/2026
$R_{dm}$: Re-conceptualizing Distribution Matching as a Reward for Diffusion Distillation
Diffusion distillation approach using reinforcement learning to improve student model performance beyond teacher anchoring.
3/31/2026
With a Little Help From My Friends: Collective Manipulation in Risk-Controlling Recommender Systems
Study of coordinated user manipulation tactics in recommendation systems through collective rating and review mechanisms.
3/31/2026
Multimodal Analytics of Cybersecurity Crisis Preparation Exercises: What Predicts Success?
Analysis of instructional alignment in cybersecurity exercises using multimodal data and Bloom's taxonomy coding.
3/31/2026
Towards a Medical AI Scientist
Medical AI Scientist system that autonomously generates hypotheses, conducts experiments, and writes manuscripts in clinical medicine.
3/31/2026
Universal Approximation Constraints of Narrow ResNets: The Tunnel Effect
Theoretical analysis of approximation constraints in narrow ResNets, proving inability to represent critical points.
3/31/2026
Unsafe2Safe: Controllable Image Anonymization for Downstream Utility
Automated pipeline using diffusion models to detect and anonymize sensitive regions in images while preserving utility.
3/31/2026
Constructing Composite Features for Interpretable Music-Tagging
Genetic programming pipeline for automatically evolving interpretable composite features for music tagging tasks.
3/31/2026
Why Aggregate Accuracy is Inadequate for Evaluating Fairness in Law Enforcement Facial Recognition Systems
Study of demographic bias and fairness issues in facial recognition systems used by law enforcement.
3/31/2026
On-the-fly Repulsion in the Contextual Space for Rich Diversity in Diffusion Transformers
Method for improving diversity in text-to-image diffusion transformers through contextual space repulsion to address typicality bias.
3/31/2026
Algorithmic Insurance
Paper examining financial insurance mechanisms for AI system failures and algorithmic liability in high-stakes domains.
3/31/2026
Less is More: Rethinking Few-Shot Learning and Recurrent Neural Nets
Study on few-shot learning and RNNs applying asymptotic equipartition property from information theory to machine learning.
3/31/2026
Convergence of the Inexact Langevin Algorithm in KL Divergence with Application to Score-based Generative Models
Theoretical analysis of inexact Langevin algorithm convergence for score-based generative models with KL divergence guarantees.
3/31/2026
Continual Graph Learning: A Survey
Survey on continual graph learning covering incremental learning from streaming graph data with experience and generative replay approaches.
3/31/2026
Correcting Auto-Differentiation in Neural-ODE Training
Mathematical analysis of auto-differentiation reliability in neural-ODE training with high-order numerical methods.
3/31/2026
Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks
Novel prior learning method for neural networks using structured posteriors to improve generalization and uncertainty estimation.
3/31/2026
Unichain and Aperiodicity are Sufficient for Asymptotic Optimality of Average-Reward Restless Bandits
Proves asymptotic optimality of new restless bandit policies with O(1/√N) gap under unichain and aperiodicity conditions.
3/31/2026
Learning the Model While Learning Q: Finite-Time Sample Complexity of Online SyncMBQ
Theoretical analysis of sample complexity for model-based Q-learning, establishing finite-time convergence bounds for model-learning algorithms.
3/31/2026
Remedying uncertainty representations in visual inference through Explaining-Away Variational Autoencoders
Paper proposing Explaining-Away Variational Autoencoders to improve uncertainty representations in deep generative models for visual inference tasks.
3/31/2026
Recent Advances of Multimodal Continual Learning: A Comprehensive Survey
Survey of multimodal continual learning methods that enable models to learn from new data across multiple modalities while retaining previous knowledge without catastrophic forgetting.
3/31/2026
Transformers learn variable-order Markov chains in-context
Transformers learn variable-order Markov chains in-context with finite-sample accuracy analysis using context-tree weighting.
3/31/2026
Understanding SAM's Robustness to Noisy Labels through Gradient Down-weighting
Analysis of Sharpness-Aware Minimization robustness to label noise through gradient down-weighting at element-wise level.
3/31/2026
Scalable Neural Network Verification with Branch-and-bound Inferred Cutting Planes
Scalable neural network verification using branch-and-bound with inferred cutting planes instead of external MIP solvers.
3/31/2026
Gradient Compression Beyond Low-Rank: Wavelet Subspaces Compact Optimizer States
Wavelet subspace compression for optimizer states reduces memory during LLM training, improving upon low-rank approaches.
3/31/2026
Class-Imbalanced-Aware Adaptive Dataset Distillation for Scalable Pretrained Model on Credit Scoring
Dataset distillation for credit scoring models addressing class imbalance in pretrained models on tabular financial data.
3/31/2026
Binned Spectral Power Loss for Improved Prediction of Chaotic Systems
Binned spectral power loss function for improved deep learning predictions of chaotic multiscale dynamical systems.
3/31/2026
MM-DADM: Multimodal Drug-Aware Diffusion Model for Virtual Clinical Trials
Multimodal drug-aware diffusion model for ECG generation in virtual clinical trials with demographic disentanglement.
3/31/2026
Shifting Perspectives: Steering Vectors for Robust Bias Mitigation in LLMs
Steering vectors applied to LLM activations for bias mitigation across social dimensions like age, gender, and race.
3/31/2026
Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a Measurement
Large graph dataset and measurement framework for evaluating long-range interactions in graph representation learning.
3/31/2026
Measuring the (Un)Faithfulness of Concept-Based Explanations
Methods to measure faithfulness of concept-based explanations in deep vision models using surrogate models.
3/31/2026
OpenAVS: Training-Free Open-Vocabulary Audio Visual Segmentation with Foundational Models
Training-free audio-visual segmentation using foundational models for open-vocabulary pixel-level mask prediction.
3/31/2026
Large Language Models for Computer-Aided Design: A Survey
Survey of LLM integration with Computer-Aided Design tools, covering applications in 3D modeling and design workflows.
3/31/2026
Birch SGD: A Tree Graph Framework for Local and Asynchronous SGD Methods
Birch SGD framework represents distributed SGD methods as computation trees to unify analysis and design of optimization algorithms.
3/31/2026
Deep Latent Variable Model based Vertical Federated Learning with Flexible Alignment and Labeling Scenarios
Deep latent variable models for vertical federated learning with flexible alignment and labeling across feature-partitioned data.
3/31/2026
Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machine learning
Foundation models for time-series prediction often use simple parroting strategies rather than learning physics, revealing shared failure modes.
3/31/2026
FlowPure: Continuous Normalizing Flows for Adversarial Purification
FlowPure uses continuous normalizing flows for adversarial purification to remove perturbations from ML model inputs at inference time.
3/31/2026
Structured Agent Distillation for Large Language Model
Structured Agent Distillation compresses large LLM-based ReAct agents into smaller models while preserving reasoning and action consistency.
3/31/2026
CoDec: Prefix-Shared Decoding Kernel for LLMs
CoDec kernel optimizes LLM decoding by sharing prefix computation across multiple prompts to reduce memory-intensive KV cache access.
3/31/2026
Meet Me at the Arm: The Cooperative Multi-Armed Bandits Problem with Shareable Arms
Decentralized multi-player multi-armed bandits problem with unknown arm capacities and no collision sensing.
3/31/2026
Improving ideal MHD equilibrium accuracy with physics-informed neural networks
Physics-informed neural networks compute 3D magnetohydrodynamic equilibria by parametrizing Fourier modes and minimizing force residuals.
3/31/2026
Designing User-Centric Metrics for Evaluation of Counterfactual Explanations
User-centric evaluation metrics for counterfactual explanations in ML models, focusing on actionability and end-user preferences.