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.
3/31/2026
MicroMix: Efficient Mixed-Precision Quantization with Microscaling Formats for Large Language Models
MicroMix: mixed-precision quantization method using microscaling formats for efficient LLM inference on NVIDIA Blackwell hardware.
3/31/2026
Forgetting: A New Mechanism Towards Better Large Language Model Fine-tuning
Novel fine-tuning mechanism for LLMs that addresses data quality/volume issues through controlled forgetting to improve domain adaptation.
3/31/2026
PENGUIN: Enhancing Transformer with Periodic-Nested Group Attention for Long-term Time Series Forecasting
PENGUIN: Transformer variant with periodic-nested group attention mechanism for improved long-term time series forecasting.
3/31/2026
Initialization Schemes for Kolmogorov-Arnold Networks: An Empirical Study
Empirical study of initialization schemes for Kolmogorov-Arnold Networks, proposing theory-driven approaches to improve training of spline-based KANs.
3/31/2026
No Need for Learning to Defer? A Training Free Deferral Framework to Multiple Experts through Conformal Prediction
Training-free framework for deferring predictions to multiple experts using conformal prediction without retraining.
3/31/2026
ReTrack: Data Unlearning in Diffusion Models through Redirecting the Denoising Trajectory
ReTrack enables data unlearning in diffusion models via importance sampling to remove memorized training data influence.
3/31/2026
GaussianPSL: Soft partitioning for complex PSL problem
GaussianPSL framework for multi-objective optimization with soft partitioning handling complex discontinuous and degenerate Pareto frontiers.
3/31/2026
Learning Genetic Circuit Modules with Neural Networks: Full Version
Neural network approach to learning modular genetic circuit functions in synthetic biology from input/output data.
3/31/2026
Asynchronous Policy Gradient Aggregation for Efficient Distributed Reinforcement Learning
Algorithms for distributed RL with policy gradients under asynchronous parallel computation and communication.
3/31/2026
LLM-Assisted Emergency Triage Benchmark: Bridging Hospital-Rich and MCI-Like Field Simulation
Benchmark for LLM-assisted emergency triage from MIMIC-IV-ED database with preprocessing for rapid patient deterioration prediction.
3/31/2026
To Augment or Not to Augment? Diagnosing Distributional Symmetry Breaking
Method for diagnosing when data augmentation and equivariant architectures improve or harm generalization under distribution asymmetry.
3/31/2026
Q-Learning with Shift-Aware Upper Confidence Bound in Non-Stationary Reinforcement Learning
Q-learning algorithm for non-stationary RL with distribution shifts under both episodic and infinite-horizon settings.
3/31/2026
LLM as an Algorithmist: Enhancing Anomaly Detectors via Programmatic Synthesis
Uses LLMs to programmatically synthesize anomaly detectors for tabular data without direct processing of raw data for privacy.
3/31/2026
Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
ACE framework evolves context for self-improving LLM agents, addressing brevity bias and context collapse in iterative refinement.
3/31/2026
Mitigating Premature Exploitation in Particle-based Monte Carlo for Inference-Time Scaling
Mitigates premature exploitation in particle filtering for inference-time scaling of language models using process reward models.
3/31/2026
TabPFN-Wide: Continued Pre-Training for Extreme Feature Counts
TabPFN-Wide extends prior-data fitted networks for tabular data with extreme feature counts in biomedicine applications.
3/31/2026
Constraints-of-Thought: A Framework for Constrained Reasoning in Language-Model-Guided Search
Constraints-of-Thought framework enables LLMs to perform constrained multi-step reasoning while satisfying symbolic constraints and user intent.
3/31/2026
PANTHER: Generative Pretraining Beyond Language for Sequential User Behavior Modeling
PANTHER applies generative pretraining to model user behavior sequences beyond language, using multi-dimensional action attributes.
3/31/2026
Learning When Not to Learn: Risk-Sensitive Abstention in Bandits with Unbounded Rewards
Bandit algorithm for high-stakes sequential decision-making that learns when to abstain from actions with irreparable consequences.
3/31/2026
Dense and Diverse Goal Coverage in Multi Goal Reinforcement Learning
RL algorithm for learning policies that maximize return while inducing dispersed state distributions across multiple reward sources.