Adaptive learning approach for trajectory prediction in autonomous vehicles addressing long-tail distribution of safety-critical scenarios.
Statistical method for handling non-reciprocal pairwise comparisons in decision analysis and preference modeling with noise calibration.
Optimization technique integrating layout propagation into GEMM operations to reduce memory overhead in sequential matrix multiplications for ML workloads.
Kolmogorov-Arnold Networks applied to interpret crystalline energy landscapes for physics-informed property prediction with improved explainability.
Zero-shot depth reconstruction from UAV imagery using diffusion models for real-time geospatial tasks without task-specific retraining.
Studies optimality of Bayesian neural networks through statistical decision theory lens, analyzing minimaxity and admissibility properties.
Policy-driven model reconstructs protein residue networks to predict folding pathways correlating with experimental folding rates.
Fine-tuning integrity verification for neural networks using norm, rank, and sparsity certificates to detect backdoors and unauthorized changes.
Protocol enabling two AI agents to conduct secret conversations while producing transcripts indistinguishable from normal interaction.
Domain-specific BERT model trained on Turkish legal texts for NLP applications in legal technology.
Multi-modal sensor fusion framework using hybrid attention for 3D object detection in autonomous driving.
Compressed sensing with hybrid deterministic-random sampling from unitary matrix rows, providing denoising guarantees.
SkillX framework automatically constructs reusable skill knowledge bases for LLM agents, enabling efficient learning and generalization across tasks.
Hybrid quantum-classical Fourier Neural Operator for surrogate modeling of laser processing in PDE solvers.
Sparse identification of nonlinear dynamics with autoencoder for discovering system equations from noisy data.
Synthetic sandbox environment for training ML engineering agents that can handle expensive ML verification tasks via fast mock pipelines.
Improves exploration in reinforcement learning with verifiable rewards for LLMs using bidirectional entropy modulation instead of standard regularization.
QED-Nano trains small neural networks to prove mathematical theorems, enabling reproducible and efficient theorem-proving without large models.
Verification and analysis of symbolic properties in deep reinforcement learning agents for systems and networking tasks.
Physics-informed neural networks for optimal control of PDEs using direct and indirect formulations.
Method for early stopping in large language model reasoning by analyzing confidence dynamics to reduce computational cost without degrading performance.
Addresses value hallucination in Dyna-style reinforcement learning agents by using multistep predecessor models to improve model-based RL.
State-space models with relational inductive biases for multivariate time series prediction using graph structures.
Neural networks applied to contextual multi-armed bandits, comparing epsilon-greedy, Thompson Sampling, and UCB techniques for exploration-exploitation trade-offs.
GraphL0BnB learns sparse precision matrices in Gaussian graphical models using discrete optimization with ℓ0 penalties.
Federated transfer learning framework addressing data heterogeneity and privacy across distributed sites using differential privacy.
FedScalar reduces federated learning communication overhead by encoding high-dimensional updates as two scalar values per agent per round.
EventFlow uses flow matching to forecast temporal point processes with irregular event intervals, improving on autoregressive neural approaches.
Open-source RL framework for vehicle routing problems, extending reinforcement learning to discrete optimization in operations research.
Framework for training verifiably Lyapunov-stable neural controllers using branch-and-bound certified training within region-of-attraction.
Safe active learning method using amortized neural policies for real-time data acquisition with safety constraints, replacing repeated GP updates.
Paper on causal bandit algorithms for unknown DAGs using confidence bounds and backdoor adjustment for intervention discovery.
Research on finite-horizon restless bandit problems reformulated as thresholding with improved sample complexity and policy convergence.
arXiv paper on decentralized learning using consensus gradient descent with privacy and communication constraints across networked devices.
Research paper on model stealing attacks and defenses, analyzing vulnerabilities of ML services to adversarial extraction through query access.
Analytical framework explaining spectral bias in diffusion model training dynamics using Gaussian equivalence and probability-flow ODEs.
RaPA improves transferable targeted adversarial attacks by random parameter pruning to reduce reliance on surrogate model subsets.
Finite-time convergence analysis for average-reward Q-learning with adaptive stepsizes, showing O(1/k) convergence rate.
First mechanistic interpretability framework for VAEs using multi-level causal interventions to understand generative model representations.
FABLE framework investigates adversarial attacks on deep learning weather forecasting models and proposes targeted attack methods.
Proposes fairness constraints using difference-of-convex programming for partial fairness in ML predictions across percentile intervals.
Introduces Bayesian ablation framework for interpreting learned task representations in neural networks through probabilistic inference.
MSDformer extends discrete token modeling for time series generation using multi-scale transformer architecture to capture temporal patterns.
SoSBench benchmarks safety alignment of LLMs across six scientific domains with sophisticated risks beyond basic misuse scenarios.
Studies the problem of using LLMs as judges for evaluating LLM outputs, addressing epistemic uncertainty in judge quality beyond sampling variability.
K-Steering enables unified multi-attribute control of LLMs at inference time using non-linear classifiers on hidden activations to handle attribute interference.
MLorc proposes momentum low-rank compression for memory-efficient LLM fine-tuning, reducing memory demands compared to LoRA while maintaining performance.
SFBD Flow framework trains diffusion models on corrupted/noisy data with clean samples to reduce privacy risks and improve convergence in generative modeling.
Token significance approach in RL for efficient LLM reasoning by identifying and prioritizing important tokens over length optimization.
Federated Item Response Theory (FedIRT) framework enabling distributed psychometric estimation without centralizing raw response data.