The Parameterized Diffusion Policy (PDP) framework transforms diffusion models from general stochastic generators into precise, steerable tools for learning and adapting complex robotic behaviors by e…

The paper proposes LAG-Fusion, a framework for asynchronous multimodal diffusion policy composition with latency-aware guidance fusion.

This paper introduces DADiff, a diffusion-based framework for domain adaptation in reinforcement learning, which estimates dynamics mismatch based on generative trajectory deviation.

DriftQL introduces a novel, efficient offline RL method that combines a drift-based behavioral regularizer with critic-driven policy improvement, achieving state-of-the-art performance while maintaini…

The paper proposes TDPM, a time-aware diffusion model for generative recommendation, which significantly improves recommendation accuracy by explicitly modeling the non-stationary, time-evolving natur…

This paper introduces Spatially Conditioned Diffusion Policy (SCDP), a single-camera manipulation system that uses end-effector trajectories as visual attention anchors.

This paper investigates the effects of structural and temporal inhomogeneities in decentralised federated learning and shows that they significantly slow down the convergence process.

This paper establishes a connection between neural network approximation of score functions and approximation of probability distributions generated by reverse diffusion models.

This paper analyzes the bid-ask spread and welfare in the Glosten-Milgrom model when the market maker observes a noisy, privacy-protected trade direction signal, deriving a specific 'privacy subsidy'…

This paper extends the privacy subsidy concept from the single-period Kyle model to continuous time, deriving a closed-form expression for the cumulative expected transfer (privacy subsidy) in a conti…

This paper introduces a stochastic differential equation approximation for linear Temporal Difference (TD) learning under Markovian noise, explaining the constant-stepsize error floor.

This paper models Ethereum's mempool as a dynamic scheduling problem using an MDP, showing that dynamic pricing stabilizes the system and maximizes long-run rewards, and that the optimal policy conver…

This paper proposes a fairness-aware adaptation of graph-based diffusion methods by modifying the Laplacian operator to mitigate bias-related components.

TIMEGATE introduces a resource-aware policy layer that manages continual ML adaptation by dynamically budgeting time and evaluation resources, achieving significant compute and energy savings without…

The paper proposes a unified, constrained optimization framework using KL divergence and likelihood constraints to achieve effective and principled unlearning in diffusion models.

The paper proposes S2L-PO, a framework that uses smaller, naturally diverse models as structured explorers to enhance the policy-level diversity and performance of larger language models during traini…

This paper introduces mean field reinforcement learning through Markov decision processes in large-population stochastic control, developing the necessary framework for representative-agent learning,…

This paper addresses the problem of inferring a directed network from nodal measurements using graph convolutional filters and identifies the diffusion filter and network topology.