arXiv:2606. 14636v1 Announce Type: new Abstract: Control-function instrumental variable estimators need a first-stage residual, not merely a first-stage prediction.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2606. 14636v2 Announce Type: replace Abstract: Control-function instrumental-variable estimators pass an estimated first-stage residual to an outcome model.
arXiv:2606.14636v3 Announce Type: replace Abstract: Many two-stage estimators assess the first-stage learner by prediction error, even when the next stage uses its residual. In control-function instr...
arXiv:2608. 13096v1 Announce Type: new Abstract: Limit order book (LOB) simulators are most useful to practitioners when they combine realistic market dynamics, computationally efficient sampling, controllable scenario generation, and the ability to generalize beyond the instruments seen during training---properties that existing agent-based and deep generative simulators provide only partially.
arXiv:2607. 07665v1 Announce Type: new Abstract: Classifier-free guidance (CFG) is the standard way to strengthen class-conditioning in diffusion and flow-matching samplers, yet at large guidance it oversaturates and destabilizes, symptoms practitioners suppress with more steps or limited-interval schedules.
I‑SplineFlow introduces a new way to learn monotone spline stochastic interpolant schedulers for few‑step generation with pretrained diffusion and flow models. By parameterizing the scheduler with integrated monotone splines (I‑splines), the method decouples polynomial degree from the number of mixture weights, enabling compact support, better‑conditioned Jacobians, and strictly monotone signal‑to‑noise ratios without ordering constraints. Experiments on EDM, ReFlow, and Simple ReFlow show that I‑SplineFlow consistently improves few‑step FID over Bézier scheduling, especially at low NFEs, while training in only minutes.
arXiv:2606. 14801v1 Announce Type: cross Abstract: Flow-matching and diffusion policies are expressive action generators, but optimizing them with temporal-difference reinforcement learning (RL) remains difficult.