arXiv Machine Learning

Beyond the Smile: A Hybrid Convolutional VAE for Crypto Volatility Surfaces

arXiv:2606. 16961v1 Announce Type: new Abstract: We present a convolutional variational autoencoder for cryptocurrency implied-volatility surfaces, together with a deployable predictor that combines it with a quadratic smile re-fit through a deterministic per-tenor routing rule.

arXiv AI
Jun 17

PIVOT: Bridging Black-Scholes Implied-Volatility and Price Objectives via Differentiable J\"ackel Operator

arXiv:2606. 17065v1 Announce Type: cross Abstract: Modern option-learning systems operate in two coordinates: price space, where markets quote and no-arbitrage constraints are most naturally enforced, and implied volatility (IV) space, where volatility surfaces are smoothed, regularized, and evaluated.

By Raeid Saqur, Yannick Limmer, Anastasis Kratsios, Blanka Horvath, Hans Buehler
arXiv AI
Jun 3

Exact equivariance, kept through training, buys zero-shot generalisation across the symmetry group

arXiv:2606. 03003v1 Announce Type: cross Abstract: A latent world model built from an equivariant encoder $E$ and an equivariant predictor $f$ inherits a provable symmetry of its training loss: when the world's dynamics genuinely carries a group $G$ acting on latents by an orthogonal representation $\rho(g)$, the one-step prediction relMSE is exactly invariant across the whole group, so fitting the dynamics on a restricted slice of orientations mathematically determines it on the entire orbit (j\v{u} y\=i f\v{a}n s\=an).

By Hongbo Wang (Stony Brook University)
arXiv Machine Learning
6d ago

FunnelCausalNet: Funnel-aware Joint Conversion-Revenue Uplift for Multi-tier Coupon Allocation

arXiv:2608. 11675v1 Announce Type: new Abstract: Coupon campaigns seek to lift both conversion and revenue, but gross merchandise value (GMV) follows a deterministic funnel from conversion to conditional order value and is zero-inflated and heavy-tailed.

By Yu Zhang (AMap Alibaba Group, Beijing, China), Zhihan Wang (AMap Alibaba Group, Beijing, China), Guanlin Chen (AMap Alibaba Group, Beijing, China), Min Jiang (AMap Alibaba Group, Beijing, China), Shuai Li (AMap Alibaba Group, Beijing, China)
arXiv Machine Learning
Jul 13

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility

arXiv:2510. 04487v5 Announce Type: replace Abstract: While accuracy is a critical requirement for time series forecasting, an equally important desideratum is reasonable forecast volatility across forecast creation dates (FCDs).

By Willa Potosnak, Malcolm Wolff, Mengfei Cao, Ruijun Ma, Tatiana Konstantinova, Dmitry Efimov, Michael W. Mahoney, Boris Oreshkin, Kin G. Olivares