#online learning
21 papers · 1 filter
Train Often, Deploy Selectively: Forward-Gated Model Replacement in Crypto Markets
Aditya Dutta
The paper proposes a deployment strategy called Shadow Before Swap (SBS) that evaluates retrained forecasting models off the live path and only promotes them when they show a suffi…
Learning to Persuade Privately Informed Receivers
I. Arda Vurankaya, Ufuk Topcu
The paper studies online Bayesian persuasion where a sender must influence a binary-action receiver who also receives private signals from an unknown fixed signaling scheme, and pr…
Kairos: Numerically Robust News Recommendation under Item Cold-Start via Cholesky-based LinUCB
Finn Hertsch
The paper introduces Kairos, a news recommendation framework that tackles item cold‑start by using a contextual bandit (LinUCB) with numerically stable Cholesky‑based rank‑1 update…
Self-Adaptive Learning and Model Predictive Control for Tracking Unknown Dynamics with No Regret
Atharva Navsalkar, Hongyu Zhou, Vasileios Tzoumas
The paper introduces a self-adaptive online learning and model predictive control framework that learns multiple predictors on the fly to track unknown, possibly switching target d…
Online Neural Space Time Memory for Dynamic Novel View Synthesis
Baback Elmieh, Lynn Tsai, Zeman Li +8
The paper introduces an online neural space‑time memory system that periodically updates a persistent memory while applying it per frame, enabling real‑time novel view synthesis fo…
General theory of monitored Quantum Reservoir Computing
Oriol MorguÃ-Sancho, Gian Luca Giorgi, Gonzalo Manzano +1
The paper develops a unified theoretical framework for quantum reservoir computing that incorporates various types of measurements, showing how measurement back‑action can be harne…