Saved in:
| Main Author: | Giorgio, Bruno |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.11601 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Neural Variance-aware Dueling Bandits with Deep Representation and Shallow Exploration
by: Oh, Youngmin, et al.
Published: (2025)
by: Oh, Youngmin, et al.
Published: (2025)
LLM Routing with Dueling Feedback
by: Chiang, Chao-Kai, et al.
Published: (2025)
by: Chiang, Chao-Kai, et al.
Published: (2025)
Federated Linear Dueling Bandits
by: Huang, Xuhan, et al.
Published: (2025)
by: Huang, Xuhan, et al.
Published: (2025)
Riemannian Dueling Optimization
by: Ren, Yuxuan, et al.
Published: (2026)
by: Ren, Yuxuan, et al.
Published: (2026)
Learning Tennis Strategy Through Curriculum-Based Dueling Double Deep Q-Networks
by: Mohan, Vishnu
Published: (2025)
by: Mohan, Vishnu
Published: (2025)
Deep Learning-Based Financial Time Series Forecasting via Sliding Window and Variational Mode Decomposition
by: Li, Luke
Published: (2025)
by: Li, Luke
Published: (2025)
Deep Reinforcement Learning based Triggering Function for Early Classifiers of Time Series
by: Renault, Aurélien, et al.
Published: (2025)
by: Renault, Aurélien, et al.
Published: (2025)
On Multivariate Financial Time Series Classification
by: Bournassenko, Grégory
Published: (2025)
by: Bournassenko, Grégory
Published: (2025)
Multi-Player Approaches for Dueling Bandits
by: Raveh, Or, et al.
Published: (2024)
by: Raveh, Or, et al.
Published: (2024)
Online Clustering of Dueling Bandits
by: Wang, Zhiyong, et al.
Published: (2025)
by: Wang, Zhiyong, et al.
Published: (2025)
Deep Learning for Financial Time Series: A Large-Scale Benchmark of Risk-Adjusted Performance
by: Saly-Kaufmann, Adir, et al.
Published: (2026)
by: Saly-Kaufmann, Adir, et al.
Published: (2026)
Biased Dueling Bandits with Stochastic Delayed Feedback
by: Yi, Bongsoo, et al.
Published: (2024)
by: Yi, Bongsoo, et al.
Published: (2024)
Contrastive Learning of Asset Embeddings from Financial Time Series
by: Dolphin, Rian, et al.
Published: (2024)
by: Dolphin, Rian, et al.
Published: (2024)
Time Series Foundation Models for Multivariate Financial Time Series Forecasting
by: Marconi, Ben A.
Published: (2025)
by: Marconi, Ben A.
Published: (2025)
Multi-period Learning for Financial Time Series Forecasting
by: Zhang, Xu, et al.
Published: (2025)
by: Zhang, Xu, et al.
Published: (2025)
Learning to Play 7 Wonders Duel Without Human Supervision
by: Paolini, Giovanni, et al.
Published: (2024)
by: Paolini, Giovanni, et al.
Published: (2024)
Selective Learning for Deep Time Series Forecasting
by: Fu, Yisong, et al.
Published: (2025)
by: Fu, Yisong, et al.
Published: (2025)
The Sampling Complexity of Condorcet Winner Identification in Dueling Bandits
by: Saad, El Mehdi, et al.
Published: (2026)
by: Saad, El Mehdi, et al.
Published: (2026)
Learning To Play Atari Games Using Dueling Q-Learning and Hebbian Plasticity
by: Salehin, Md Ashfaq
Published: (2024)
by: Salehin, Md Ashfaq
Published: (2024)
Learning Time-Inhomogeneous Markov Dynamics in Financial Time Series via Neural Parameterization
by: Rovirosa, Jan, et al.
Published: (2026)
by: Rovirosa, Jan, et al.
Published: (2026)
Adapting to the Unknown: Robust Meta-Learning for Zero-Shot Financial Time Series Forecasting
by: Liu, Anxian, et al.
Published: (2025)
by: Liu, Anxian, et al.
Published: (2025)
Fusing Reward and Dueling Feedback in Stochastic Bandits
by: Wang, Xuchuang, et al.
Published: (2025)
by: Wang, Xuchuang, et al.
Published: (2025)
Conversational Dueling Bandits in Generalized Linear Models
by: Yang, Shuhua, et al.
Published: (2024)
by: Yang, Shuhua, et al.
Published: (2024)
Linear and Neural Dueling Bandits with Delayed Feedback
by: Wang, Xiangyi, et al.
Published: (2026)
by: Wang, Xiangyi, et al.
Published: (2026)
Learning from Imperfect Human Feedback: a Tale from Corruption-Robust Dueling
by: Cheng, Yuwei, et al.
Published: (2024)
by: Cheng, Yuwei, et al.
Published: (2024)
A Deep Reinforcement Learning Framework For Financial Portfolio Management
by: Li, Jinyang
Published: (2024)
by: Li, Jinyang
Published: (2024)
Look Into the LITE in Deep Learning for Time Series Classification
by: Ismail-Fawaz, Ali, et al.
Published: (2024)
by: Ismail-Fawaz, Ali, et al.
Published: (2024)
LLM-Enhanced Reinforcement Learning for Time Series Anomaly Detection
by: Golchin, Bahareh, et al.
Published: (2026)
by: Golchin, Bahareh, et al.
Published: (2026)
Recycling History: Efficient Recommendations from Contextual Dueling Bandits
by: Sankagiri, Suryanarayana, et al.
Published: (2025)
by: Sankagiri, Suryanarayana, et al.
Published: (2025)
Utility-based Dueling Bandits as a Partial Monitoring Game
by: Gajane, Pratik, et al.
Published: (2015)
by: Gajane, Pratik, et al.
Published: (2015)
Scaling Law for Large-Scale Pre-Training Using Chaotic Time Series and Predictability in Financial Time Series
by: Takemoto, Yuki
Published: (2025)
by: Takemoto, Yuki
Published: (2025)
DP-Dueling: Learning from Preference Feedback without Compromising User Privacy
by: Saha, Aadirupa, et al.
Published: (2024)
by: Saha, Aadirupa, et al.
Published: (2024)
Graph Deep Learning for Time Series Forecasting
by: Cini, Andrea, et al.
Published: (2023)
by: Cini, Andrea, et al.
Published: (2023)
Hindsight Preference Optimization for Financial Time Series Advisory
by: Cui, Yanwei, et al.
Published: (2026)
by: Cui, Yanwei, et al.
Published: (2026)
Gradient Reduction Convolutional Neural Network Policy for Financial Deep Reinforcement Learning
by: Montazeri, Sina, et al.
Published: (2024)
by: Montazeri, Sina, et al.
Published: (2024)
Feel-Good Thompson Sampling for Contextual Dueling Bandits
by: Li, Xuheng, et al.
Published: (2024)
by: Li, Xuheng, et al.
Published: (2024)
Targeted Manipulation: Slope-Based Attacks on Financial Time-Series Data
by: Luszczynski, Dominik
Published: (2025)
by: Luszczynski, Dominik
Published: (2025)
Deep Learning For Time Series Analysis With Application On Human Motion
by: Ismail-Fawaz, Ali
Published: (2025)
by: Ismail-Fawaz, Ali
Published: (2025)
Event Detection in Time Series: Universal Deep Learning Approach
by: Azib, Menouar, et al.
Published: (2023)
by: Azib, Menouar, et al.
Published: (2023)
Duel-Evolve: Reward-Free Test-Time Scaling via LLM Self-Preferences
by: Karlekar, Sweta, et al.
Published: (2026)
by: Karlekar, Sweta, et al.
Published: (2026)
Similar Items
-
Neural Variance-aware Dueling Bandits with Deep Representation and Shallow Exploration
by: Oh, Youngmin, et al.
Published: (2025) -
LLM Routing with Dueling Feedback
by: Chiang, Chao-Kai, et al.
Published: (2025) -
Federated Linear Dueling Bandits
by: Huang, Xuhan, et al.
Published: (2025) -
Riemannian Dueling Optimization
by: Ren, Yuxuan, et al.
Published: (2026) -
Learning Tennis Strategy Through Curriculum-Based Dueling Double Deep Q-Networks
by: Mohan, Vishnu
Published: (2025)