Saved in:
| Main Authors: | Hanashiro, Rafael, Jaillet, Patrick |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2312.13130 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Is Multi-Distribution Learning as Easy as PAC Learning: Sharp Rates with Bounded Label Noise
by: Hanashiro, Rafael, et al.
Published: (2026)
by: Hanashiro, Rafael, et al.
Published: (2026)
Multi-Timescale Primal Dual Hybrid Gradient with Application to Distributed Optimization
by: Zhang, Junhui, et al.
Published: (2025)
by: Zhang, Junhui, et al.
Published: (2025)
Choice-Model-Assisted Q-learning for Delayed-Feedback Revenue Management
by: Shen, Owen, et al.
Published: (2026)
by: Shen, Owen, et al.
Published: (2026)
Online Resource Allocation with Convex-set Machine-Learned Advice
by: Golrezaei, Negin, et al.
Published: (2023)
by: Golrezaei, Negin, et al.
Published: (2023)
Learning with Exact Invariances in Polynomial Time
by: Soleymani, Ashkan, et al.
Published: (2025)
by: Soleymani, Ashkan, et al.
Published: (2025)
Double Machine Learning Based Structure Identification from Temporal Data
by: Angelis, Emmanouil, et al.
Published: (2023)
by: Angelis, Emmanouil, et al.
Published: (2023)
Incentive-Aware Dynamic Resource Allocation under Long-Term Cost Constraints
by: Dai, Yan, et al.
Published: (2025)
by: Dai, Yan, et al.
Published: (2025)
A Universal Class of Sharpness-Aware Minimization Algorithms
by: Tahmasebi, Behrooz, et al.
Published: (2024)
by: Tahmasebi, Behrooz, et al.
Published: (2024)
Inaccurate Label Distribution Learning with Dependency Noise
by: Kou, Zhiqiang, et al.
Published: (2024)
by: Kou, Zhiqiang, et al.
Published: (2024)
A Single-Sample Polylogarithmic Regret Bound for Nonstationary Online Linear Programming
by: Xu, Haoran, et al.
Published: (2026)
by: Xu, Haoran, et al.
Published: (2026)
Learning Rate Schedules in the Presence of Distribution Shift
by: Fahrbach, Matthew, et al.
Published: (2023)
by: Fahrbach, Matthew, et al.
Published: (2023)
Incentives in Private Collaborative Machine Learning
by: Sim, Rachael Hwee Ling, et al.
Published: (2024)
by: Sim, Rachael Hwee Ling, et al.
Published: (2024)
Optimal Multi-Distribution Learning
by: Zhang, Zihan, et al.
Published: (2023)
by: Zhang, Zihan, et al.
Published: (2023)
On Calibration in Multi-Distribution Learning
by: Verma, Rajeev, et al.
Published: (2024)
by: Verma, Rajeev, et al.
Published: (2024)
HMDN: Hierarchical Multi-Distribution Network for Click-Through Rate Prediction
by: Lou, Xingyu, et al.
Published: (2024)
by: Lou, Xingyu, et al.
Published: (2024)
Incentivizing Truthfulness and Collaborative Fairness in Bayesian Learning
by: Sim, Rachael Hwee Ling, et al.
Published: (2026)
by: Sim, Rachael Hwee Ling, et al.
Published: (2026)
Dependable Distributed Training of Compressed Machine Learning Models
by: Malandrino, Francesco, et al.
Published: (2024)
by: Malandrino, Francesco, et al.
Published: (2024)
Neural Dueling Bandits: Preference-Based Optimization with Human Feedback
by: Verma, Arun, et al.
Published: (2024)
by: Verma, Arun, et al.
Published: (2024)
Local MixVR: Breaking the Communication-Sample Dependence in Distributed Learning
by: Dahan, Tehila, et al.
Published: (2026)
by: Dahan, Tehila, et al.
Published: (2026)
Distribution-Free Rates in Neyman-Pearson Classification
by: Kalan, Mohammadreza M., et al.
Published: (2024)
by: Kalan, Mohammadreza M., et al.
Published: (2024)
Online Scheduling for LLM Inference with KV Cache Constraints
by: Jaillet, Patrick, et al.
Published: (2025)
by: Jaillet, Patrick, et al.
Published: (2025)
ASAP: Unsupervised Post-training with Label Distribution Shift Adaptive Learning Rate
by: Park, Heewon, et al.
Published: (2025)
by: Park, Heewon, et al.
Published: (2025)
Convergence Theorems for Entropy-Regularized and Distributional Reinforcement Learning
by: Jhaveri, Yash, et al.
Published: (2025)
by: Jhaveri, Yash, et al.
Published: (2025)
On Distributional Dependent Performance of Classical and Neural Routing Solvers
by: Thyssens, Daniela, et al.
Published: (2025)
by: Thyssens, Daniela, et al.
Published: (2025)
Aggregation of Dependent Expert Distributions in Multimodal Variational Autoencoders
by: Mancisidor, Rogelio A, et al.
Published: (2025)
by: Mancisidor, Rogelio A, et al.
Published: (2025)
Distributed Networked Multi-task Learning
by: Hong, Lingzhou, et al.
Published: (2024)
by: Hong, Lingzhou, et al.
Published: (2024)
Convergence Rates for Distribution Matching with Sliced Optimal Transport
by: Thurin, Gauthier, et al.
Published: (2026)
by: Thurin, Gauthier, et al.
Published: (2026)
Fed-ADE: Adaptive Learning Rate for Federated Post-adaptation under Distribution Shift
by: Park, Heewon, et al.
Published: (2026)
by: Park, Heewon, et al.
Published: (2026)
Intelligent Learning Rate Distribution to reduce Catastrophic Forgetting in Transformers
by: Kenneweg, Philip, et al.
Published: (2024)
by: Kenneweg, Philip, et al.
Published: (2024)
Scalable Label Distribution Learning for Multi-Label Classification
by: Zhao, Xingyu, et al.
Published: (2023)
by: Zhao, Xingyu, et al.
Published: (2023)
Prompt Optimization with Human Feedback
by: Lin, Xiaoqiang, et al.
Published: (2024)
by: Lin, Xiaoqiang, et al.
Published: (2024)
Distributed and Rate-Adaptive Feature Compression
by: Deshmukh, Aditya, et al.
Published: (2024)
by: Deshmukh, Aditya, et al.
Published: (2024)
Label Distribution Learning with Biased Annotations by Learning Multi-Label Representation
by: Kou, Zhiqiang, et al.
Published: (2025)
by: Kou, Zhiqiang, et al.
Published: (2025)
Distributed Multi-Task Learning for Stochastic Bandits with Context Distribution and Stage-wise Constraints
by: Lin, Jiabin, et al.
Published: (2024)
by: Lin, Jiabin, et al.
Published: (2024)
Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity
by: Johnson, Emmeran, et al.
Published: (2023)
by: Johnson, Emmeran, et al.
Published: (2023)
Understanding the Robustness of Multi-modal Contrastive Learning to Distribution Shift
by: Xue, Yihao, et al.
Published: (2023)
by: Xue, Yihao, et al.
Published: (2023)
Deep Reinforcement Learning for Robotic Manipulation under Distribution Shift with Bounded Extremum Seeking
by: Saxena, Shaifalee, et al.
Published: (2026)
by: Saxena, Shaifalee, et al.
Published: (2026)
Distributed Multi-Head Learning Systems for Power Consumption Prediction
by: Syu, Jia-Hao, et al.
Published: (2025)
by: Syu, Jia-Hao, et al.
Published: (2025)
A Class of Dependent Random Distributions Based on Atom Skipping
by: Bi, Dehua, et al.
Published: (2023)
by: Bi, Dehua, et al.
Published: (2023)
A Family of Distributions of Random Subsets for Controlling Positive and Negative Dependence
by: Kawashima, Takahiro, et al.
Published: (2024)
by: Kawashima, Takahiro, et al.
Published: (2024)
Similar Items
-
Is Multi-Distribution Learning as Easy as PAC Learning: Sharp Rates with Bounded Label Noise
by: Hanashiro, Rafael, et al.
Published: (2026) -
Multi-Timescale Primal Dual Hybrid Gradient with Application to Distributed Optimization
by: Zhang, Junhui, et al.
Published: (2025) -
Choice-Model-Assisted Q-learning for Delayed-Feedback Revenue Management
by: Shen, Owen, et al.
Published: (2026) -
Online Resource Allocation with Convex-set Machine-Learned Advice
by: Golrezaei, Negin, et al.
Published: (2023) -
Learning with Exact Invariances in Polynomial Time
by: Soleymani, Ashkan, et al.
Published: (2025)