Optimistic Rates for Learning from Label Proportions
Fuente:
arXiv
Saved in:
| Main Authors: | Li, Gene, Chen, Lin, Javanmard, Adel, Mirrokni, Vahab |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Understanding the Role of Training Data in Test-Time Scaling
by: Javanmard, Adel, et al.
Published: (2025)
by: Javanmard, Adel, et al.
Published: (2025)
Theoretical Perspectives on Data Quality and Synergistic Effects in Pre- and Post-Training Reasoning Models
by: Javanmard, Adel, et al.
Published: (2026)
by: Javanmard, Adel, et al.
Published: (2026)
Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions
by: Javanmard, Adel, et al.
Published: (2024)
by: Javanmard, Adel, et al.
Published: (2024)
Learning Rate Schedules in the Presence of Distribution Shift
by: Fahrbach, Matthew, et al.
Published: (2023)
by: Fahrbach, Matthew, et al.
Published: (2023)
PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses
by: Javanmard, Adel, et al.
Published: (2024)
by: Javanmard, Adel, et al.
Published: (2024)
Lattice: Learning to Efficiently Compress the Memory
by: Karami, Mahdi, et al.
Published: (2025)
by: Karami, Mahdi, et al.
Published: (2025)
Improving the Variance of Differentially Private Randomized Experiments through Clustering
by: Javanmard, Adel, et al.
Published: (2023)
by: Javanmard, Adel, et al.
Published: (2023)
Differentially Private Synthetic Data Release for Topics API Outputs
by: Dick, Travis, et al.
Published: (2025)
by: Dick, Travis, et al.
Published: (2025)
Titans: Learning to Memorize at Test Time
by: Behrouz, Ali, et al.
Published: (2024)
by: Behrouz, Ali, et al.
Published: (2024)
Nested Learning: The Illusion of Deep Learning Architectures
by: Behrouz, Ali, et al.
Published: (2025)
by: Behrouz, Ali, et al.
Published: (2025)
Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing
by: Javanmard, Adel, et al.
Published: (2025)
by: Javanmard, Adel, et al.
Published: (2025)
TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate
by: Zandieh, Amir, et al.
Published: (2025)
by: Zandieh, Amir, et al.
Published: (2025)
It's All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization
by: Behrouz, Ali, et al.
Published: (2025)
by: Behrouz, Ali, et al.
Published: (2025)
Sampling and Loss Weights in Multi-Domain Training
by: Salmani, Mahdi, et al.
Published: (2025)
by: Salmani, Mahdi, et al.
Published: (2025)
ECO: Quantized Training without Full-Precision Master Weights
by: Nikdan, Mahdi, et al.
Published: (2026)
by: Nikdan, Mahdi, et al.
Published: (2026)
PolarQuant: Quantizing KV Caches with Polar Transformation
by: Han, Insu, et al.
Published: (2025)
by: Han, Insu, et al.
Published: (2025)
Memory Caching: RNNs with Growing Memory
by: Behrouz, Ali, et al.
Published: (2026)
by: Behrouz, Ali, et al.
Published: (2026)
SubGen: Token Generation in Sublinear Time and Memory
by: Zandieh, Amir, et al.
Published: (2024)
by: Zandieh, Amir, et al.
Published: (2024)
Retraining with Predicted Hard Labels Provably Increases Model Accuracy
by: Das, Rudrajit, et al.
Published: (2024)
by: Das, Rudrajit, et al.
Published: (2024)
Forming Auxiliary High-confident Instance-level Loss to Promote Learning from Label Proportions
by: Ma, Tianhao, et al.
Published: (2024)
by: Ma, Tianhao, et al.
Published: (2024)
DeepCrossAttention: Supercharging Transformer Residual Connections
by: Heddes, Mike, et al.
Published: (2025)
by: Heddes, Mike, et al.
Published: (2025)
Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation
by: Havaldar, Shreyas, et al.
Published: (2023)
by: Havaldar, Shreyas, et al.
Published: (2023)
Agnostic Reinforcement Learning: Foundations and Algorithms
by: Li, Gene
Published: (2025)
by: Li, Gene
Published: (2025)
TNT: Improving Chunkwise Training for Test-Time Memorization
by: Li, Zeman, et al.
Published: (2025)
by: Li, Zeman, et al.
Published: (2025)
Optimistic Reinforcement Learning with Quantile Objectives
by: Alipour-Vaezi, Mohammad, et al.
Published: (2025)
by: Alipour-Vaezi, Mohammad, et al.
Published: (2025)
Understanding Transformer Reasoning Capabilities via Graph Algorithms
by: Sanford, Clayton, et al.
Published: (2024)
by: Sanford, Clayton, et al.
Published: (2024)
Optimistic Policy Regularization
by: Pham, Mai, et al.
Published: (2026)
by: Pham, Mai, et al.
Published: (2026)
Optimistic Exploration for Risk-Averse Constrained Reinforcement Learning
by: McCarthy, James, et al.
Published: (2025)
by: McCarthy, James, et al.
Published: (2025)
Optimistic Regret Bounds for Online Learning in Adversarial Markov Decision Processes
by: Moon, Sang Bin, et al.
Published: (2024)
by: Moon, Sang Bin, et al.
Published: (2024)
Optimistic Policy Learning under Pessimistic Adversaries with Regret and Violation Guarantees
by: Ganguly, Sourav, et al.
Published: (2026)
by: Ganguly, Sourav, et al.
Published: (2026)
Aletheia tackles FirstProof autonomously
by: Feng, Tony, et al.
Published: (2026)
by: Feng, Tony, et al.
Published: (2026)
The Role of Environment Access in Agnostic Reinforcement Learning
by: Krishnamurthy, Akshay, et al.
Published: (2025)
by: Krishnamurthy, Akshay, et al.
Published: (2025)
Optimistic Gradient Learning with Hessian Corrections for High-Dimensional Black-Box Optimization
by: Kfir, Yedidya, et al.
Published: (2025)
by: Kfir, Yedidya, et al.
Published: (2025)
General Exploratory Bonus for Optimistic Exploration in RLHF
by: Li, Wendi, et al.
Published: (2025)
by: Li, Wendi, et al.
Published: (2025)
Optimistic World Models: Efficient Exploration in Model-Based Deep Reinforcement Learning
by: Mete, Akshay, et al.
Published: (2026)
by: Mete, Akshay, et al.
Published: (2026)
Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm
by: Vakili, Sattar, et al.
Published: (2024)
by: Vakili, Sattar, et al.
Published: (2024)
An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints
by: Zhu, Jiahui, et al.
Published: (2025)
by: Zhu, Jiahui, et al.
Published: (2025)
Optimistic Thompson Sampling for No-Regret Learning in Unknown Games
by: Li, Yingru, et al.
Published: (2024)
by: Li, Yingru, et al.
Published: (2024)
Semantic-guided Representation Learning for Multi-Label Recognition
by: Zhang, Ruhui, et al.
Published: (2025)
by: Zhang, Ruhui, et al.
Published: (2025)
Provable Last-Iterate Convergence for Multi-Objective Safe LLM Alignment via Optimistic Primal-Dual
by: Li, Yining, et al.
Published: (2026)
by: Li, Yining, et al.
Published: (2026)
Similar Items
-
Understanding the Role of Training Data in Test-Time Scaling
by: Javanmard, Adel, et al.
Published: (2025) -
Theoretical Perspectives on Data Quality and Synergistic Effects in Pre- and Post-Training Reasoning Models
by: Javanmard, Adel, et al.
Published: (2026) -
Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions
by: Javanmard, Adel, et al.
Published: (2024) -
Learning Rate Schedules in the Presence of Distribution Shift
by: Fahrbach, Matthew, et al.
Published: (2023) -
PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses
by: Javanmard, Adel, et al.
Published: (2024)