How to Boost Any Loss Function
Fuente:
arXiv
Saved in:
| Main Authors: | Nock, Richard, Mansour, Yishay |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Boosting gets full Attention for Relational Learning
by: Guillame-Bert, Mathieu, et al.
Published: (2024)
by: Guillame-Bert, Mathieu, et al.
Published: (2024)
Generative Forests
by: Nock, Richard, et al.
Published: (2023)
by: Nock, Richard, et al.
Published: (2023)
Seasoning Generative Models for a Generalization Aftertaste
by: Husain, Hisham, et al.
Published: (2026)
by: Husain, Hisham, et al.
Published: (2026)
Tempered Calculus for ML: Application to Hyperbolic Model Embedding
by: Nock, Richard, et al.
Published: (2024)
by: Nock, Richard, et al.
Published: (2024)
Fusing Rewards and Preferences in Reinforcement Learning
by: Khorasani, Sadegh, et al.
Published: (2025)
by: Khorasani, Sadegh, et al.
Published: (2025)
Local-Order Auxiliary Losses Can Improve Autoencoder Reconstruction
by: Dam, Harvey, et al.
Published: (2025)
by: Dam, Harvey, et al.
Published: (2025)
Explore the Loss space with Hill-ADAM
by: Manikandan, Meenakshi, et al.
Published: (2025)
by: Manikandan, Meenakshi, et al.
Published: (2025)
CAMRI Loss: Improving Recall of a Specific Class without Sacrificing Accuracy
by: Nishiyama, Daiki, et al.
Published: (2022)
by: Nishiyama, Daiki, et al.
Published: (2022)
How Many Ratings per Item are Necessary for Reliable Significance Testing?
by: Homan, Christopher, et al.
Published: (2024)
by: Homan, Christopher, et al.
Published: (2024)
BOND: License to Train with Black-Box Functions
by: Clark, Andrew, et al.
Published: (2025)
by: Clark, Andrew, et al.
Published: (2025)
DPO Unchained: Your Training Algorithm is Secretly Disentangled in Human Choice Theory
by: Zhou, Wenxuan, et al.
Published: (2025)
by: Zhou, Wenxuan, et al.
Published: (2025)
Deep Memory Search: A Metaheuristic Approach for Optimizing Heuristic Search
by: Hedar, Abdel-Rahman, et al.
Published: (2024)
by: Hedar, Abdel-Rahman, et al.
Published: (2024)
Hard Samples, Bad Labels: Robust Loss Functions That Know When to Back Off
by: Pellegrino, Nicholas, et al.
Published: (2025)
by: Pellegrino, Nicholas, et al.
Published: (2025)
Convergence of Distributionally Robust Q-Learning with Linear Function Approximation
by: Mandal, Saptarshi, et al.
Published: (2025)
by: Mandal, Saptarshi, et al.
Published: (2025)
Relating Misfit to Gain in Weak-to-Strong Generalization Beyond the Squared Loss
by: Mulgund, Abhijeet, et al.
Published: (2025)
by: Mulgund, Abhijeet, et al.
Published: (2025)
AI and Machine Learning Approaches for Predicting Nanoparticles Toxicity The Critical Role of Physiochemical Properties
by: Yousaf, Iqra
Published: (2024)
by: Yousaf, Iqra
Published: (2024)
KerZOO: Kernel Function Informed Zeroth-Order Optimization for Accurate and Accelerated LLM Fine-Tuning
by: Mi, Zhendong, et al.
Published: (2025)
by: Mi, Zhendong, et al.
Published: (2025)
Understanding Boolean Function Learnability on Deep Neural Networks: PAC Learning Meets Neurosymbolic Models
by: Nicolau, Marcio, et al.
Published: (2020)
by: Nicolau, Marcio, et al.
Published: (2020)
2Mamba2Furious: Linear in Complexity, Competitive in Accuracy
by: Mongaras, Gabriel, et al.
Published: (2026)
by: Mongaras, Gabriel, et al.
Published: (2026)
On Semantic Loss Fine-Tuning Approach for Preventing Model Collapse in Causal Reasoning
by: Deshmukh, Pratik, et al.
Published: (2026)
by: Deshmukh, Pratik, et al.
Published: (2026)
How VLAs Fail Differently: Black-Box Action Monitoring Reveals Architecture-Specific Failure Signatures
by: Gupta, Krishnam
Published: (2026)
by: Gupta, Krishnam
Published: (2026)
FluidWorld: Reaction-Diffusion Dynamics as a Predictive Substrate for World Models
by: Polly, Fabien
Published: (2026)
by: Polly, Fabien
Published: (2026)
I-GLIDE: Input Groups for Latent Health Indicators in Degradation Estimation
by: Thil, Lucas, et al.
Published: (2025)
by: Thil, Lucas, et al.
Published: (2025)
A Novel Loss Function for Deep Learning Based Daily Stock Trading System
by: Guo, Ruoyu, et al.
Published: (2025)
by: Guo, Ruoyu, et al.
Published: (2025)
Loss-Complexity Landscape and Model Structure Functions
by: Kolpakov, Alexander
Published: (2025)
by: Kolpakov, Alexander
Published: (2025)
Potential-Based Reward Shaping For Intrinsic Motivation
by: Forbes, Grant C., et al.
Published: (2024)
by: Forbes, Grant C., et al.
Published: (2024)
Interpretable Multi-View Clustering
by: Jiang, Mudi, et al.
Published: (2024)
by: Jiang, Mudi, et al.
Published: (2024)
The Bayesian Confidence (BACON) Estimator for Deep Neural Networks
by: Kee, Patrick D., et al.
Published: (2024)
by: Kee, Patrick D., et al.
Published: (2024)
Pre-Ictal Seizure Prediction Using Personalized Deep Learning
by: Jaddu, Shriya, et al.
Published: (2024)
by: Jaddu, Shriya, et al.
Published: (2024)
xLSTM-Mixer: Multivariate Time Series Forecasting by Mixing via Scalar Memories
by: Kraus, Maurice, et al.
Published: (2024)
by: Kraus, Maurice, et al.
Published: (2024)
Securing Reliability: A Brief Overview on Enhancing In-Context Learning for Foundation Models
by: Huang, Yunpeng, et al.
Published: (2024)
by: Huang, Yunpeng, et al.
Published: (2024)
Representation learning with CGAN for casual inference
by: Weng, Zhaotian, et al.
Published: (2024)
by: Weng, Zhaotian, et al.
Published: (2024)
Data-Incremental Continual Offline Reinforcement Learning
by: Gai, Sibo, et al.
Published: (2024)
by: Gai, Sibo, et al.
Published: (2024)
Adaptive Epsilon Adversarial Training for Robust Gravitational Wave Parameter Estimation Using Normalizing Flows
by: Yang, Yiqian, et al.
Published: (2024)
by: Yang, Yiqian, et al.
Published: (2024)
Normalization Layer Per-Example Gradients are Sufficient to Predict Gradient Noise Scale in Transformers
by: Gray, Gavia, et al.
Published: (2024)
by: Gray, Gavia, et al.
Published: (2024)
Potential-Based Intrinsic Motivation: Preserving Optimality With Complex, Non-Markovian Shaping Rewards
by: Forbes, Grant C., et al.
Published: (2024)
by: Forbes, Grant C., et al.
Published: (2024)
CPT: Competence-progressive Training Strategy for Few-shot Node Classification
by: Yan, Qilong, et al.
Published: (2024)
by: Yan, Qilong, et al.
Published: (2024)
Learning Useful Representations of Recurrent Neural Network Weight Matrices
by: Herrmann, Vincent, et al.
Published: (2024)
by: Herrmann, Vincent, et al.
Published: (2024)
New Paradigm of Adversarial Training: Releasing Accuracy-Robustness Trade-Off via Dummy Class
by: Wang, Yanyun, et al.
Published: (2024)
by: Wang, Yanyun, et al.
Published: (2024)
RobustBlack: Challenging Black-Box Adversarial Attacks on State-of-the-Art Defenses
by: Djilani, Mohamed, et al.
Published: (2024)
by: Djilani, Mohamed, et al.
Published: (2024)
Similar Items
-
Boosting gets full Attention for Relational Learning
by: Guillame-Bert, Mathieu, et al.
Published: (2024) -
Generative Forests
by: Nock, Richard, et al.
Published: (2023) -
Seasoning Generative Models for a Generalization Aftertaste
by: Husain, Hisham, et al.
Published: (2026) -
Tempered Calculus for ML: Application to Hyperbolic Model Embedding
by: Nock, Richard, et al.
Published: (2024) -
Fusing Rewards and Preferences in Reinforcement Learning
by: Khorasani, Sadegh, et al.
Published: (2025)