Imagen de Portada

Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Javanmard, Adel, Chen, Lin, Mirrokni, Vahab, Badanidiyuru, Ashwinkumar, Fu, Gang
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning Artificial Intelligence Statistics Theory
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing
por: Javanmard, Adel, et al.
Publicado: (2025)

Optimistic Rates for Learning from Label Proportions
por: Li, Gene, et al.
Publicado: (2024)

Understanding the Role of Training Data in Test-Time Scaling
por: Javanmard, Adel, et al.
Publicado: (2025)

Theoretical Perspectives on Data Quality and Synergistic Effects in Pre- and Post-Training Reasoning Models
por: Javanmard, Adel, et al.
Publicado: (2026)

Model Monotonicity in Autobidding Auctions: When Do Better Predictions Lead to Better Outcomes?
por: Badanidiyuru, Ashwinkumar
Publicado: (2026)

PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses
por: Javanmard, Adel, et al.
Publicado: (2024)

The curse of overparametrization in adversarial training: Precise analysis of robust generalization for random features regression
por: Hassani, Hamed, et al.
Publicado: (2022)

Differentially Private Model-X Knockoffs via Johnson-Lindenstrauss Transform
por: Tao, Yuxuan, et al.
Publicado: (2025)

Adaptive Sample Aggregation In Transfer Learning
por: Hanneke, Steve, et al.
Publicado: (2024)

Sampling and Loss Weights in Multi-Domain Training
por: Salmani, Mahdi, et al.
Publicado: (2025)

Lattice: Learning to Efficiently Compress the Memory
por: Karami, Mahdi, et al.
Publicado: (2025)

Learning Rate Schedules in the Presence of Distribution Shift
por: Fahrbach, Matthew, et al.
Publicado: (2023)

Improving the Variance of Differentially Private Randomized Experiments through Clustering
por: Javanmard, Adel, et al.
Publicado: (2023)

Training Implicit Generative Models via an Invariant Statistical Loss
por: de Frutos, José Manuel, et al.
Publicado: (2024)

Differentially Private Synthetic Data Release for Topics API Outputs
por: Dick, Travis, et al.
Publicado: (2025)

Learning Hierarchical Polynomials of Multiple Nonlinear Features with Three-Layer Networks
por: Fu, Hengyu, et al.
Publicado: (2024)

Titans: Learning to Memorize at Test Time
por: Behrouz, Ali, et al.
Publicado: (2024)

Nested Learning: The Illusion of Deep Learning Architectures
por: Behrouz, Ali, et al.
Publicado: (2025)

Neural Networks Learn Generic Multi-Index Models Near Information-Theoretic Limit
por: Zhang, Bohan, et al.
Publicado: (2025)

Outcome-Based Online Reinforcement Learning: Algorithms and Fundamental Limits
por: Chen, Fan, et al.
Publicado: (2025)

Unified Algorithms for RL with Decision-Estimation Coefficients: PAC, Reward-Free, Preference-Based Learning, and Beyond
por: Chen, Fan, et al.
Publicado: (2022)

DeepCrossAttention: Supercharging Transformer Residual Connections
por: Heddes, Mike, et al.
Publicado: (2025)

Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models
por: Rajendran, Goutham, et al.
Publicado: (2024)

Compression, Generalization and Learning
por: Campi, Marco C., et al.
Publicado: (2023)

Decision Making in Changing Environments: Robustness, Query-Based Learning, and Differential Privacy
por: Chen, Fan, et al.
Publicado: (2025)

Online Learning with Unknown Constraints
por: Sridharan, Karthik, et al.
Publicado: (2024)

Selling Joint Ads: A Regret Minimization Perspective
por: Aggarwal, Gagan, et al.
Publicado: (2024)

Inverse Mixed-Integer Programming: Learning Constraints then Objective Functions
por: Kitaoka, Akira
Publicado: (2025)

Conformal Prediction for Privacy-Preserving Machine Learning
por: Balinsky, Alexander David, et al.
Publicado: (2025)

Learning with Differentially Private (Sliced) Wasserstein Gradients
por: Rodríguez-Vítores, David, et al.
Publicado: (2025)

Provable Reward-Agnostic Preference-Based Reinforcement Learning
por: Zhan, Wenhao, et al.
Publicado: (2023)

Understanding In-Context Learning on Structured Manifolds: Bridging Attention to Kernel Methods
por: Shen, Zhaiming, et al.
Publicado: (2025)

Chemical Reaction Networks Learn Better than Spiking Neural Networks
por: Jaffard, Sophie, et al.
Publicado: (2026)

Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning
por: Foster, Dylan J., et al.
Publicado: (2024)

Beyond identifiability: Learning causal representations with few environments and finite samples
por: Lee, Inbeom, et al.
Publicado: (2026)

A Theory of the Mechanics of Information: Generalization Through Measurement of Uncertainty (Learning is Measuring)
por: Hazard, Christopher J., et al.
Publicado: (2025)

A Statistical Analysis of Deep Federated Learning for Intrinsically Low-dimensional Data
por: Chakraborty, Saptarshi, et al.
Publicado: (2024)

Labels or Preferences? Budget-Constrained Learning with Human Judgments over AI-Generated Outputs
por: Dong, Zihan, et al.
Publicado: (2026)

It's All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization
por: Behrouz, Ali, et al.
Publicado: (2025)

Towards a Sharp Analysis of Offline Policy Learning for $f$-Divergence-Regularized Contextual Bandits
por: Zhao, Qingyue, et al.
Publicado: (2025)