Cover Image

Consistency Conditions for Differentiable Surrogate Losses

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Khurana, Drona, Thilagar, Anish, Kimpara, Dhamma, Frongillo, Rafael
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Trading off Consistency and Dimensionality of Convex Surrogates for the Mode
by: Nueve, Enrique, et al.
Published: (2024)

Hedging and Approximate Truthfulness in Traditional Forecasting Competitions
by: Monroe, Mary, et al.
Published: (2024)

Smoothed Elicitation Complexity for Approximate $Γ$-calibration of Discrete Classification Tasks
by: Finocchiaro, Jessica, et al.
Published: (2026)

Equilibria in Large Position-Optimization Games
by: Frongillo, Rafael, et al.
Published: (2026)

Learning Differentiable Surrogate Losses for Structured Prediction
by: Yang, Junjie, et al.
Published: (2024)

Structured Prediction with Abstention via the Lovász Hinge
by: Finocchiaro, Jessie, et al.
Published: (2025)

WOMAC: A Mechanism For Prediction Competitions
by: Srinivasan, Siddarth, et al.
Published: (2025)

Minimizing Surrogate Losses for Decision-Focused Learning using Differentiable Optimization
by: Mandi, Jayanta, et al.
Published: (2025)

Controllable Probabilistic Forecasting with Stochastic Decomposition Layers
by: Schreck, John S., et al.
Published: (2025)

Zero Grads: Learning Local Surrogate Losses for Non-Differentiable Graphics
by: Fischer, Michael, et al.
Published: (2023)

Robust forecast aggregation via additional queries
by: Frongillo, Rafael, et al.
Published: (2025)

A Foundation Model for Instruction-Conditioned In-Context Time Series Tasks
by: Saha, Anish, et al.
Published: (2026)

Non-Stationary Online Structured Prediction with Surrogate Losses
by: Sakaue, Shinsaku, et al.
Published: (2025)

The Adversarial Consistency of Surrogate Risks for Binary Classification
by: Frank, Natalie, et al.
Published: (2023)

A Universal Growth Rate for Learning with Smooth Surrogate Losses
by: Mao, Anqi, et al.
Published: (2024)

Normalized Conditional Mutual Information Surrogate Loss for Deep Neural Classifiers
by: Ye, Linfeng, et al.
Published: (2026)

Establishing Linear Surrogate Regret Bounds for Convex Smooth Losses via Convolutional Fenchel-Young Losses
by: Cao, Yuzhou, et al.
Published: (2025)

Analyzing Cost-Sensitive Surrogate Losses via $\mathcal{H}$-calibration
by: Shah, Sanket, et al.
Published: (2025)

LIMEtree: Consistent and Faithful Surrogate Explanations of Multiple Classes
by: Sokol, Kacper, et al.
Published: (2020)

Control Consistency Losses for Diffusion Bridges
by: Howard, Samuel, et al.
Published: (2025)

Solving Hidden Monotone Variational Inequalities with Surrogate Losses
by: D'Orazio, Ryan, et al.
Published: (2024)

Realizable $H$-Consistent and Bayes-Consistent Loss Functions for Learning to Defer
by: Mao, Anqi, et al.
Published: (2024)

Online Structured Prediction with Fenchel--Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss
by: Sakaue, Shinsaku, et al.
Published: (2024)

On the Conditioning Consistency Gap in Conditional Neural Processes
by: Young, Robin
Published: (2026)

Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications
by: Schreck, John S., et al.
Published: (2023)

A Flexible Fairness Framework with Surrogate Loss Reweighting for Addressing Sociodemographic Disparities
by: Xu, Wen, et al.
Published: (2025)

Beyond Loss Guidance: Using PDE Residuals as Spectral Attention in Diffusion Neural Operators
by: Sawhney, Medha, et al.
Published: (2025)

A Cycle-Consistent Graph Surrogate for Full-Cycle Left Ventricular Myocardial Biomechanics
by: Mu, Siyu, et al.
Published: (2026)

Linear-Core Surrogates: Smooth Loss Functions with Linear Rates for Classification and Structured Prediction
by: Mohri, Mehryar, et al.
Published: (2026)

Multifidelity Surrogate Modeling of Depressurized Loss of Forced Cooling in High-temperature Gas Reactors
by: Eaheart, Meredith, et al.
Published: (2026)

Smart Surrogate Losses for Contextual Stochastic Linear Optimization with Robust Constraints
by: Im, Hyungki, et al.
Published: (2025)

Using physics-inspired Singular Learning Theory to understand grokking & other phase transitions in modern neural networks
by: Lakkapragada, Anish
Published: (2025)

A Novel Explanation Against Linear Neural Networks
by: Lakkapragada, Anish
Published: (2023)

Exponential Family Discriminant Analysis: Generalizing LDA-Style Generative Classification to Non-Gaussian Models
by: Lakkapragada, Anish
Published: (2026)

Keeping Score: Efficiency Improvements in Neural Likelihood Surrogate Training via Score-Augmented Loss Functions
by: Shen, Alexander, et al.
Published: (2026)

Consistent Optimal Transport with Empirical Conditional Measures
by: Manupriya, Piyushi, et al.
Published: (2023)

Deep Operator Networks for Surrogate Modeling of Cyclic Adsorption Processes with Varying Initial Conditions
by: Ceccanti, Beatrice, et al.
Published: (2026)

Robust Amortized Bayesian Inference with Self-Consistency Losses on Unlabeled Data
by: Mishra, Aayush, et al.
Published: (2025)

Expectation Consistency Loss: Rethink Confidence Calibration under Covariate Shift
by: Dong, Jinzong, et al.
Published: (2026)

Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy
by: Agarwal, Anish, et al.
Published: (2021)