Publications associées à Estimation of value of travel-time savings using Mixed Logit models

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Serge Vaudenay

The maximal achievable advantage of a (computationally unbounded) distinguisher to determine whether a source

Z

is distributed according to distribution

P_0

P_1

, when given access to one sample of

Z

, is characterized by the statistical distance ...

Springer, Cham2020

Estimation of value of travel-time savings using Mixed Logit models

Graph Chatbot

Chattez avec Graph Search

Beyond fine-tuning: LoRA modules boost near-OOD detection and LLM security

Beyond fine-tuning: LoRA modules boost near-OOD detection and LLM security

Regressive Tomography and Direct Regressive Reconstruction: A Research Note

Robust discrete choice models with t-distributed kernel errors

Comparing Annual Glare Simulation Methods

Learning from demonstration using products of experts: Applications to manipulation and task prioritization

Unbiased Monte Carlo cluster updates with autoregressive neural networks

The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Scalable Stochastic Optimization: Scenario Reduction with Guarantees

Regressive Tomography and Direct Regressive Reconstruction: A Research Note

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Robust discrete choice models with t-distributed kernel errors

Unbiased Monte Carlo cluster updates with autoregressive neural networks

Scalable Stochastic Optimization: Scenario Reduction with Guarantees

Beyond fine-tuning: LoRA modules boost near-OOD detection and LLM security

Beyond fine-tuning: LoRA modules boost near-OOD detection and LLM security

Comparing Annual Glare Simulation Methods

Learning from demonstration using products of experts: Applications to manipulation and task prioritization

The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective