Publications related to Satisficing measure approach for vehicle routing problem with time windows under uncertainty

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

Pierre Vandergheynst, Milos Vasic, Francesco Craighero, Renata Khasanova

Under resource constraints, LLMs are usually fine- tuned with additional knowledge using Parameter Efficient Fine-Tuning (PEFT), using Low-Rank Adaptation (LoRA) modules. In fact, LoRA injects a new set of small trainable matrices to adapt an LLM to a new ...

2024

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

Pierre Vandergheynst, Milos Vasic, Francesco Craighero, Renata Khasanova

Under resource constraints, LLMs are usually fine-tuned with additional knowledge using Parameter Efficient Fine-Tuning (PEFT), using Low-Rank Adaptation (LoRA) modules. In fact, LoRA injects a new set of small trainable matrices to adapt an LLM to a new t ...

2024

On the Use of Benford’s Law to Assess the Quality of the Data Provided by Lightning Locating Systems

Marcos Rubinstein, Amirhossein Mostajabi, Wolfgang Schulz

Lightning causes significant damage and casualties globally by directly striking humans and livestock, by igniting forest fires, and by inducing electrical surges in electronic infrastructure, airplanes, rockets, etc. Monitoring the evolution of thundersto ...

2022

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

Naman Goel, Amit Sharma

Training datasets for machine learning often have some form of missingness. For example, to learn a model for deciding whom to give a loan, the available training data includes individuals who were given a loan in the past, but not those who were not. This ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2021

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

or

P_1

, when given access to one sample of

Z

, is characterized by the statistical distance ...

Springer, Cham2020

Scalable Stochastic Optimization: Scenario Reduction with Guarantees

Kilian Schindler

Stochastic optimization is a popular modeling paradigm for decision-making under uncertainty and has a wide spectrum of applications in management science, economics and engineering. However, the stochastic optimization models one faces in practice are int ...

EPFL2020

Statistical Aspects of Wasserstein Distances

Victor Panaretos, Yoav Zemel

Wasserstein distances are metrics on probability distributions inspired by the problem of optimal mass transportation. Roughly speaking, they measure the minimal effort required to reconfigure the probability mass of one distribution in order to recover th ...

ANNUAL REVIEWS2019

The electric autonomous dial-a-ride problem

Nikolaos Geroliminis, Claudia Bongiovanni, Mor Kaspi

In the Dial-a-Ride-Problem (DARP) a fleet of vehicles provides shared-ride services to users specifying their origin, destination, and preferred arrival time. Typically, the problem consists of finding minimum cost routes, satisfying operational constraint ...

Elsevier2019

Scenario Reduction Revisited: Fundamental Limits and Guarantees

Daniel Kuhn, Napat Rujeerapaiboon, Kilian Schindler, Wolfram Wiesemann

The goal of scenario reduction is to approximate a given discrete distribution with another discrete distribution that has fewer atoms. We distinguish continuous scenario reduction, where the new atoms may be chosen freely, and discrete scenario reduction, ...

2018

The online electric autonomous dial-a-ride problem

Anouk Larisse Allenspach

The DARP (Dial-a-Ride Problem) is concerned with providing service to a number of requests, specifying their location of origin and destination, as well as time windows indicating their desired service time for at least one of the locations. In the dynamic ...

2018

Satisficing measure approach for vehicle routing problem with time windows under uncertainty

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

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

On the Use of Benford’s Law to Assess the Quality of the Data Provided by Lightning Locating Systems

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

Statistical Aspects of Wasserstein Distances

The electric autonomous dial-a-ride problem

Scenario Reduction Revisited: Fundamental Limits and Guarantees

The online electric autonomous dial-a-ride problem

Scalable Stochastic Optimization: Scenario Reduction with Guarantees

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

Scenario Reduction Revisited: Fundamental Limits and Guarantees

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

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

The electric autonomous dial-a-ride problem

Statistical Aspects of Wasserstein Distances

On the Use of Benford’s Law to Assess the Quality of the Data Provided by Lightning Locating Systems

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

The online electric autonomous dial-a-ride problem