Publications related to A Bayesian MCMC Approach to Assess the Complete Distribution of Fitness Effects of New Mutations: Uncovering the Potential for Adaptive Walks in Challenging Environments

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

Price discrimination with robust beliefs

Thomas Alois Weber, Jun Han

This paper considers the problem of second-degree price discrimination when the type distribution is unknown or imperfectly specified by means of an ambiguity set. As robustness measure we use a performance index, equivalent to relative regret, which quant ...

2023

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

Wasserstein Distributionally Robust Learning

Soroosh Shafieezadeh Abadeh

Many decision problems in science, engineering, and economics are affected by uncertainty, which is typically modeled by a random variable governed by an unknown probability distribution. For many practical applications, the probability distribution is onl ...

EPFL2020

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

Directed evolution of the optoelectronic properties of synthetic nanomaterials

Ardemis Anoush Boghossian, Benjamin Paul Johanès Gabriel Lambert, Alice Judith Gillen, Shang-Jung Wu

Directed evolution is a powerful approach to tailor protein properties toward new or enhanced functions. Herein, we use directed evolution to engineer the optoelectronic properties of DNA-wrapped single-walled carbon nanotube sensors through DNA mutation. ...

2019

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 fitness landscape of the codon space across environments

Jeffrey David Jensen, Claudia Bank, Sebastian Matuszewski

Fitness landscapes map the relationship between genotypes and fitness. However, most fitness landscape studies ignore the genetic architecture imposed by the codon table and thereby neglect the potential role of synonymous mutations. To quantify the fitnes ...

NATURE PUBLISHING GROUP2018

A Bayesian MCMC Approach to Assess the Complete Distribution of Fitness Effects of New Mutations: Uncovering the Potential for Adaptive Walks in Challenging Environments

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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

Price discrimination with robust beliefs

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Wasserstein Distributionally Robust Learning

Scalable Stochastic Optimization: Scenario Reduction with Guarantees

Statistical Aspects of Wasserstein Distances

Directed evolution of the optoelectronic properties of synthetic nanomaterials

Scenario Reduction Revisited: Fundamental Limits and Guarantees

The fitness landscape of the codon space across environments

Price discrimination with robust beliefs

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

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

Directed evolution of the optoelectronic properties of synthetic nanomaterials

The fitness landscape of the codon space across environments

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

Wasserstein Distributionally Robust Learning

Statistical Aspects of Wasserstein Distances

Scalable Stochastic Optimization: Scenario Reduction with Guarantees

Scenario Reduction Revisited: Fundamental Limits and Guarantees