Niki Kilbertus | DeepAI

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∙ 07/24/2023

Predicting Ordinary Differential Equations with Transformers

We develop a transformer-based sequence-to-sequence model that recovers ...

0 Sören Becker, et al. ∙

∙ 06/16/2023

Stabilized Neural Differential Equations for Learning Constrained Dynamics

Many successful methods to learn dynamical systems from data have recent...

0 Alistair White, et al. ∙

∙ 02/11/2023

Sequential Underspecified Instrument Selection for Cause-Effect Estimation

Instrumental variable (IV) methods are used to estimate causal effects i...

0 Elisabeth Ailer, et al. ∙

∙ 11/05/2022

Discovering ordinary differential equations that govern time-series

Natural laws are often described through differential equations yet find...

0 Sören Becker, et al. ∙

∙ 10/26/2022

Sparsity in Continuous-Depth Neural Networks

Neural Ordinary Differential Equations (NODEs) have proven successful in...

0 Hananeh Aliee, et al. ∙

∙ 07/20/2022

Learning Counterfactually Invariant Predictors

We propose a method to learn predictors that are invariant under counter...

0 Francesco Quinzan, et al. ∙

∙ 06/27/2022

Modeling Content Creator Incentives on Algorithm-Curated Platforms

Content creators compete for user attention. Their reach crucially depen...

6 Jiri Hron, et al. ∙

∙ 05/18/2022

Multi-disciplinary fairness considerations in machine learning for clinical trials

While interest in the application of machine learning to improve healthc...

4 Isabel Chien, et al. ∙

∙ 05/15/2022

Supervised Learning and Model Analysis with Compositional Data

The compositionality and sparsity of high-throughput sequencing data pos...

0 Shimeng Huang, et al. ∙

∙ 04/28/2022

Predicting single-cell perturbation responses for unseen drugs

Single-cell transcriptomics enabled the study of cellular heterogeneity ...

0 Leon Hetzel, et al. ∙

∙ 01/17/2022

Numerical Analysis of the Causal Action Principle in Low Dimensions

The numerical analysis of causal fermion systems is advanced by employin...

0 Felix Finster, et al. ∙

∙ 06/28/2021

On component interactions in two-stage recommender systems

Thanks to their scalability, two-stage recommenders are used by many of ...

5 Jiri Hron, et al. ∙

∙ 06/23/2021

Beyond Predictions in Neural ODEs: Identification and Interventions

Spurred by tremendous success in pattern matching and prediction tasks, ...

4 Hananeh Aliee, et al. ∙

∙ 06/21/2021

A causal view on compositional data

Many scientific datasets are compositional in nature. Important examples...

0 Elisabeth Ailer, et al. ∙

∙ 01/29/2021

Beyond traditional assumptions in fair machine learning

This thesis scrutinizes common assumptions underlying traditional machin...

0 Niki Kilbertus, et al. ∙

∙ 09/01/2020

Exploration in two-stage recommender systems

Two-stage recommender systems are widely adopted in industry due to thei...

0 Jiri Hron, et al. ∙

∙ 06/14/2020

Is Independence all you need? On the Generalization of Representations Learned from Correlated Data

Despite impressive progress in the last decade, it still remains an open...

14 Frederik Träuble, et al. ∙

∙ 06/11/2020

A Class of Algorithms for General Instrumental Variable Models

Causal treatment effect estimation is a key problem that arises in a var...

12 Niki Kilbertus, et al. ∙

∙ 07/01/2019

The Sensitivity of Counterfactual Fairness to Unmeasured Confounding

Causal approaches to fairness have seen substantial recent interest, bot...

5 Niki Kilbertus, et al. ∙

∙ 04/18/2019

Convolutional neural networks: a magic bullet for gravitational-wave detection?

In the last few years, machine learning techniques, in particular convol...

32 Timothy D. Gebhard, et al. ∙

∙ 02/08/2019

Improving Consequential Decision Making under Imperfect Predictions

Consequential decisions are increasingly informed by sophisticated data-...

6 Niki Kilbertus, et al. ∙

∙ 12/03/2018

Generalization in anti-causal learning

The ability to learn and act in novel situations is still a prerogative ...

0 Niki Kilbertus, et al. ∙

∙ 06/08/2018

Blind Justice: Fairness with Encrypted Sensitive Attributes

Recent work has explored how to train machine learning models which do n...

0 Niki Kilbertus, et al. ∙

∙ 12/04/2017

Learning Independent Causal Mechanisms

Independent causal mechanisms are a central concept in the study of caus...

0 Giambattista Parascandolo, et al. ∙

∙ 06/08/2017

Avoiding Discrimination through Causal Reasoning

Recent work on fairness in machine learning has focused on various stati...

0 Niki Kilbertus, et al. ∙

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