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07/24/2023
Predicting Ordinary Differential Equations with Transformers
We develop a transformer-based sequence-to-sequence model that recovers ...
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06/16/2023
Stabilized Neural Differential Equations for Learning Constrained Dynamics
Many successful methods to learn dynamical systems from data have recent...
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02/11/2023
Sequential Underspecified Instrument Selection for Cause-Effect Estimation
Instrumental variable (IV) methods are used to estimate causal effects i...
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11/05/2022
Discovering ordinary differential equations that govern time-series
Natural laws are often described through differential equations yet find...
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10/26/2022
Sparsity in Continuous-Depth Neural Networks
Neural Ordinary Differential Equations (NODEs) have proven successful in...
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07/20/2022
Learning Counterfactually Invariant Predictors
We propose a method to learn predictors that are invariant under counter...
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06/27/2022
Modeling Content Creator Incentives on Algorithm-Curated Platforms
Content creators compete for user attention. Their reach crucially depen...
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05/18/2022
Multi-disciplinary fairness considerations in machine learning for clinical trials
While interest in the application of machine learning to improve healthc...
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05/15/2022
Supervised Learning and Model Analysis with Compositional Data
The compositionality and sparsity of high-throughput sequencing data pos...
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04/28/2022
Predicting single-cell perturbation responses for unseen drugs
Single-cell transcriptomics enabled the study of cellular heterogeneity ...
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01/17/2022
Numerical Analysis of the Causal Action Principle in Low Dimensions
The numerical analysis of causal fermion systems is advanced by employin...
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06/28/2021
On component interactions in two-stage recommender systems
Thanks to their scalability, two-stage recommenders are used by many of ...
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06/23/2021
Beyond Predictions in Neural ODEs: Identification and Interventions
Spurred by tremendous success in pattern matching and prediction tasks, ...
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06/21/2021
A causal view on compositional data
Many scientific datasets are compositional in nature. Important examples...
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01/29/2021
Beyond traditional assumptions in fair machine learning
This thesis scrutinizes common assumptions underlying traditional machin...
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09/01/2020
Exploration in two-stage recommender systems
Two-stage recommender systems are widely adopted in industry due to thei...
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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...
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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...
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07/01/2019
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding
Causal approaches to fairness have seen substantial recent interest, bot...
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04/18/2019
Convolutional neural networks: a magic bullet for gravitational-wave detection?
In the last few years, machine learning techniques, in particular convol...
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02/08/2019
Improving Consequential Decision Making under Imperfect Predictions
Consequential decisions are increasingly informed by sophisticated data-...
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12/03/2018
Generalization in anti-causal learning
The ability to learn and act in novel situations is still a prerogative ...
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06/08/2018
Blind Justice: Fairness with Encrypted Sensitive Attributes
Recent work has explored how to train machine learning models which do n...
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12/04/2017
Learning Independent Causal Mechanisms
Independent causal mechanisms are a central concept in the study of caus...
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06/08/2017