Krikamol Muandet

research

∙ 08/30/2023

Causal Strategic Learning with Competitive Selection

We study the problem of agent selection in causal strategic learning und...

0 Kiet Q. H. Vo, et al. ∙

research

∙ 07/21/2023

Fast Adaptive Test-Time Defense with Robust Features

Adaptive test-time defenses are used to improve the robustness of deep n...

0 Anurag Singh, et al. ∙

research

∙ 05/24/2023

Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models

We present a novel approach for explaining Gaussian processes (GPs) that...

0 Siu Lun Chau, et al. ∙

research

∙ 05/19/2023

A Measure-Theoretic Axiomatisation of Causality

Causality is a central concept in a wide range of research areas, yet th...

0 Junhyung Park, et al. ∙

research

∙ 12/13/2022

On the Relationship Between Explanation and Prediction: A Causal View

Explainability has become a central requirement for the development, dep...

0 Amir-Hossein Karimi, et al. ∙

research

∙ 07/20/2022

Learning Counterfactually Invariant Predictors

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

0 Francesco Quinzan, et al. ∙

research

∙ 07/11/2022

Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions

Important problems in causal inference, economics, and, more generally, ...

1 Heiner Kremer, et al. ∙

research

∙ 06/24/2022

Gated Domain Units for Multi-source Domain Generalization

Distribution shift (DS) is a common problem that deteriorates the perfor...

0 Simon Föll, et al. ∙

research

∙ 06/17/2022

AutoML Two-Sample Test

Two-sample tests are important in statistics and machine learning, both ...

10 Jonas M. Kübler, et al. ∙

research

∙ 06/05/2022

Impossibility of Collective Intelligence

Democratization of AI involves training and deploying machine learning m...

0 Krikamol Muandet, et al. ∙

research

∙ 02/09/2022

Towards Empirical Process Theory for Vector-Valued Functions: Metric Entropy of Smooth Function Classes

This paper provides some first steps in developing empirical process the...

0 Junhyung Park, et al. ∙

research

∙ 02/16/2021

Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression

We propose to analyse the conditional distributional treatment effect (C...

8 Junhyung Park, et al. ∙

research

∙ 02/10/2021

An Optimal Witness Function for Two-Sample Testing

We propose data-dependent test statistics based on a one-dimensional wit...

4 Jonas M. Kübler, et al. ∙

research

∙ 10/21/2020

Regularised Least-Squares Regression with Infinite-Dimensional Output Space

We present some learning theory results on reproducing kernel Hilbert sp...

0 Junhyunng Park, et al. ∙

research

∙ 08/10/2020

Grasping Field: Learning Implicit Representations for Human Grasps

In recent years, substantial progress has been made on robotic grasping ...

10 Korrawe Karunratanakul, et al. ∙

research

∙ 06/03/2020

Learning Kernel Tests Without Data Splitting

Modern large-scale kernel-based tests such as maximum mean discrepancy (...

4 Jonas M. Kübler, et al. ∙

research

∙ 02/21/2020

Kernel Conditional Moment Test via Maximum Moment Restriction

We propose a new family of specification tests called kernel conditional...

0 Krikamol Muandet, et al. ∙

research

∙ 02/10/2020

A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings

We present a new operator-free, measure-theoretic definition of the cond...

0 Junhyung Park, et al. ∙

research

∙ 11/25/2019

A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic Programming

This work presents the concept of kernel mean embedding and kernel proba...

17 Jia-Jie Zhu, et al. ∙

research

∙ 10/29/2019

Kernel-Guided Training of Implicit Generative Models with Stability Guarantees

Modern implicit generative models such as generative adversarial network...

16 Arash Mehrjou, et al. ∙

research

∙ 10/27/2019

Dual IV: A Single Stage Instrumental Variable Regression

We present a novel single-stage procedure for instrumental variable (IV)...

0 Krikamol Muandet, et al. ∙

research

∙ 06/03/2019

Low-rank Random Tensor for Bilinear Pooling

Bilinear pooling is capable of extracting high-order information from da...

0 Yan Zhang, et al. ∙

research

∙ 05/31/2019

Quantum Mean Embedding of Probability Distributions

The kernel mean embedding of probability distributions is commonly used ...

0 Jonas M. Kübler, et al. ∙

research

∙ 05/29/2019

Private Causal Inference using Propensity Scores

The use of propensity score methods to reduce selection bias when determ...

0 Si Kai Lee, et al. ∙

research

∙ 05/27/2019

Kernel Conditional Density Operators

We introduce a conditional density estimation model termed the condition...

0 Ingmar Schuster, et al. ∙

research

∙ 02/08/2019

Improving Consequential Decision Making under Imperfect Predictions

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

6 Niki Kilbertus, et al. ∙

research

∙ 01/26/2019

Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces

Modern implicit generative models such as generative adversarial network...

20 Arash Mehrjou, et al. ∙

research

∙ 12/05/2018

Local Temporal Bilinear Pooling for Fine-grained Action Parsing

Fine-grained temporal action parsing is important in many applications, ...

0 Yan Zhang, et al. ∙

research

∙ 05/22/2018

Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference

This paper introduces a novel Hilbert space representation of a counterf...

0 Krikamol Muandet, et al. ∙

research

∙ 12/05/2017

Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces

Transfer operators such as the Perron-Frobenius or Koopman operator play...

0 Stefan Klus, et al. ∙

research

∙ 08/31/2017

Design and Analysis of the NIPS 2016 Review Process

Neural Information Processing Systems (NIPS) is a top-tier annual confer...

0 Nihar B. Shah, et al. ∙

research

∙ 05/31/2016

Kernel Mean Embedding of Distributions: A Review and Beyond

A Hilbert space embedding of a distribution---in short, a kernel mean em...

0 Krikamol Muandet, et al. ∙

research

∙ 02/09/2015

Towards a Learning Theory of Cause-Effect Inference

We pose causal inference as the problem of learning to classify probabil...

0 David Lopez-Paz, et al. ∙

research

∙ 01/27/2015

Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations

We describe a method to perform functional operations on probability dis...

0 Bernhard Schölkopf, et al. ∙

research

∙ 11/04/2014

Kernel Mean Estimation via Spectral Filtering

The problem of estimating the kernel mean in a reproducing kernel Hilber...

0 Krikamol Muandet, et al. ∙

research

∙ 08/09/2014

One-Class Support Measure Machines for Group Anomaly Detection

We propose one-class support measure machines (OCSMMs) for group anomaly...

0 Krikamol Muandet, et al. ∙

research

∙ 05/21/2014

Kernel Mean Shrinkage Estimators

A mean function in a reproducing kernel Hilbert space (RKHS), or a kerne...

0 Krikamol Muandet, et al. ∙

research

∙ 06/04/2013

Kernel Mean Estimation and Stein's Effect

A mean function in reproducing kernel Hilbert space, or a kernel mean, i...

0 Krikamol Muandet, et al. ∙

research

∙ 01/10/2013

Domain Generalization via Invariant Feature Representation

This paper investigates domain generalization: How to take knowledge acq...

0 Krikamol Muandet, et al. ∙

research

∙ 10/16/2012

Hilbert Space Embedding for Dirichlet Process Mixtures

This paper proposes a Hilbert space embedding for Dirichlet Process mixt...

0 Krikamol Muandet, et al. ∙

research

∙ 02/29/2012

Learning from Distributions via Support Measure Machines

This paper presents a kernel-based discriminative learning framework on ...

0 Krikamol Muandet, et al. ∙

Krikamol Muandet

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