Search Results for author:
Found 45 papers, 24 papers with code
Grasping Field: Learning Implicit Representations for Human Grasps
Specifically, our generative model is able to synthesize high-quality human grasps, given only on a 3D object point cloud.
Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces
Transfer operators such as the Perron--Frobenius or Koopman operator play an important role in the global analysis of complex dynamical systems.
Towards a Learning Theory of Cause-Effect Inference
We pose causal inference as the problem of learning to classify probability distributions.
Learning Counterfactually Invariant Predictors
Notions of counterfactual invariance (CI) have proven essential for predictors that are fair, robust, and generalizable in the real world.
AutoML Two-Sample Test
Two-sample tests are important in statistics and machine learning, both as tools for scientific discovery as well as to detect distribution shifts.
Local Temporal Bilinear Pooling for Fine-grained Action Parsing
Fine-grained temporal action parsing is important in many applications, such as daily activity understanding, human motion analysis, surgical robotics and others requiring subtle and precise operations in a long-term period.
MATE: Plugging in Model Awareness to Task Embedding for Meta Learning
Meta-learning improves generalization of machine learning models when faced with previously unseen tasks by leveraging experiences from different, yet related prior tasks.
Fair Decisions Despite Imperfect Predictions
In this paper, we show that in this selective labels setting, learning a predictor directly only from available labeled data is suboptimal in terms of both fairness and utility.
Instrumental Variable Regression via Kernel Maximum Moment Loss
We investigate a simple objective for nonlinear instrumental variable (IV) regression based on a kernelized conditional moment restriction (CMR) known as a maximum moment restriction (MMR).
Frontal Low-rank Random Tensors for Fine-grained Action Segmentation
In this paper, we propose an approach to representing high-order information for temporal action segmentation via a simple yet effective bilinear form.
Kernel Conditional Moment Test via Maximum Moment Restriction
We propose a new family of specification tests called kernel conditional moment (KCM) tests.
Learning Kernel Tests Without Data Splitting
Modern large-scale kernel-based tests such as maximum mean discrepancy (MMD) and kernelized Stein discrepancy (KSD) optimize kernel hyperparameters on a held-out sample via data splitting to obtain the most powerful test statistics.
Gated Domain Units for Multi-source Domain Generalization
To address this problem, we postulate that real-world distributions are composed of latent Invariant Elementary Distributions (I. E. D) across different domains.
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction
In particular, we provide a unifying view of two-stage and moment restriction approaches for solving this problem in a nonlinear setting.
Domain generalization via invariant feature representation
This paper investigates domain generalization: How to take knowledge acquired from an arbitrary number of related domains and apply it to previously unseen domains?
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 probabilistic programming in the context of stochastic systems.
Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions
Important problems in causal inference, economics, and, more generally, robust machine learning can be expressed as conditional moment restrictions, but estimation becomes challenging as it requires solving a continuum of unconditional moment restrictions.
Design and Analysis of the NIPS 2016 Review Process
Neural Information Processing Systems (NIPS) is a top-tier annual conference in machine learning.
Dual Instrumental Variable Regression
We present a novel algorithm for non-linear instrumental variable (IV) regression, DualIV, which simplifies traditional two-stage methods via a dual formulation.
Counterfactual Mean Embeddings
In this work, we propose to model counterfactual distributions using a novel Hilbert space representation called counterfactual mean embedding (CME).
Kernel Mean Embedding of Distributions: A Review and Beyond
Next, we discuss the Hilbert space embedding for conditional distributions, give theoretical insights, and review some applications.
Kernel Mean Shrinkage Estimators
A mean function in a reproducing kernel Hilbert space (RKHS), or a kernel mean, is central to kernel methods in that it is used by many classical algorithms such as kernel principal component analysis, and it also forms the core inference step of modern kernel methods that rely on embedding probability distributions in RKHSs.
Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations
We describe a method to perform functional operations on probability distributions of random variables.
Kernel Mean Estimation via Spectral Filtering
The problem of estimating the kernel mean in a reproducing kernel Hilbert space (RKHS) is central to kernel methods in that it is used by classical approaches (e. g., when centering a kernel PCA matrix), and it also forms the core inference step of modern kernel methods (e. g., kernel-based non-parametric tests) that rely on embedding probability distributions in RKHSs.
The Randomized Causation Coefficient
We are interested in learning causal relationships between pairs of random variables, purely from observational data.
One-Class Support Measure Machines for Group Anomaly Detection
We propose one-class support measure machines (OCSMMs) for group anomaly detection which aims at recognizing anomalous aggregate behaviors of data points.
Kernel Mean Estimation and Stein's Effect
A mean function in reproducing kernel Hilbert space, or a kernel mean, is an important part of many applications ranging from kernel principal component analysis to Hilbert-space embedding of distributions.
One-Class Support Measure Machines for Group Anomaly Detection
We propose one-class support measure machines (OCSMMs) for group anomaly detection which aims at recognizing anomalous aggregate behaviors of data points.
Learning from Distributions via Support Measure Machines
This paper presents a kernel-based discriminative learning framework on probability measures.
Kernel-Guided Training of Implicit Generative Models with Stability Guarantees
Modern implicit generative models such as generative adversarial networks (GANs) are generally known to suffer from issues such as instability, uninterpretability, and difficulty in assessing their performance.
Kernel Conditional Density Operators
The proposed model is based on a novel approach to the reconstruction of probability densities from their kernel mean embeddings by drawing connections to estimation of Radon-Nikodym derivatives in the reproducing kernel Hilbert space (RKHS).
Privacy-Preserving Causal Inference via Inverse Probability Weighting
The use of inverse probability weighting (IPW) methods to estimate the causal effect of treatments from observational studies is widespread in econometrics, medicine and social sciences.
Quantum Mean Embedding of Probability Distributions
The kernel mean embedding of probability distributions is commonly used in machine learning as an injective mapping from distributions to functions in an infinite dimensional Hilbert space.
Kernel-Guided Training of Implicit Generative Models with Stability Guarantees
Modern implicit generative models such as generative adversarial networks (GANs) are generally known to suffer from issues such as instability, uninterpretability, and difficulty in assessing their performance.
A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings
We present an operator-free, measure-theoretic approach to the conditional mean embedding (CME) as a random variable taking values in a reproducing kernel Hilbert space.
Regularised Least-Squares Regression with Infinite-Dimensional Output Space
This short technical report presents some learning theory results on vector-valued reproducing kernel Hilbert space (RKHS) regression, where the input space is allowed to be non-compact and the output space is a (possibly infinite-dimensional) Hilbert space.
A Witness Two-Sample Test
That is, the test set is used to simultaneously estimate the expectations and define the basis points, while the training set only serves to select the kernel and is discarded.
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression
We propose to analyse the conditional distributional treatment effect (CoDiTE), which, in contrast to the more common conditional average treatment effect (CATE), is designed to encode a treatment's distributional aspects beyond the mean.
Instrument Space Selection for Kernel Maximum Moment Restriction
Kernel maximum moment restriction (KMMR) recently emerges as a popular framework for instrumental variable (IV) based conditional moment restriction (CMR) models with important applications in conditional moment (CM) testing and parameter estimation for IV regression and proximal causal learning.
Impossibility of Collective Intelligence
Democratization of AI involves training and deploying machine learning models across heterogeneous and potentially massive environments.
On the Relationship Between Explanation and Prediction: A Causal View
Our work borrows tools from causal inference to systematically assay this relationship.
Fast Adaptive Test-Time Defense with Robust Features
Adaptive test-time defenses are used to improve the robustness of deep neural networks to adversarial examples.
Causal Strategic Learning with Competitive Selection
We study the problem of agent selection in causal strategic learning under multiple decision makers and address two key challenges that come with it.
Looping in the Human Collaborative and Explainable Bayesian Optimization
Like many optimizers, Bayesian optimization often falls short of gaining user trust due to opacity.
Domain Generalisation via Imprecise Learning
Out-of-distribution (OOD) generalisation is challenging because it involves not only learning from empirical data, but also deciding among various notions of generalisation, e. g., optimising the average-case risk, worst-case risk, or interpolations thereof.
