
Regularised LeastSquares Regression with InfiniteDimensional Output Space
We present some learning theory results on reproducing kernel Hilbert sp...
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Grasping Field: Learning Implicit Representations for Human Grasps
In recent years, substantial progress has been made on robotic grasping ...
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Learning Kernel Tests Without Data Splitting
Modern largescale kernelbased tests such as maximum mean discrepancy (...
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Kernel Conditional Moment Test via Maximum Moment Restriction
We propose a new family of specification tests called kernel conditional...
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A MeasureTheoretic Approach to Kernel Conditional Mean Embeddings
We present a new operatorfree, measuretheoretic definition of the cond...
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A New DistributionFree 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...
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KernelGuided Training of Implicit Generative Models with Stability Guarantees
Modern implicit generative models such as generative adversarial network...
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Dual IV: A Single Stage Instrumental Variable Regression
We present a novel singlestage procedure for instrumental variable (IV)...
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Lowrank Random Tensor for Bilinear Pooling
Bilinear pooling is capable of extracting highorder information from da...
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Quantum Mean Embedding of Probability Distributions
The kernel mean embedding of probability distributions is commonly used ...
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Private Causal Inference using Propensity Scores
The use of propensity score methods to reduce selection bias when determ...
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Kernel Conditional Density Operators
We introduce a conditional density estimation model termed the condition...
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Improving Consequential Decision Making under Imperfect Predictions
Consequential decisions are increasingly informed by sophisticated data...
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Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces
Modern implicit generative models such as generative adversarial network...
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Local Temporal Bilinear Pooling for Finegrained Action Parsing
Finegrained temporal action parsing is important in many applications, ...
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Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference
This paper introduces a novel Hilbert space representation of a counterf...
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Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces
Transfer operators such as the PerronFrobenius or Koopman operator play...
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Design and Analysis of the NIPS 2016 Review Process
Neural Information Processing Systems (NIPS) is a toptier annual confer...
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Kernel Mean Embedding of Distributions: A Review and Beyond
A Hilbert space embedding of a distributionin short, a kernel mean em...
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Towards a Learning Theory of CauseEffect Inference
We pose causal inference as the problem of learning to classify probabil...
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Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations
We describe a method to perform functional operations on probability dis...
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Kernel Mean Estimation via Spectral Filtering
The problem of estimating the kernel mean in a reproducing kernel Hilber...
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OneClass Support Measure Machines for Group Anomaly Detection
We propose oneclass support measure machines (OCSMMs) for group anomaly...
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Kernel Mean Shrinkage Estimators
A mean function in a reproducing kernel Hilbert space (RKHS), or a kerne...
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Kernel Mean Estimation and Stein's Effect
A mean function in reproducing kernel Hilbert space, or a kernel mean, i...
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Domain Generalization via Invariant Feature Representation
This paper investigates domain generalization: How to take knowledge acq...
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Hilbert Space Embedding for Dirichlet Process Mixtures
This paper proposes a Hilbert space embedding for Dirichlet Process mixt...
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Learning from Distributions via Support Measure Machines
This paper presents a kernelbased discriminative learning framework on ...
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Krikamol Muandet
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Lecturer at Department of Mathematics at Mahidol University