Search Results for author:
Found 17 papers, 5 papers with code
FiP: a Fixed-Point Approach for Causal Generative Modeling
Based on this, we design a two-stage causal generative model that first infers the causal order from observations in a zero-shot manner, thus by-passing the search, and then learns the generative fixed-point SCM on the ordered variables.
The Essential Role of Causality in Foundation World Models for Embodied AI
This paper focuses on the prospects of building foundation world models for the upcoming generation of embodied agents and presents a novel viewpoint on the significance of causality within these.
Robust Linear Regression: Phase-Transitions and Precise Tradeoffs for General Norms
In this paper, we investigate the impact of test-time adversarial attacks on linear regression models and determine the optimal level of robustness that any model can reach while maintaining a given level of standard predictive performance (accuracy).
Polynomial-Time Solvers for the Discrete $\infty$-Optimal Transport Problems
In this note, we propose polynomial-time algorithms solving the Monge and Kantorovich formulations of the $\infty$-optimal transport problem in the discrete and finite setting.
Robust Linear Regression: Gradient-descent, Early-stopping, and Beyond
However, we show that this strategy can be arbitrarily sub-optimal in the case of general Mahalanobis attacks.
Low-rank Optimal Transport: Approximation, Statistics and Debiasing
The matching principles behind optimal transport (OT) play an increasingly important role in machine learning, a trend which can be observed when OT is used to disambiguate datasets in applications (e. g. single-cell genomics) or used to improve more complex methods (e. g. balanced attention in transformers or self-supervised learning).
Triangular Flows for Generative Modeling: Statistical Consistency, Smoothness Classes, and Fast Rates
Triangular flows, also known as Kn\"{o}the-Rosenblatt measure couplings, comprise an important building block of normalizing flow models for generative modeling and density estimation, including popular autoregressive flow models such as real-valued non-volume preserving transformation models (Real NVP).
An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings
We propose a new conditional dependence measure and a statistical test for conditional independence.
Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs
The ability to align points across two related yet incomparable point clouds (e. g. living in different spaces) plays an important role in machine learning.
Low-Rank Sinkhorn Factorization
Because matrix-vector products are pervasive in the Sinkhorn algorithm, several works have proposed to \textit{approximate} kernel matrices appearing in its iterations using low-rank factors.
Mixed Nash Equilibria in the Adversarial Examples Game
This paper tackles the problem of adversarial examples from a game theoretic point of view.
Equitable and Optimal Transport with Multiple Agents
When there is only one agent, we recover the Optimal Transport problem.
Linear Time Sinkhorn Divergences using Positive Features
Although Sinkhorn divergences are now routinely used in data sciences to compare probability distributions, the computational effort required to compute them remains expensive, growing in general quadratically in the size $n$ of the support of these distributions.
Harmonic Decompositions of Convolutional Networks
We present a description of the function space and the smoothness class associated with a convolutional network using the machinery of reproducing kernel Hilbert spaces.
A Spectral Analysis of Dot-product Kernels
We present eigenvalue decay estimates of integral operators associated with compositional dot-product kernels.
Comparing distributions: \ell_1 geometry improves kernel two-sample testing
Here, we show that $L^p$ distances (with $p\geq 1$) between these distribution representatives give metrics on the space of distributions that are well-behaved to detect differences between distributions as they metrize the weak convergence.
Deep K-SVD Denoising
The question we address in this paper is whether K-SVD was brought to its peak in its original conception, or whether it can be made competitive again.
