5.2k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- SupélecFR485 papers
- University of Applied Sciences and Arts of Southern SwitzerlandCH485 papers
- New York UniversityUS52 papers
- Google (United States)US16 papers
- Princeton UniversityUS15 papers
- Courant Institute of Mathematical SciencesUS13 papers
- Meta (Israel)IL11 papers
- Massachusetts Institute of TechnologyUS10 papers
- Stanford UniversityUS8 papers
- University of California, BerkeleyUS8 papers
- Cornell UniversityUS7 papers
- Carnegie Mellon UniversityUS6 papers
18 papers · 1 filter
PAC-Learning Uniform Ergodic Communicative Networks
Yihan He
This work addressed the problem of learning a network with communication between vertices. The communication between vertices is presented in the form of perturbation on the measur…
Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods
Marylou Gabrié, Grant M. Rotskoff, Eric Vanden-Eijnden
Normalizing flows can generate complex target distributions and thus show promise in many applications in Bayesian statistics as an alternative or complement to MCMC for sampling p…
FastSHAP: Real-Time Shapley Value Estimation
Neil Jethani, Mukund Sudarshan, Ian Covert +2
Shapley values are widely used to explain black-box models, but they are costly to calculate because they require many model evaluations. We introduce FastSHAP, a method for estima…
On the Sample Complexity of Learning under Invariance and Geometric Stability
Alberto Bietti, Luca Venturi, Joan Bruna
Many supervised learning problems involve high-dimensional data such as images, text, or graphs. In order to make efficient use of data, it is often useful to leverage certain geom…
Separation Results between Fixed-Kernel and Feature-Learning Probability Metrics
Carles Domingo-Enrich, Youssef Mroueh
Several works in implicit and explicit generative modeling empirically observed that feature-learning discriminators outperform fixed-kernel discriminators in terms of the sample q…
Fast Adaptation with Linearized Neural Networks
Wesley J. Maddox, Shuai Tang, Pablo Garcia Moreno +2
The inductive biases of trained neural networks are difficult to understand and, consequently, to adapt to new settings. We study the inductive biases of linearizations of neural n…
Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference
Jiyuan Tu, Weidong Liu, Xiaojun Mao +1
This paper develops an efficient distributed inference algorithm, which is robust against a moderate fraction of Byzantine nodes, namely arbitrary and possibly adversarial machines…
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations
Neil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs +1
While the need for interpretable machine learning has been established, many common approaches are slow, lack fidelity, or hard to evaluate. Amortized explanation methods reduce th…
Self-Regularity of Non-Negative Output Weights for Overparameterized Two-Layer Neural Networks
David Gamarnik, Eren C. Kızıldağ, Ilias Zadik
We consider the problem of finding a two-layer neural network with sigmoid, rectified linear unit (ReLU), or binary step activation functions that "fits" a training data set as acc…
Kernel Interpolation for Scalable Online Gaussian Processes
Samuel Stanton, Wesley J. Maddox, Ian Delbridge +1
Gaussian processes (GPs) provide a gold standard for performance in online settings, such as sample-efficient control and black box optimization, where we need to update a posterio…
Approximation and Learning with Deep Convolutional Models: a Kernel Perspective
Alberto Bietti
The empirical success of deep convolutional networks on tasks involving high-dimensional data such as images or audio suggests that they can efficiently approximate certain functio…
Deep Equals Shallow for ReLU Networks in Kernel Regimes
Alberto Bietti, Francis Bach
Deep networks are often considered to be more expressive than shallow ones in terms of approximation. Indeed, certain functions can be approximated by deep networks provably more e…
Dynamic Batch Learning in High-Dimensional Sparse Linear Contextual Bandits
Zhimei Ren, Zhengyuan Zhou
We study the problem of dynamic batch learning in high-dimensional sparse linear contextual bandits, where a decision maker, under a given maximum-number-of-batch constraint and on…
Deep Direct Likelihood Knockoffs
Mukund Sudarshan, Wesley Tansey, Rajesh Ranganath
Predictive modeling often uses black box machine learning methods, such as deep neural networks, to achieve state-of-the-art performance. In scientific domains, the scientist often…
Why Normalizing Flows Fail to Detect Out-of-Distribution Data
Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson
Detecting out-of-distribution (OOD) data is crucial for robust machine learning systems. Normalizing flows are flexible deep generative models that often surprisingly fail to disti…
Strong Consistency, Graph Laplacians, and the Stochastic Block Model
Shaofeng Deng, Shuyang Ling, Thomas Strohmer
Spectral clustering has become one of the most popular algorithms in data clustering and community detection. We study the performance of classical two-step spectral clustering via…
Global convergence of neuron birth-death dynamics
Grant Rotskoff, Samy Jelassi, Joan Bruna +1
Neural networks with a large number of parameters admit a mean-field description, which has recently served as a theoretical explanation for the favorable training properties of "o…
Support and Invertibility in Domain-Invariant Representations
Fredrik D. Johansson, David Sontag, Rajesh Ranganath
Learning domain-invariant representations has become a popular approach to unsupervised domain adaptation and is often justified by invoking a particular suite of theoretical resul…