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06/27/2023
Length Generalization in Arithmetic Transformers
We examine how transformers cope with two challenges: learning basic int...
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06/20/2023
Open Problem: Learning with Variational Objectives on Measures
The theory of statistical learning has focused on variational objectives...
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04/28/2023
Multisample Flow Matching: Straightening Flows with Minibatch Couplings
Simulation-free methods for training continuous-time generative models c...
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02/23/2023
An Explicit Expansion of the Kullback-Leibler Divergence along its Fisher-Rao Gradient Flow
Let V_* : ℝ^d →ℝ be some (possibly non-convex) potential function, and c...
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01/14/2023
Compress Then Test: Powerful Kernel Testing in Near-linear Time
Kernel two-sample testing provides a powerful framework for distinguishi...
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06/01/2022
Computing the Variance of Shuffling Stochastic Gradient Algorithms via Power Spectral Density Analysis
When solving finite-sum minimization problems, two common alternatives t...
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05/27/2022
Auditing Differential Privacy in High Dimensions with the Kernel Quantum Rényi Divergence
Differential privacy (DP) is the de facto standard for private data rele...
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05/27/2022
Learning with Stochastic Orders
Learning high-dimensional distributions is often done with explicit like...
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02/14/2022
Simultaneous Transport Evolution for Minimax Equilibria on Measures
Min-max optimization problems arise in several key machine learning setu...
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12/27/2021
Depth and Feature Learning are Provably Beneficial for Neural Network Discriminators
We construct pairs of distributions μ_d, ν_d on ℝ^d such that the quanti...
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10/07/2021
Tighter Sparse Approximation Bounds for ReLU Neural Networks
A well-known line of work (Barron, 1993; Breiman, 1993; Klusowski Ba...
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07/11/2021
Dual Training of Energy-Based Models with Overparametrized Shallow Neural Networks
Energy-based models (EBMs) are generative models that are usually traine...
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06/10/2021
Separation Results between Fixed-Kernel and Feature-Learning Probability Metrics
Several works in implicit and explicit generative modeling empirically o...
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04/15/2021
On Energy-Based Models with Overparametrized Shallow Neural Networks
Energy-based models (EBMs) are a simple yet powerful framework for gener...
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10/05/2020
Average-case Acceleration for Bilinear Games and Normal Matrices
Advances in generative modeling and adversarial learning have given rise...
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02/14/2020
A mean-field analysis of two-player zero-sum games
Finding Nash equilibria in two-player zero-sum continuous games is a cen...
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05/29/2019