research
∙
09/20/2023
Deep Networks as Denoising Algorithms: Sample-Efficient Learning of Diffusion Models in High-Dimensional Graphical Models
We investigate the approximation efficiency of score functions by deep n...
share
research
∙
07/21/2023
What can a Single Attention Layer Learn? A Study Through the Random Features Lens
Attention layers – which map a sequence of inputs to a sequence of outpu...
research
∙
06/07/2023
Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection
Neural sequence models based on the transformer architecture have demons...
research
∙
02/02/2023
Lower Bounds for Learning in Revealing POMDPs
This paper studies the fundamental limits of reinforcement learning (RL)...
research
∙
12/13/2022
A Novel Location Free Link Prediction in Multiplex Social Networks
In recent decades, the emergence of social networks has enabled internet...
research
∙
11/04/2022
Near-optimal multiple testing in Bayesian linear models with finite-sample FDR control
In high dimensional variable selection problems, statisticians often see...
research
∙
09/29/2022
Partially Observable RL with B-Stability: Unified Structural Condition and Sharp Sample-Efficient Algorithms
Partial Observability – where agents can only observe partial informatio...
research
∙
09/23/2022
Unified Algorithms for RL with Decision-Estimation Coefficients: No-Regret, PAC, and Reward-Free Learning
Finding unified complexity measures and algorithms for sample-efficient ...
research
∙
05/30/2022
Efficient Φ-Regret Minimization in Extensive-Form Games via Online Mirror Descent
A conceptually appealing approach for learning Extensive-Form Games (EFG...
research
∙
05/15/2022
Sample-Efficient Learning of Correlated Equilibria in Extensive-Form Games
Imperfect-Information Extensive-Form Games (IIEFGs) is a prevalent model...
research
∙
04/21/2022
Performance and limitations of the QAOA at constant levels on large sparse hypergraphs and spin glass models
The Quantum Approximate Optimization Algorithm (QAOA) is a general purpo...
research
∙
02/22/2022
Efficient and Differentiable Conformal Prediction with General Function Classes
Quantifying the data uncertainty in learning tasks is often done by lear...
research
∙
02/03/2022
Near-Optimal Learning of Extensive-Form Games with Imperfect Information
This paper resolves the open question of designing near-optimal algorith...
research
∙
11/16/2021
Learning with convolution and pooling operations in kernel methods
Recent empirical work has shown that hierarchical convolutional kernels ...
research
∙
11/13/2021
The Three Stages of Learning Dynamics in High-Dimensional Kernel Methods
To understand how deep learning works, it is crucial to understand the t...
research
∙
10/08/2021
When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently?
Multi-agent reinforcement learning has made substantial empirical progre...
research
∙
06/21/2021
Local convexity of the TAP free energy and AMP convergence for Z2-synchronization
We study mean-field variational Bayesian inference using the TAP approac...
research
∙
06/10/2021
Understanding the Under-Coverage Bias in Uncertainty Estimation
Estimating the data uncertainty in regression tasks is often done by lea...
research
∙
03/08/2021
Exact Gap between Generalization Error and Uniform Convergence in Random Feature Models
Recent work showed that there could be a large gap between the classical...
research
∙
02/25/2021
Learning with invariances in random features and kernel models
A number of machine learning tasks entail a high degree of invariance: t...
research
∙
02/15/2021
Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification
Modern machine learning models with high accuracy are often miscalibrate...
research
∙
01/26/2021
Generalization error of random features and kernel methods: hypercontractivity and kernel matrix concentration
Consider the classical supervised learning problem: we are given data (y...
research
∙
06/24/2020
When Do Neural Networks Outperform Kernel Methods?
For a certain scaling of the initialization of stochastic gradient desce...
research
∙
08/14/2019
The generalization error of random features regression: Precise asymptotics and double descent curve
Deep learning methods operate in regimes that defy the traditional stati...
research
∙
06/21/2019
Limitations of Lazy Training of Two-layers Neural Networks
We study the supervised learning problem under either of the following t...
research
∙
04/27/2019
Linearized two-layers neural networks in high dimension
We consider the problem of learning an unknown function f_ on the d-dime...
research
∙
03/01/2019
Proximal algorithms for constrained composite optimization, with applications to solving low-rank SDPs
We study a family of (potentially non-convex) constrained optimization p...
research
∙
02/16/2019
Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit
We consider learning two layer neural networks using stochastic gradient...
research
∙
08/23/2018
TAP free energy, spin glasses, and variational inference
We consider the Sherrington-Kirkpatrick model of spin glasses with ferro...
research
∙
04/18/2018
A Mean Field View of the Landscape of Two-Layers Neural Networks
Multi-layer neural networks are among the most powerful models in machin...
research
∙
11/15/2017
The landscape of the spiked tensor model
We consider the problem of estimating a large rank-one tensor u^⊗ k∈( R...
research
∙
03/25/2017
Solving SDPs for synchronization and MaxCut problems via the Grothendieck inequality
A number of statistical estimation problems can be addressed by semidefi...
research
∙
07/22/2016