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08/31/2023
Transformers as Support Vector Machines
Since its inception in "Attention Is All You Need", transformer architec...
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08/16/2023
Can Transformers Learn Optimal Filtering for Unknown Systems?
Transformers have demonstrated remarkable success in natural language pr...
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07/10/2023
FedYolo: Augmenting Federated Learning with Pretrained Transformers
The growth and diversity of machine learning applications motivate a ret...
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06/23/2023
Max-Margin Token Selection in Attention Mechanism
Attention mechanism is a central component of the transformer architectu...
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06/06/2023
On the Role of Attention in Prompt-tuning
Prompt-tuning is an emerging strategy to adapt large language models (LL...
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06/02/2023
Federated Multi-Sequence Stochastic Approximation with Local Hypergradient Estimation
Stochastic approximation with multiple coupled sequences (MSA) has found...
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05/30/2023
Dissecting Chain-of-Thought: A Study on Compositional In-Context Learning of MLPs
Chain-of-thought (CoT) is a method that enables language models to handl...
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05/15/2023
Learning on Manifolds: Universal Approximations Properties using Geometric Controllability Conditions for Neural ODEs
In numerous robotics and mechanical engineering applications, among othe...
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03/08/2023
Provable Pathways: Learning Multiple Tasks over Multiple Paths
Constructing useful representations across a large number of tasks is a ...
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02/02/2023
Stochastic Contextual Bandits with Long Horizon Rewards
The growing interest in complex decision-making and language modeling pr...
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01/17/2023
Transformers as Algorithms: Generalization and Implicit Model Selection in In-context Learning
In-context learning (ICL) is a type of prompting where a transformer mod...
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08/29/2022
Finite Sample Identification of Bilinear Dynamical Systems
Bilinear dynamical systems are ubiquitous in many different domains and ...
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05/12/2022
Representation Learning for Context-Dependent Decision-Making
Humans are capable of adjusting to changing environments flexibly and qu...
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05/04/2022
FEDNEST: Federated Bilevel, Minimax, and Compositional Optimization
Standard federated optimization methods successfully apply to stochastic...
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03/30/2022
System Identification via Nuclear Norm Regularization
This paper studies the problem of identifying low-order linear systems v...
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03/03/2022
Provable and Efficient Continual Representation Learning
In continual learning (CL), the goal is to design models that can learn ...
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01/16/2022
Towards Sample-efficient Overparameterized Meta-learning
An overarching goal in machine learning is to build a generalizable mode...
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01/13/2022
Non-Stationary Representation Learning in Sequential Linear Bandits
In this paper, we study representation learning for multi-task decision-...
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01/04/2022
AutoBalance: Optimized Loss Functions for Imbalanced Data
Imbalanced datasets are commonplace in modern machine learning problems....
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11/13/2021
Identification and Adaptive Control of Markov Jump Systems: Sample Complexity and Regret Bounds
Learning how to effectively control unknown dynamical systems is crucial...
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10/06/2021
Post-hoc Models for Performance Estimation of Machine Learning Inference
Estimating how well a machine learning model performs during inference i...
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05/26/2021
Certainty Equivalent Quadratic Control for Markov Jump Systems
Real-world control applications often involve complex dynamics subject t...
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04/29/2021
Generalization Guarantees for Neural Architecture Search with Train-Validation Split
Neural Architecture Search (NAS) is a popular method for automatically d...
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04/05/2021
Unsupervised Multi-source Domain Adaptation Without Access to Source Data
Unsupervised Domain Adaptation (UDA) aims to learn a predictor model for...
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03/02/2021
Label-Imbalanced and Group-Sensitive Classification under Overparameterization
Label-imbalanced and group-sensitive classification seeks to appropriate...
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02/22/2021
Super-Convergence with an Unstable Learning Rate
Conventional wisdom dictates that learning rate should be in the stable ...
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02/14/2021
Sample Efficient Subspace-based Representations for Nonlinear Meta-Learning
Constructing good representations is critical for learning complex tasks...
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12/16/2020
Provable Benefits of Overparameterization in Model Compression: From Double Descent to Pruning Neural Networks
Deep networks are typically trained with many more parameters than the s...
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11/16/2020
On the Marginal Benefit of Active Learning: Does Self-Supervision Eat Its Cake?
Active learning is the set of techniques for intelligently labeling larg...
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11/16/2020
Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View
Contemporary machine learning applications often involve classification ...
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07/05/2020
Unsupervised Paraphrasing via Deep Reinforcement Learning
Paraphrasing is expressing the meaning of an input sentence in different...
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06/19/2020
Statistical and Algorithmic Insights for Semi-supervised Learning with Self-training
Self-training is a classical approach in semi-supervised learning which ...
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06/19/2020
Exploring Weight Importance and Hessian Bias in Model Pruning
Model pruning is an essential procedure for building compact and computa...
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02/23/2020
On the Role of Dataset Quality and Heterogeneity in Model Confidence
Safety-critical applications require machine learning models that output...
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02/20/2020
Non-asymptotic and Accurate Learning of Nonlinear Dynamical Systems
We consider the problem of learning stabilizable systems governed by non...
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07/03/2019
Quickly Finding the Best Linear Model in High Dimensions
We study the problem of finding the best linear model that can minimize ...
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06/12/2019
Generalization Guarantees for Neural Networks via Harnessing the Low-rank Structure of the Jacobian
Modern neural network architectures often generalize well despite contai...
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03/27/2019
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks
Modern neural networks are typically trained in an over-parameterized re...
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02/12/2019
Towards moderate overparameterization: global convergence guarantees for training shallow neural networks
Many modern neural network architectures are trained in an overparameter...
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12/25/2018
Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path?
Many modern learning tasks involve fitting nonlinear models to data whic...
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09/09/2018
Stochastic Gradient Descent Learns State Equations with Nonlinear Activations
We study discrete time dynamical systems governed by the state equation ...
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06/14/2018
Non-asymptotic Identification of LTI Systems from a Single Trajectory
We consider the problem of learning a realization for a linear time-inva...
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05/16/2018
End-to-end Learning of a Convolutional Neural Network via Deep Tensor Decomposition
In this paper we study the problem of learning the weights of a deep con...
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02/05/2018
Learning Compact Neural Networks with Regularization
We study the impact of regularization for learning neural networks. Our ...
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05/20/2017
Learning Feature Nonlinearities with Non-Convex Regularized Binned Regression
For various applications, the relations between the dependent and indepe...
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10/23/2016
Fast and Reliable Parameter Estimation from Nonlinear Observations
In this paper we study the problem of recovering a structured but unknow...
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11/30/2015
Universality laws for randomized dimension reduction, with applications
Dimension reduction is the process of embedding high-dimensional data in...
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06/11/2015
Isometric sketching of any set via the Restricted Isometry Property
In this paper we show that for the purposes of dimensionality reduction ...
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04/27/2011
Finding Dense Clusters via "Low Rank + Sparse" Decomposition
Finding "densely connected clusters" in a graph is in general an importa...
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11/29/2010
