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07/24/2023
Provable Benefits of Policy Learning from Human Preferences in Contextual Bandit Problems
A crucial task in decision-making problems is reward engineering. It is ...
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07/13/2023
Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement
We explore the methodology and theory of reward-directed generation via ...
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07/06/2023
Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight
This paper studies the sample-efficiency of learning in Partially Observ...
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07/04/2023
Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks
Convolutional residual neural networks (ConvResNets), though overparamet...
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06/26/2023
Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories
Existing theories on deep nonparametric regression have shown that when ...
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06/02/2023
Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations
In real-world reinforcement learning (RL) systems, various forms of impa...
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05/25/2023
Counterfactual Generative Models for Time-Varying Treatments
Estimating average causal effects is a common practice to test new treat...
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03/18/2023
Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning
Fine-tuning large pre-trained language models on downstream tasks has be...
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02/25/2023
On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds
Generative networks have experienced great empirical successes in distri...
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02/14/2023
Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data
Diffusion models achieve state-of-the-art performance in various generat...
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12/01/2022
High Dimensional Binary Classification under Label Shift: Phase Transition and Regularization
Label Shift has been widely believed to be harmful to the generalization...
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06/06/2022
Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks
We consider the off-policy evaluation problem of reinforcement learning ...
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05/04/2022
A Manifold Two-Sample Test Study: Integral Probability Metric with Neural Networks
Two-sample tests are important areas aiming to determine whether two col...
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01/06/2022
Deep Learning Assisted End-to-End Synthesis of mm-Wave Passive Networks with 3D EM Structures: A Study on A Transformer-Based Matching Network
This paper presents a deep learning assisted synthesis approach for dire...
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01/01/2022
Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces
Learning operators between infinitely dimensional spaces is an important...
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10/07/2021
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect
Recent empirical advances show that training deep models with large lear...
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09/07/2021
Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks
Most of existing statistical theories on deep neural networks have sampl...
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05/25/2021
Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization
The Lottery Ticket Hypothesis suggests that an over-parametrized network...
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11/03/2020
Doubly Robust Off-Policy Learning on Low-Dimensional Manifolds by Deep Neural Networks
Causal inference explores the causation between actions and the conseque...
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10/12/2020
How Important is the Train-Validation Split in Meta-Learning?
Meta-learning aims to perform fast adaptation on a new task through lear...
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08/25/2020
Residual Network Based Direct Synthesis of EM Structures: A Study on One-to-One Transformers
We propose using machine learning models for the direct synthesis of on-...
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06/24/2020
Towards Understanding Hierarchical Learning: Benefits of Neural Representations
Deep neural networks can empirically perform efficient hierarchical lear...
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02/16/2020
Differentiable Top-k Operator with Optimal Transport
The top-k operation, i.e., finding the k largest or smallest elements fr...
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02/10/2020
Statistical Guarantees of Generative Adversarial Networks for Distribution Estimation
Generative Adversarial Networks (GANs) have achieved great success in un...
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01/09/2020
On Computation and Generalization of Generative Adversarial Imitation Learning
Generative Adversarial Imitation Learning (GAIL) is a powerful and pract...
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10/28/2019
On Generalization Bounds of a Family of Recurrent Neural Networks
Recurrent Neural Networks (RNNs) have been widely applied to sequential ...
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09/10/2019
Towards Understanding the Importance of Shortcut Connections in Residual Networks
Residual Network (ResNet) is undoubtedly a milestone in deep learning. R...
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08/05/2019
Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds
Deep neural networks have revolutionized many real world applications, d...
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05/01/2019
On Scalable and Efficient Computation of Large Scale Optimal Transport
Optimal Transport (OT) naturally arises in many machine learning applica...
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12/28/2018
On Computation and Generalization of GANs with Spectrum Control
Generative Adversarial Networks (GANs), though powerful, is hard to trai...
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03/06/2018