research
∙
05/29/2023
Privileged Knowledge Distillation for Sim-to-Real Policy Generalization
Reinforcement Learning (RL) has recently achieved remarkable success in ...
share
research
∙
02/17/2023
Approaching epidemiological dynamics of COVID-19 with physics-informed neural networks
A physics-informed neural network (PINN) embedded with the susceptible-i...
research
∙
01/09/2023
Exploration in Model-based Reinforcement Learning with Randomized Reward
Model-based Reinforcement Learning (MBRL) has been widely adapted due to...
research
∙
12/30/2022
An Analysis of Attention via the Lens of Exchangeability and Latent Variable Models
With the attention mechanism, transformers achieve significant empirical...
research
∙
07/29/2022
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning
In view of its power in extracting feature representation, contrastive s...
research
∙
05/26/2022
Embed to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency
Reinforcement learning in partially observed Markov decision processes (...
research
∙
03/10/2022
Nationally Representative Individualized Risk Estimation Combining Individual Data from Epidemiologic Studies and Representative Surveys with Summary Statistics from Disease Re
Estimating individualized absolute risks is fundamental to clinical deci...
research
∙
02/23/2022
Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning
Offline Reinforcement Learning (RL) aims to learn policies from previous...
research
∙
02/14/2022
GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges
Color fundus photography and Optical Coherence Tomography (OCT) are the ...
research
∙
12/12/2021
Automatic differentiation approach for reconstructing spectral functions with neural networks
Reconstructing spectral functions from Euclidean Green's functions is an...
research
∙
11/29/2021
Reconstructing spectral functions via automatic differentiation
Reconstructing spectral functions from Euclidean Green's functions is an...
research
∙
10/24/2021
SCORE: Spurious COrrelation REduction for Offline Reinforcement Learning
Offline reinforcement learning (RL) aims to learn the optimal policy fro...
research
∙
10/20/2021
Dynamic Bottleneck for Robust Self-Supervised Exploration
Exploration methods based on pseudo-count of transitions or curiosity of...
research
∙
10/14/2021
Adaptive Differentially Private Empirical Risk Minimization
We propose an adaptive (stochastic) gradient perturbation method for dif...
research
∙
05/18/2021
Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach
Multi-agent reinforcement learning (MARL) becomes more challenging in th...
research
∙
05/13/2021
Principled Exploration via Optimistic Bootstrapping and Backward Induction
One principled approach for provably efficient exploration is incorporat...
research
∙
11/30/2020
Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk
As the COVID-19 pandemic continues to ravage the world, it is of critica...
research
∙
11/30/2020
Efficient and Robust Propensity-Score-Based Methods for Population Inference using Epidemiologic Cohorts
Most epidemiologic cohorts are composed of volunteers who do not represe...
research
∙
10/17/2020
Variational Dynamic for Self-Supervised Exploration in Deep Reinforcement Learning
Efficient exploration remains a challenging problem in reinforcement lea...
research
∙
07/06/2020
Adjusted Logistic Propensity Weighting Methods for Population Inference using Nonprobability Volunteer-Based Epidemiologic Cohorts
Many epidemiologic studies forgo probability sampling and turn to nonpro...
research
∙
06/23/2020
On the Global Optimality of Model-Agnostic Meta-Learning
Model-agnostic meta-learning (MAML) formulates meta-learning as a bileve...
research
∙
06/22/2020
Provably Efficient Causal Reinforcement Learning with Confounded Observational Data
Empowered by expressive function approximators such as neural networks, ...
research
∙
06/21/2020
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning
Multi-agent reinforcement learning (MARL) achieves significant empirical...
research
∙
05/21/2020
Revisiting Membership Inference Under Realistic Assumptions
Membership inference attacks on models trained using machine learning ha...
research
∙
10/30/2019
Efficient Privacy-Preserving Nonconvex Optimization
While many solutions for privacy-preserving convex empirical risk minimi...
research
∙
09/13/2019
A Knowledge Transfer Framework for Differentially Private Sparse Learning
We study the problem of estimating high dimensional models with underlyi...
research
∙
08/29/2019
Neural Policy Gradient Methods: Global Optimality and Rates of Convergence
Policy gradient methods with actor-critic schemes demonstrate tremendous...
research
∙
06/20/2018
Learning One-hidden-layer ReLU Networks via Gradient Descent
We study the problem of learning one-hidden-layer neural networks with R...
research
∙
11/27/2017
DeepDeblur: Fast one-step blurry face images restoration
We propose a very fast and effective one-step restoring method for blurr...
research
∙
04/20/2017
Robust Wirtinger Flow for Phase Retrieval with Arbitrary Corruption
We consider the phase retrieval problem of recovering the unknown signal...
research
∙
02/21/2017
A Nonconvex Free Lunch for Low-Rank plus Sparse Matrix Recovery
We study the problem of low-rank plus sparse matrix recovery. We propose...
research
∙
01/09/2017
A Universal Variance Reduction-Based Catalyst for Nonconvex Low-Rank Matrix Recovery
We propose a generic framework based on a new stochastic variance-reduce...
research
∙
01/02/2017
Stochastic Variance-reduced Gradient Descent for Low-rank Matrix Recovery from Linear Measurements
We study the problem of estimating low-rank matrices from linear measure...
research
∙
10/17/2016