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05/29/2023
Privileged Knowledge Distillation for Sim-to-Real Policy Generalization
Reinforcement Learning (RL) has recently achieved remarkable success in ...
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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...
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01/09/2023
Exploration in Model-based Reinforcement Learning with Randomized Reward
Model-based Reinforcement Learning (MBRL) has been widely adapted due to...
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12/30/2022
An Analysis of Attention via the Lens of Exchangeability and Latent Variable Models
With the attention mechanism, transformers achieve significant empirical...
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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...
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05/26/2022
Embed to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency
Reinforcement learning in partially observed Markov decision processes (...
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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...
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02/23/2022
Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning
Offline Reinforcement Learning (RL) aims to learn policies from previous...
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02/14/2022
GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges
Color fundus photography and Optical Coherence Tomography (OCT) are the ...
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12/12/2021
Automatic differentiation approach for reconstructing spectral functions with neural networks
Reconstructing spectral functions from Euclidean Green's functions is an...
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11/29/2021
Reconstructing spectral functions via automatic differentiation
Reconstructing spectral functions from Euclidean Green's functions is an...
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10/24/2021
SCORE: Spurious COrrelation REduction for Offline Reinforcement Learning
Offline reinforcement learning (RL) aims to learn the optimal policy fro...
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10/20/2021
Dynamic Bottleneck for Robust Self-Supervised Exploration
Exploration methods based on pseudo-count of transitions or curiosity of...
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10/14/2021
Adaptive Differentially Private Empirical Risk Minimization
We propose an adaptive (stochastic) gradient perturbation method for dif...
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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...
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05/13/2021
Principled Exploration via Optimistic Bootstrapping and Backward Induction
One principled approach for provably efficient exploration is incorporat...
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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...
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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...
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10/17/2020
Variational Dynamic for Self-Supervised Exploration in Deep Reinforcement Learning
Efficient exploration remains a challenging problem in reinforcement lea...
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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...
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06/23/2020
On the Global Optimality of Model-Agnostic Meta-Learning
Model-agnostic meta-learning (MAML) formulates meta-learning as a bileve...
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06/22/2020
Provably Efficient Causal Reinforcement Learning with Confounded Observational Data
Empowered by expressive function approximators such as neural networks, ...
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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...
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05/21/2020
Revisiting Membership Inference Under Realistic Assumptions
Membership inference attacks on models trained using machine learning ha...
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10/30/2019
Efficient Privacy-Preserving Nonconvex Optimization
While many solutions for privacy-preserving convex empirical risk minimi...
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09/13/2019
A Knowledge Transfer Framework for Differentially Private Sparse Learning
We study the problem of estimating high dimensional models with underlyi...
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08/29/2019
Neural Policy Gradient Methods: Global Optimality and Rates of Convergence
Policy gradient methods with actor-critic schemes demonstrate tremendous...
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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...
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11/27/2017
DeepDeblur: Fast one-step blurry face images restoration
We propose a very fast and effective one-step restoring method for blurr...
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04/20/2017
Robust Wirtinger Flow for Phase Retrieval with Arbitrary Corruption
We consider the phase retrieval problem of recovering the unknown signal...
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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...
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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...
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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...
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10/17/2016
