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Found 17 papers, 1 papers with code
Time-aware Heterogeneous Graph Transformer with Adaptive Attention Merging for Health Event Prediction
The widespread application of Electronic Health Records (EHR) data in the medical field has led to early successes in disease risk prediction using deep learning methods.
TransFusion: Covariate-Shift Robust Transfer Learning for High-Dimensional Regression
Nonasymptotic bound is provided for the estimation error of the target model, showing the robustness of the proposed method to covariate shifts.
AdaTrans: Feature-wise and Sample-wise Adaptive Transfer Learning for High-dimensional Regression
We consider the transfer learning problem in the high dimensional setting, where the feature dimension is larger than the sample size.
Enhancing Robustness of Gradient-Boosted Decision Trees through One-Hot Encoding and Regularization
Gradient-boosted decision trees (GBDT) are widely used and highly effective machine learning approach for tabular data modeling.
Detection and Estimation of Structural Breaks in High-Dimensional Functional Time Series
In this paper, we consider detecting and estimating breaks in heterogeneous mean functions of high-dimensional functional time series which are allowed to be cross-sectionally correlated and temporally dependent.
RECLIP: Resource-efficient CLIP by Training with Small Images
We present RECLIP (Resource-efficient CLIP), a simple method that minimizes computational resource footprint for CLIP (Contrastive Language Image Pretraining).
Functional-Coefficient Quantile Regression for Panel Data with Latent Group Structure
A latent group structure is imposed on the heterogenous quantile regression models so that the number of nonparametric functional coefficients to be estimated can be reduced considerably.
MonoIndoor++:Towards Better Practice of Self-Supervised Monocular Depth Estimation for Indoor Environments
As compared to outdoor environments, estimating depth of monocular videos for indoor environments, using self-supervised methods, results in two additional challenges: (i) the depth range of indoor video sequences varies a lot across different frames, making it difficult for the depth network to induce consistent depth cues for training; (ii) the indoor sequences recorded with handheld devices often contain much more rotational motions, which cause difficulties for the pose network to predict accurate relative camera poses.
Transferring Knowledge with Attention Distillation for Multi-Domain Image-to-Image Translation
Gradient-based attention modeling has been used widely as a way to visualize and understand convolutional neural networks.
Learning Local Recurrent Models for Human Mesh Recovery
We conduct a variety of experiments on standard video mesh recovery benchmark datasets such as Human3. 6M, MPI-INF-3DHP, and 3DPW, demonstrating the efficacy of our design of modeling local dynamics as well as establishing state-of-the-art results based on standard evaluation metrics.
Ranked #44 on
3D Human Pose Estimation
on 3DPW
MonoIndoor: Towards Good Practice of Self-Supervised Monocular Depth Estimation for Indoor Environments
The effectiveness of each module is shown through a carefully conducted ablation study and the demonstration of the state-of-the-art performance on three indoor datasets, \ie, EuRoC, NYUv2, and 7-scenes.
Risk-Sensitive Deep RL: Variance-Constrained Actor-Critic Provably Finds Globally Optimal Policy
In particular, we focus on a variance-constrained policy optimization problem where the goal is to find a policy that maximizes the expected value of the long-run average reward, subject to a constraint that the long-run variance of the average reward is upper bounded by a threshold.
Learning the aerodynamic design of supercritical airfoils through deep reinforcement learning
The policy is then trained in environments based on surrogate models, of which the mean drag reduction of 200 airfoils can be effectively improved by reinforcement learning.
Computational Engineering, Finance, and Science Data Analysis, Statistics and Probability
Nearly Dimension-Independent Sparse Linear Bandit over Small Action Spaces via Best Subset Selection
We consider the stochastic contextual bandit problem under the high dimensional linear model.
Towards Visually Explaining Variational Autoencoders
We present methods to generate visual attention from the learned latent space, and also demonstrate such attention explanations serve more than just explaining VAE predictions.
Model-free Feature Screening and FDR Control with Knockoff Features
It can be shown that the proposed two-step approach enjoys both sure screening and FDR control if the pre-specified FDR level $\alpha$ is greater or equal to $1/s$, where $s$ is the number of active features.
On the Feasibility of Distributed Kernel Regression for Big Data
Under mild conditions, we show that, with a proper number of segments, DKR leads to an estimator that is generalization consistent to the unknown regression function.
