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Found 22 papers, 6 papers with code
Low-Rank Contextual Reinforcement Learning from Heterogeneous Human Feedback
Reinforcement learning from human feedback (RLHF) has become a cornerstone for aligning large language models with human preferences.
Minimax Regret Learning for Data with Heterogeneous Subgroups
Given the observed groups of data, we develop a min-max-regret (MMR) learning framework for general supervised learning, which targets to minimize the worst-group regret.
Low-Rank Online Dynamic Assortment with Dual Contextual Information
In this paper, we consider the dynamic assortment problem with dual contexts -- user and item features.
Mutual-Learning Knowledge Distillation for Nighttime UAV Tracking
This framework is constructed to learn a compact and fast nighttime tracker via knowledge transferring from the teacher and knowledge sharing among various students.
Consistency of Lloyd's Algorithm Under Perturbations
In canonical settings with ground truth clusters, we derive bounds for algorithms such as $k$-means$++$ to find good initializations and thus leading to the correctness of clustering via the main result.
A Scalable Arrangement Method for Aperiodic Array Antennas to Reduce Peak Sidelobe Level
Peak sidelobe level reduction (PSLR) is crucial in the application of large-scale array antenna, which directly determines the radiation performance of array antenna.
Asymptotic Inference for Multi-Stage Stationary Treatment Policy with Variable Selection
Dynamic treatment regimes or policies are a sequence of decision functions over multiple stages that are tailored to individual features.
MobRecon: Mobile-Friendly Hand Mesh Reconstruction from Monocular Image
In this work, we propose a framework for single-view hand mesh reconstruction, which can simultaneously achieve high reconstruction accuracy, fast inference speed, and temporal coherence.
Ranked #3 on
3D Hand Pose Estimation
on FreiHAND
Rejoinder: Learning Optimal Distributionally Robust Individualized Treatment Rules
However, when the growth of testing sample size available for training is in a slower order, efficient value function estimates may not perform well anymore.
Efficient Learning of Optimal Individualized Treatment Rules for Heteroscedastic or Misspecified Treatment-Free Effect Models
Other than potential misspecified nuisance models, most existing methods do not account for the potential problem when the variance of outcome is heterogeneous among covariates and treatment.
Improving Robustness for Pose Estimation via Stable Heatmap Regression
Deep learning methods have achieved excellent performance in pose estimation, but the lack of robustness causes the keypoints to change drastically between similar images.
Regressive Domain Adaptation for Unsupervised Keypoint Detection
First, based on our observation that the probability density of the output space is sparse, we introduce a spatial probability distribution to describe this sparsity and then use it to guide the learning of the adversarial regressor.
Camera-Space Hand Mesh Recovery via Semantic Aggregation and Adaptive 2D-1D Registration
In the root-relative mesh recovery task, we exploit semantic relations among joints to generate a 3D mesh from the extracted 2D cues.
Ranked #20 on
3D Hand Pose Estimation
on FreiHAND
Learning Optimal Distributionally Robust Individualized Treatment Rules
We propose a novel distributionally robust ITR (DR-ITR) framework that maximizes the worst-case value function across the values under a set of underlying distributions that are "close" to the training distribution.
High dimensional precision medicine from patient-derived xenografts
PDX data are characterized by correlated outcomes, a high-dimensional feature space, and a large number of treatments.
Statistical Analysis of Stationary Solutions of Coupled Nonconvex Nonsmooth Empirical Risk Minimization
This paper has two main goals: (a) establish several statistical properties---consistency, asymptotic distributions, and convergence rates---of stationary solutions and values of a class of coupled nonconvex and nonsmoothempirical risk minimization problems, and (b) validate these properties by a noisy amplitude-based phase retrieval problem, the latter being of much topical interest. Derived from available data via sampling, these empirical risk minimization problems are the computational workhorse of a population risk model which involves the minimization of an expected value of a random functional.
Adaptive Wasserstein Hourglass for Weakly Supervised Hand Pose Estimation from Monocular RGB
During training, based on the relation between these common characteristics and 3D pose learned from fully-annotated synthetic datasets, it is beneficial for the network to restore the 3D pose of weakly labeled real-world datasets with the aid of 2D annotations and depth images.
Estimation of Individualized Decision Rules Based on an Optimized Covariate-Dependent Equivalent of Random Outcomes
Recent exploration of optimal individualized decision rules (IDRs) for patients in precision medicine has attracted a lot of attention due to the heterogeneous responses of patients to different treatments.
Stability Enhanced Large-Margin Classifier Selection
Stability is an important aspect of a classification procedure because unstable predictions can potentially reduce users' trust in a classification system and also harm the reproducibility of scientific conclusions.
Simultaneous Clustering and Estimation of Heterogeneous Graphical Models
We consider joint estimation of multiple graphical models arising from heterogeneous and high-dimensional observations.
Joint Estimation of Multiple Dependent Gaussian Graphical Models with Applications to Mouse Genomics
In this paper, we propose a novel estimator for data arising from a group of Gaussian graphical models that are themselves dependent.
Large-Margin Classification with Multiple Decision Rules
Binary classification is a common statistical learning problem in which a model is estimated on a set of covariates for some outcome indicating the membership of one of two classes.
