Search Results for author:
Found 19 papers, 2 papers with code
Synthesis and Completion of Facades from Satellite Imagery
Automatic satellite-based reconstruction enables large and widespread creation of urban areas.
Pre-training Tasks for User Intent Detection and Embedding Retrieval in E-commerce Search
BERT-style models pre-trained on the general corpus (e. g., Wikipedia) and fine-tuned on specific task corpus, have recently emerged as breakthrough techniques in many NLP tasks: question answering, text classification, sequence labeling and so on.
Smooth Nested Simulation: Bridging Cubic and Square Root Convergence Rates in High Dimensions
Nested simulation concerns estimating functionals of a conditional expectation via simulation.
How Does Risk Hedging Impact Operations? Insights from a Price-Setting Newsvendor Model
With the same condition on the target mean return}, hedging also reduces the optimal VPQ when the asset price trend positively impacts product demand; meanwhile, it may increase the VPQ by a small margin when the impact is negative.
Dynamic Selection in Algorithmic Decision-making
This paper identifies and addresses dynamic selection problems in online learning algorithms with endogenous data.
High-Dimensional Simulation Optimization via Brownian Fields and Sparse Grids
High-dimensional simulation optimization is notoriously challenging.
Membraneless organelles formed by liquid-liquid phase separation increase bacterial fitness
Liquid-liquid phase separation is emerging as a crucial phenomenon in several fundamental cell processes.
Causal Reinforcement Learning: An Instrumental Variable Approach
In the standard data analysis framework, data is first collected (once for all), and then data analysis is carried out.
Sample and Computationally Efficient Stochastic Kriging in High Dimensions
However, its use is limited to cases where the design space is low-dimensional because, in general, the sample complexity (i. e., the number of design points required for stochastic kriging to produce an accurate prediction) grows exponentially in the dimensionality of the design space.
MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis
The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications.
Rule-Guided Compositional Representation Learning on Knowledge Graphs
Representation learning on a knowledge graph (KG) is to embed entities and relations of a KG into low-dimensional continuous vector spaces.
Local Distribution in Neighborhood for Classification
Additionally, based on the local distribution, we generate a generalized local classification form that can be effectively applied to various datasets through tuning the parameters.
Ranking and Selection with Covariates for Personalized Decision Making
The goal of ranking and selection with covariates (R&S-C) is to use simulation samples to obtain a selection policy that specifies the best alternative with certain statistical guarantee for subsequent individuals upon observing their covariates.
Towards Compact and Fast Neural Machine Translation Using a Combined Method
Neural Machine Translation (NMT) lays intensive burden on computation and memory cost.
Too Far to See? Not Really! --- Pedestrian Detection with Scale-aware Localization Policy
A major bottleneck of pedestrian detection lies on the sharp performance deterioration in the presence of small-size pedestrians that are relatively far from the camera.
Multivariate Regression with Gross Errors on Manifold-valued Data
Our model first takes a correction step on the grossly corrupted responses via geodesic curves on the manifold, and then performs multivariate linear regression on the corrected data.
Sparse Kernel Canonical Correlation Analysis via $\ell_1$-regularization
Our algorithm is based on a relationship between kernel CCA and least squares.
Multivariate Regression with Grossly Corrupted Observations: A Robust Approach and its Applications
The implementation of our approach and comparison methods as well as the involved datasets are made publicly available in support of the open-source and reproducible research initiatives.
Transduction on Directed Graphs via Absorbing Random Walks
In this paper we consider the problem of graph-based transductive classification, and we are particularly interested in the directed graph scenario which is a natural form for many real world applications.
