Search Results for author:
Found 27 papers, 4 papers with code
Synthesis and Completion of Facades from Satellite Imagery
Automatic satellite-based reconstruction enables large and widespread creation of urban areas.
DEMONet: Underwater Acoustic Target Recognition based on Multi-Expert Network and Cross-Temporal Variational Autoencoder
Building a robust underwater acoustic recognition system in real-world scenarios is challenging due to the complex underwater environment and the dynamic motion states of targets.
Seesaw Experimentation: A/B Tests with Spillovers
This paper investigates how a firm's performance can decline even when consistently implementing innovations validated through A/B testing.
AI Persuasion, Bayesian Attribution, and Career Concerns of Doctors
AI's interpretability influences how doctors attribute these sources and their willingness to change their minds.
Conjugate-Gradient-like Based Adaptive Moment Estimation Optimization Algorithm for Deep Learning
Training deep neural networks is a challenging task.
Helmsman of the Masses? Evaluate the Opinion Leadership of Large Language Models in the Werewolf Game
The results suggest that the Werewolf game is a suitable test bed to evaluate the opinion leadership of LLMs, and few LLMs possess the capacity for opinion leadership.
CPCL: Cross-Modal Prototypical Contrastive Learning for Weakly Supervised Text-based Person Re-Identification
Weakly supervised text-based person re-identification (TPRe-ID) seeks to retrieve images of a target person using textual descriptions, without relying on identity annotations and is more challenging and practical.
Regret Optimality of GP-UCB
We establish new upper bounds on both the simple and cumulative regret of GP-UCB when the objective function to optimize admits certain smoothness property.
Learning to Simulate: Generative Metamodeling via Quantile Regression
Traditional metamodeling techniques learn relationships between simulator inputs and a single output summary statistic, such as the mean or median.
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.
Asymptotic Theory for IV-Based Reinforcement Learning with Potential Endogeneity
We model this as a Markov decision process and show that the dynamic interaction between data generation and data analysis leads to a new type of bias -- reinforcement bias -- that exacerbates the endogeneity problem in standard data analysis.
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.
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.
Towards Compact and Fast Neural Machine Translation Using a Combined Method
Neural Machine Translation (NMT) lays intensive burden on computation and memory cost.
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.
