Search Results for author:
Found 21 papers, 8 papers with code
Learning with Noisy Labels: the Exploration of Error Bounds in Classification
Next, we estimate the error bounds of the excess risks, expressed as a sum of statistical error and approximation error.
UGMathBench: A Diverse and Dynamic Benchmark for Undergraduate-Level Mathematical Reasoning with Large Language Models
Each problem includes three randomized versions, with additional versions planned for release as leading open-source LLMs become saturated in UGMathBench.
Can LLMs Solve longer Math Word Problems Better?
We introduce Extended Grade-School Math (E-GSM), a collection of MWPs featuring lengthy narratives, and propose two novel metrics to evaluate the efficacy and resilience of LLMs in tackling these problems.
Can We Verify Step by Step for Incorrect Answer Detection?
Chain-of-Thought (CoT) prompting has marked a significant advancement in enhancing the reasoning capabilities of large language models (LLMs).
Reconstructing Close Human Interactions from Multiple Views
This paper addresses the challenging task of reconstructing the poses of multiple individuals engaged in close interactions, captured by multiple calibrated cameras.
Provable Tensor Completion with Graph Information
For the model, we establish a rigorous mathematical representation of the dynamic graph, based on which we derive a new tensor-oriented graph smoothness regularization.
Optimal Execution Using Reinforcement Learning
This work is about optimal order execution, where a large order is split into several small orders to maximize the implementation shortfall.
Integrating Tick-level Data and Periodical Signal for High-frequency Market Making
We focus on the problem of market making in high-frequency trading.
MFAI: A Scalable Bayesian Matrix Factorization Approach to Leveraging Auxiliary Information
Here, we consider a matrix factorization problem by utilizing auxiliary information, which is massively available in real-world applications, to overcome the challenges caused by poor data quality.
Deep Generative Learning via Schrödinger Bridge
At the sample level, we derive our Schr\"{o}dinger Bridge algorithm by plugging the drift term estimated by a deep score estimator and a deep density ratio estimator into the Euler-Maruyama method.
Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction
The main idea is to use CNNs to learn local appearances of vessels in image crops while using another point-cloud network to learn the global geometry of vessels in the entire image.
On the Bound of Cumulative Return in Trading Series and the Verification Using Technical Trading Rules
Although there is a wide use of technical trading rules in stock markets, the profitability of them still remains controversial.
Wasserstein-Wasserstein Auto-Encoders
To address the challenges in learning deep generative models (e. g., the blurriness of variational auto-encoder and the instability of training generative adversarial networks, we propose a novel deep generative model, named Wasserstein-Wasserstein auto-encoders (WWAE).
BOLT-SSI: A Statistical Approach to Screening Interaction Effects for Ultra-High Dimensional Data
In this paper, we first propose a simple method for sure screening interactions (SSI).
Methodology
Deep Generative Learning via Variational Gradient Flow
We propose a general framework to learn deep generative models via \textbf{V}ariational \textbf{Gr}adient Fl\textbf{ow} (VGrow) on probability spaces.
BIVAS: A scalable Bayesian method for bi-level variable selection with applications
To address this problem, we consider variational inference for bi-level variable selection (BIVAS).
Applications
Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatial-Temporal Patterns
Most of the proposed person re-identification algorithms conduct supervised training and testing on single labeled datasets with small size, so directly deploying these trained models to a large-scale real-world camera network may lead to poor performance due to underfitting.
Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements
In this paper, we propose a novel tensor-based robust PCA (TenRPCA) approach for BSCM by decomposing video frames into backgrounds with spatial-temporal correlations and foregrounds with spatio-temporal continuity in a tensor framework.
Low-Rank Modeling and Its Applications in Image Analysis
In this paper, we review the recent advance of low-rank modeling, the state-of-the-art algorithms, and related applications in image analysis.
A Unified Primal Dual Active Set Algorithm for Nonconvex Sparse Recovery
In this paper, we consider the problem of recovering a sparse signal based on penalized least squares formulations.
Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation
To automate the analysis, object detection without a separate training phase becomes a critical task.
