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09/03/2023
Diagnosing the role of observable distribution shift in scientific replications
Many researchers have identified distribution shift as a likely contribu...
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07/18/2023
Model-free selective inference under covariate shift via weighted conformal p-values
This paper introduces weighted conformal p-values for model-free selecti...
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05/23/2023
Uncertainty Quantification over Graph with Conformalized Graph Neural Networks
Graph Neural Networks (GNNs) are powerful machine learning prediction mo...
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04/24/2023
Topological properties and organizing principles of semantic networks
Interpreting natural language is an increasingly important task in compu...
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01/31/2023
UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers
Real-world data contains a vast amount of multimodal information, among ...
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01/18/2023
Semi-Supervised Semantic Segmentation via Gentle Teaching Assistant
Semi-Supervised Semantic Segmentation aims at training the segmentation ...
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12/19/2022
Policy learning "without” overlap: Pessimism and generalized empirical Bernstein's inequality
This paper studies offline policy learning, which aims at utilizing obse...
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11/18/2022
Modular regression
This paper develops a new framework, called modular regression, to utili...
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10/25/2022
Embedded Design of Automatic Pesticide Spraying Robot Control System
In agriculture, crops need to apply pesticide spraying flow control prec...
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10/20/2022
Image-Text Retrieval with Binary and Continuous Label Supervision
Most image-text retrieval work adopts binary labels indicating whether a...
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10/04/2022
Selection by Prediction with Conformal p-values
Decision making or scientific discovery pipelines such as job hiring and...
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09/15/2022
Upper bounds on the Natarajan dimensions of some function classes
The Natarajan dimension is a fundamental tool for characterizing multi-c...
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03/10/2022
The Overlooked Classifier in Human-Object Interaction Recognition
Human-Object Interaction (HOI) recognition is challenging due to two fac...
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03/08/2022
Sensitivity Analysis under the f-Sensitivity Models: Definition, Estimation and Inference
The digitization of the economy has witnessed an explosive growth of ava...
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12/13/2021
Decoupling Object Detection from Human-Object Interaction Recognition
We propose DEFR, a DEtection-FRee method to recognize Human-Object Inter...
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12/12/2021
Improving Vision Transformers for Incremental Learning
This paper studies using Vision Transformers (ViT) in class incremental ...
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11/23/2021
Sensitivity Analysis of Individual Treatment Effects: A Robust Conformal Inference Approach
We propose a model-free framework for sensitivity analysis of individual...
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11/23/2021
Few-Shot Object Detection via Association and DIscrimination
Object detection has achieved substantial progress in the last decade. H...
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10/26/2021
Towards Optimal Variance Reduction in Online Controlled Experiments
We study the optimal variance reduction solutions for online controlled ...
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08/18/2021
Single-DARTS: Towards Stable Architecture Search
Differentiable architecture search (DARTS) marks a milestone in Neural A...
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07/29/2021
Contemporary Symbolic Regression Methods and their Relative Performance
Many promising approaches to symbolic regression have been presented in ...
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07/27/2021
Is Object Detection Necessary for Human-Object Interaction Recognition?
This paper revisits human-object interaction (HOI) recognition at image ...
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04/09/2021
Conditional Inference: Towards a Hierarchy of Statistical Evidence
Statistical uncertainty has many sources. P-values and confidence interv...
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01/27/2021
An Early Stopping Bayesian Data Assimilation Approach for Mixed-Logit Estimation
The mixed-logit model is a flexible tool in transportation choice analys...
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12/30/2020
Is Pessimism Provably Efficient for Offline RL?
We study offline reinforcement learning (RL), which aims to learn an opt...
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10/09/2020
Incorporating planning intelligence into deep learning: A planning support tool for street network design
Deep learning applications in shaping ad hoc planning proposals are limi...
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10/09/2020
DeepStreet: A deep learning powered urban street network generation module
In countries experiencing unprecedented waves of urbanization, there is ...
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02/28/2020
Learning Nonparametric Human Mesh Reconstruction from a Single Image without Ground Truth Meshes
Nonparametric approaches have shown promising results on reconstructing ...
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12/08/2019
Less Confusion More Transferable: Minimum Class Confusion for Versatile Domain Adaptation
Domain Adaptation (DA) transfers a learning model from a labeled source ...
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10/20/2019
Bayesian Symbolic Regression
Interpretability is crucial for machine learning in many scenarios such ...
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06/27/2018