Robust Inference

Robust inference focuses on developing statistical methods and machine learning models that produce reliable and accurate results even when faced with noisy, incomplete, or high-dimensional data, or when underlying assumptions are violated. Current research emphasizes techniques like doubly robust estimation, incorporating data augmentation and generative models, and employing robust loss functions (e.g., Huber loss, Wasserstein Dice loss) within various architectures including deep neural networks (e.g., U-Nets, Transformers) and kernel methods. These advancements are crucial for improving the reliability of inferences across diverse fields, from causal inference in social sciences and medical image analysis to high-dimensional statistical testing and robust stereo matching in computer vision.

Papers

October 22, 2024

Optimal Robust Estimation under Local and Global Corruptions: Stronger Adversary and Smaller Error
Thanasis Pittas, Ankit Pensia
Error Correction Adversary Agent Optimal Estimation Common Corruption Robust Statistic Contamination Detection Robust Inference Class Outlier

October 17, 2024

From Distributional Robustness to Robust Statistics: A Confidence Sets Perspective
Gabriel Chan, Bart Van Parys, Amine Bennouna
Visual Perspective Robust Optimization Parametric Model Distributional Robustness Confidence Set Robust Inference Robust Statistic Nonparametric Modeling

April 14, 2024

DKE-Research at SemEval-2024 Task 2: Incorporating Data Augmentation with Generative Models and Biomedical Knowledge to Enhance Inference Robustness
Yuqi Wang, Zeqiang Wang, Wei Wang, Qi Chen, Kaizhu Huang, Anh Nguyen, Suparna De
Generative Model Data Augmentation Natural Language Inference SemEval 2022 Task Large Pre Trained Language Model Biomedical Knowledge Robust Inference Semantic Perturbation

April 9, 2024

Robust Confidence Intervals in Stereo Matching using Possibility Theory
Roman Malinowski, Emmanuelle Sarrazin, Loïc Dumas, Emmanuel Dubois, Sébastien Destercke
Stereo Matching Confidence Interval Stereo Dataset Disparity Estimation Robust Inference

April 7, 2024

CAVIAR: Categorical-Variable Embeddings for Accurate and Robust Inference
Anirban Mukherjee, Hannah Hanwen Chang
High Dimensional Causal Inference Categorical Data Variable Selection Robust Inference Categorical Encoding

February 18, 2024

Doubly Robust Inference in Causal Latent Factor Models
Alberto Abadie, Anish Agarwal, Raaz Dwivedi, Abhin Shah
Gaussian Process Latent Causal Knowledge Enhanced Conditional Imputation Linear Convergence Average Treatment Effect Robust Inference Inverse Probability Weighting

November 2, 2023

Improving Robustness via Tilted Exponential Layer: A Communication-Theoretic Perspective
Bhagyashree Puranik, Ahmad Beirami, Yao Qin, Upamanyu Madhow
Native Robustness Deep Network Tilting Angle Robust Inference Communication Perspective

September 21, 2023

A Convex Framework for Confounding Robust Inference
Kei Ishikawa, Niao He, Takafumi Kanamori
Empirical Risk Minimization Robust Policy Convex Function Convex Program Robust Inference Offline Contextual Bandit Truncated M Estimator

September 4, 2023

Bridging the Projection Gap: Overcoming Projection Bias Through Parameterized Distance Learning
Chong Zhang, Mingyu Jin, Qinkai Yu, Haochen Xue, Shreyank N Gowda, Xiaobo Jin
Neural Network Generative Adversarial Network Variational Autoencoder Robust Learning Projection Bias Generalized Zero Shot Learning Robust Inference Visual Gap

July 25, 2023

Source Condition Double Robust Inference on Functionals of Inverse Problems
Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara
Inverse Problem Linear Inverse Problem Functional Theory Robust Inference

February 26, 2023

Kernel Conditional Moment Constraints for Confounding Robust Inference
Kei Ishikawa, Niao He
Unobserved Confounders Conservative Value Estimation Robust Inference Offline Contextual Bandit Marginal Sensitivity Model

January 23, 2023

Huber-Robust Confidence Sequences
Hongjian Wang, Aaditya Ramdas
Confidence Interval Robust Inference Confidence Sequence Sequential Experiment

November 22, 2022

Robust High-dimensional Tuning Free Multiple Testing
Jianqing Fan, Zhipeng Lou, Mengxin Yu
High Dimensional Robust Inference False Discovery Rate

September 16, 2022

Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scaling
Boris Landa, Xiuyuan Cheng
Geometric Analysis Gaussian Kernel Scaling Method Robust Inference High Dimensional Noise

August 17, 2022

Two-Stage Robust and Sparse Distributed Statistical Inference for Large-Scale Data
Emadaldin Mozafari-Majd, Visa Koivunen
High Dimensional Many Sparse Robust Optimization Statistical Inference Large Scale Data Parametric Bootstrap Robust Inference

May 20, 2022

What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment
Nathan Kallus
Causal Inference High Quality Counterfactuals Relevant Covariates Potential Harm Accurate Treatment Sharp Bound Robust Inference Robust Bayesian

December 24, 2021

Generalized Wasserstein Dice Loss, Test-time Augmentation, and Transformers for the BraTS 2021 challenge
Lucas Fidon, Suprosanna Shit, Ivan Ezhov, Johannes C. Paetzold, Sébastien Ourselin, Tom Vercauteren
Transformer Megatron Decepticons Challenge Task Brain Tumor Segmentation Test Time Augmentation 3D U Net Robust Ensemble BRAT Challenge Robust Inference