1
Flatness-Aware Minimization for Domain Generalization
2
Rethinking Distribution Shifts: Empirical Analysis and Inductive Modeling for Tabular Data
3
Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization
4
Distribution Shift Inversion for Out-of-Distribution Prediction
5
Rethinking the Evaluation Protocol of Domain Generalization
6
Out-of-Distribution Generalization in Text Classification: Past, Present, and Future
7
Causal Information Splitting: Engineering Proxy Features for Robustness to Distribution Shifts
8
Predictive Heterogeneity: Measures and Applications
9
Environment Invariant Linear Least Squares
10
Diagnosing Model Performance Under Distribution Shift
11
Change is Hard: A Closer Look at Subpopulation Shift
12
On the Connection between Invariant Learning and Adversarial Training for Out-of-Distribution Generalization
13
Stable Learning via Sparse Variable Independence
14
Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning
15
Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time
16
GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective
17
ZooD: Exploiting Model Zoo for Out-of-Distribution Generalization
18
Product Ranking for Revenue Maximization with Multiple Purchases
19
Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints
20
Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models
21
Prompt Vision Transformer for Domain Generalization
22
Invariant Preference Learning for General Debiasing in Recommendation
23
Equivariant and Invariant Grounding for Video Question Answering
24
Plex: Towards Reliability using Pretrained Large Model Extensions
25
Invariant and Transportable Representations for Anti-Causal Domain Shifts
26
PAC Generalization via Invariant Representations
27
Domain Invariant Masked Autoencoders for Self-supervised Learning from Multi-domains
28
NICO++: Towards Better Benchmarking for Domain Generalization
29
Towards Domain Generalization in Object Detection
30
Causality Inspired Representation Learning for Domain Generalization
31
Out-of-distribution Generalization with Causal Invariant Transformations
32
Decorrelate Irrelevant, Purify Relevant: Overcome Textual Spurious Correlations from a Feature Perspective
33
Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs
34
Regulatory Instruments for Fair Personalized Pricing
35
Provable Domain Generalization via Invariant-Feature Subspace Recovery
36
Discovering Invariant Rationales for Graph Neural Networks
37
High-Resolution Image Synthesis with Latent Diffusion Models
38
On Causally Disentangled Representations
39
Unsupervised Domain Generalization by Learning a Bridge Across Domains
40
Towards Principled Disentanglement for Domain Generalization
41
Masked Autoencoders Are Scalable Vision Learners
42
A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization
43
Simple data balancing achieves competitive worst-group-accuracy
44
Kernelized Heterogeneous Risk Minimization
45
Retiring Adult: New Datasets for Fair Machine Learning
46
Just Train Twice: Improving Group Robustness without Training Group Information
47
A Field Guide to Federated Optimization
48
Towards Unsupervised Domain Generalization
49
Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization
50
Meta-Learning Causal Feature Selection for Stable Prediction
51
Distributionally Robust Learning With Stable Adversarial Training
52
Invariant Information Bottleneck for Domain Generalization
53
Towards a Theoretical Framework of Out-of-Distribution Generalization
54
Invariant Policy Learning: A Causal Perspective
55
Semi-Supervised Domain Generalization with Stochastic StyleMatch
56
A Fourier-based Framework for Domain Generalization
57
Heterogeneous Risk Minimization
58
The iWildCam 2021 Competition Dataset
59
Toward Causal Representation Learning
60
Deep Stable Learning for Out-Of-Distribution Generalization
61
Domain Generalization: A Survey
62
Regularizing towards Causal Invariance: Linear Models with Proxies
63
Generalizing to Unseen Domains: A Survey on Domain Generalization
64
Linear unit-tests for invariance discovery
65
SWAD: Domain Generalization by Seeking Flat Minima
66
Domain adversarial neural networks for domain generalization: when it works and how to improve
67
Out-of-Distribution Generalization Analysis via Influence Function
68
Does Invariant Risk Minimization Capture Invariance?
69
Style Normalization and Restitution for Domain Generalization and Adaptation
70
WILDS: A Benchmark of in-the-Wild Distribution Shifts
71
Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification
72
Meta Batch-Instance Normalization for Generalizable Person Re-Identification
73
Local SGD: Unified Theory and New Efficient Methods
74
Empirical or Invariant Risk Minimization? A Sample Complexity Perspective
75
On the Transfer of Disentangled Representations in Realistic Settings
76
Environment Inference for Invariant Learning
77
The Risks of Invariant Risk Minimization
78
Large-Scale Methods for Distributionally Robust Optimization
79
Disentangled Generative Causal Representation Learning
80
Deep Semisupervised Domain Generalization Network for Rotary Machinery Fault Diagnosis Under Variable Speed
81
Batch Normalization Embeddings for Deep Domain Generalization
82
DeVLBert: Learning Deconfounded Visio-Linguistic Representations
83
DoFE: Domain-Oriented Feature Embedding for Generalizable Fundus Image Segmentation on Unseen Datasets
84
Out-of-Distribution Generalization with Maximal Invariant Predictor
85
Distributionally Robust Losses for Latent Covariate Mixtures
86
Domain Generalization with Optimal Transport and Metric Learning
87
Learning to Learn with Variational Information Bottleneck for Domain Generalization
88
Ensuring Fairness Beyond the Training Data
89
Learning to Generate Novel Domains for Domain Generalization
90
Stable Learning via Differentiated Variable Decorrelation
91
Self-Challenging Improves Cross-Domain Generalization
92
Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains
93
In Search of Lost Domain Generalization
94
Unseen Target Stance Detection with Adversarial Domain Generalization
95
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
96
Decorrelated Clustering with Data Selection Bias
97
SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness
98
Frustratingly Simple Domain Generalization via Image Stylization
99
Algorithmic Decision Making with Conditional Fairness
100
On Disentangled Representations Learned from Correlated Data
101
Risk Variance Penalization: From Distributional Robustness to Causality
102
Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning
103
Domain Generalization using Causal Matching
104
Active Invariant Causal Prediction: Experiment Selection through Stability
105
Stable Adversarial Learning under Distributional Shifts
106
Balance-Subsampled Stable Prediction
107
Domain Extrapolation via Regret Minimization
108
The Pitfalls of Simplicity Bias in Neural Networks
109
Fairness without Demographics through Adversarially Reweighted Learning
110
Language Models are Few-Shot Learners
111
Using publicly available satellite imagery and deep learning to understand economic well-being in Africa
112
An Investigation of Why Overparameterization Exacerbates Spurious Correlations
113
Open Graph Benchmark: Datasets for Machine Learning on Graphs
114
Generalized Convolutional Forest Networks for Domain Generalization and Visual Recognition
115
Single-Side Domain Generalization for Face Anti-Spoofing
116
CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models
117
Meta-Learning in Neural Networks: A Survey
118
Deep Learning and Open Set Malware Classification: A Survey
119
Learning to Learn Single Domain Generalization
120
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates
121
Invariant Rationalization
122
A Unified View of Label Shift Estimation
123
Deep Domain-Adversarial Image Generation for Domain Generalisation
124
Improved Baselines with Momentum Contrastive Learning
125
Out-of-Distribution Generalization via Risk Extrapolation (REx)
126
A Theory of Usable Information Under Computational Constraints
127
FR-Train: A mutual information-based approach to fair and robust training
128
Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization
129
A Simple Framework for Contrastive Learning of Visual Representations
130
Invariant Risk Minimization Games
131
Stable Prediction with Model Misspecification and Agnostic Distribution Shift
132
Incorporating Unlabeled Data into Distributionally Robust Learning
133
Advances and Open Problems in Federated Learning
134
Correlation-aware Adversarial Domain Adaptation and Generalization
135
Stable Learning via Sample Reweighting
136
Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization
137
Momentum Contrast for Unsupervised Visual Representation Learning
138
A Comprehensive Survey on Transfer Learning
139
Adversarial target-invariant representation learning for domain generalization
140
Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects
141
Domain Generalization via Model-Agnostic Learning of Semantic Features
142
Multi-source Domain Adaptation for Semantic Segmentation
143
Conditional Learning of Fair Representations
144
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
145
Unsupervised Domain Adaptation through Self-Supervision
146
Transfer Learning with Dynamic Distribution Adaptation
147
Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization Without Accessing Target Domain Data
148
Staining Invariant Features for Improving Generalization of Deep Convolutional Neural Networks in Computational Pathology
149
Distributionally Robust Optimization: A Review
150
Natural Adversarial Examples
151
Invariant Risk Minimization
152
On the Convergence of FedAvg on Non-IID Data
153
Domain Generalization via Multidomain Discriminant Analysis
154
Towards Non-I.I.D. image classification: A dataset and baselines
155
Likelihood Ratios for Out-of-Distribution Detection
156
Multi-Adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection
157
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks
158
A Generalization Error Bound for Multi-class Domain Generalization
160
Counterfactual Visual Explanations
161
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
162
Domain Generalization by Solving Jigsaw Puzzles
163
Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification
164
From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge
165
Feature-Critic Networks for Heterogeneous Domain Generalization
166
Episodic Training for Domain Generalization
167
Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions
168
Definitions, methods, and applications in interpretable machine learning
169
Multi-Component Image Translation for Deep Domain Generalization
170
Invariance, Causality and Robustness
171
DLOW: Domain Flow for Adaptation and Generalization
172
Moment Matching for Multi-Source Domain Adaptation
173
MetaReg: Towards Domain Generalization using Meta-Regularization
174
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
175
Recent Advances in Open Set Recognition: A Survey
176
Domain Randomization for Scene-Specific Car Detection and Pose Estimation
177
Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data
178
Learning Models with Uniform Performance via Distributionally Robust Optimization
179
Sanity Checks for Saliency Maps
180
Optimal Transport-Based Distributionally Robust Optimization: Structural Properties and Iterative Schemes
181
Domain Generalization with Domain-Specific Aggregation Modules
182
Deep Anomaly Detection with Outlier Exposure
183
Deep Domain Generalization via Conditional Invariant Adversarial Networks
184
Counterfactual Normalization: Proactively Addressing Dataset Shift and Improving Reliability Using Causal Mechanisms
185
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
186
Grounding Visual Explanations
187
Visual Domain Adaptation with Manifold Embedded Distribution Alignment
188
Multicalibration: Calibration for the (Computationally-Identifiable) Masses
189
Fairness Without Demographics in Repeated Loss Minimization
190
Neural Tangent Kernel: Convergence and Generalization in Neural Networks
191
Stable Prediction across Unknown Environments
192
Best Sources Forward: Domain Generalization through Source-Specific Nets
193
Domain Generalization with Adversarial Feature Learning
194
CAUSALITY FROM A DISTRIBUTIONAL ROBUSTNESS POINT OF VIEW
195
Multiaccuracy: Black-Box Post-Processing for Fairness in Classification
196
Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks
197
Generalizing to Unseen Domains via Adversarial Data Augmentation
198
The hardness of conditional independence testing and the generalised covariance measure
199
Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization
200
A Reductions Approach to Fair Classification
201
Path-Specific Counterfactual Fairness
202
Learning Adversarially Fair and Transferable Representations
203
Disentangling by Factorising
204
Generalizing Across Domains via Cross-Gradient Training
205
Detecting and Correcting for Label Shift with Black Box Predictors
206
Anchor regression: Heterogeneous data meet causality
207
A Robust Learning Approach for Regression Models Based on Distributionally Robust Optimization
208
Wasserstein Distributional Robustness and Regularization in Statistical Learning
209
Domain Generalization by Marginal Transfer Learning
210
Functional Map of the World
211
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
212
Certifying Some Distributional Robustness with Principled Adversarial Training
213
Regularization via Mass Transportation
214
Sim-to-Real Transfer of Robotic Control with Dynamics Randomization
215
Learning to Generalize: Meta-Learning for Domain Generalization
216
Deeper, Broader and Artier Domain Generalization
217
Unified Deep Supervised Domain Adaptation and Generalization
218
A Causal And-Or Graph Model for Visibility Fluent Reasoning in Tracking Interacting Objects
219
Causally Regularized Learning with Agnostic Data Selection Bias
220
A causal framework for explaining the predictions of black-box sequence-to-sequence models
221
Invariant Causal Prediction for Nonlinear Models
222
Invariant Causal Prediction for Sequential Data
223
Deep Hashing Network for Unsupervised Domain Adaptation
224
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
225
Avoiding Discrimination through Causal Reasoning
226
Real Time Image Saliency for Black Box Classifiers
227
A Unified Approach to Interpreting Model Predictions
228
Data-Driven Optimal Transport Cost Selection For Distributionally Robust Optimization
229
Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention
230
Characterization of the equivalence of robustification and regularization in linear and matrix regression
231
Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization
232
Counterfactual Fairness
233
Domain randomization for transferring deep neural networks from simulation to the real world
234
Understanding Black-box Predictions via Influence Functions
235
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
236
Axiomatic Attribution for Deep Networks
237
Domain Adaptation for Visual Applications: A Comprehensive Survey
238
Synthetic to Real Adaptation with Generative Correlation Alignment Networks
239
Improved Texture Networks: Maximizing Quality and Diversity in Feed-Forward Stylization and Texture Synthesis
240
Top-Down Visual Saliency Guided by Captions
241
Does Distributionally Robust Supervised Learning Give Robust Classifiers?
242
beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
243
Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment
244
Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments
245
Probabilistic model for code with decision trees
246
Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach
247
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
248
Equality of Opportunity in Supervised Learning
249
Inherent Trade-Offs in the Fair Determination of Risk Scores
250
Robust Domain Generalisation by Enforcing Distribution Invariance
251
Deep CORAL: Correlation Alignment for Deep Domain Adaptation
252
Robot learning with a spatial, temporal, and causal and-or graph
253
Learning Attributes Equals Multi-Source Domain Generalization
254
Approximate residual balancing: debiased inference of average treatment effects in high dimensions
255
Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles
256
Communication-Efficient Learning of Deep Networks from Decentralized Data
257
Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis
258
Censoring Representations with an Adversary
259
Return of Frustratingly Easy Domain Adaptation
260
Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization
261
Distributionally Robust Logistic Regression
262
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
263
Domain Generalization for Object Recognition with Multi-task Autoencoders
264
Invariant Models for Causal Transfer Learning
265
Fairness Constraints: Mechanisms for Fair Classification
266
Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data
267
A survey of multi-source domain adaptation
268
Domain Generalization Based on Transfer Component Analysis
269
Domain-Adversarial Training of Neural Networks
270
Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations
271
Visual Domain Adaptation: A survey of recent advances
272
Deep Convolutional Inverse Graphics Network
273
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
274
Causal inference by using invariant prediction: identification and confidence intervals
275
Adam: A Method for Stochastic Optimization
276
Certifying and Removing Disparate Impact
277
Deep Domain Confusion: Maximizing for Domain Invariance
278
Multi-View Perceptron: a Deep Model for Learning Face Identity and View Representations
279
Unsupervised Domain Adaptation by Backpropagation
280
Transfer Joint Matching for Unsupervised Domain Adaptation
281
Learning to Disentangle Factors of Variation with Manifold Interaction
282
Adaptation Regularization: A General Framework for Transfer Learning
283
A survey on concept drift adaptation
284
Data-driven robust optimization
285
Unbiased Metric Learning: On the Utilization of Multiple Datasets and Web Images for Softening Bias
286
Transfer Feature Learning with Joint Distribution Adaptation
287
A Unified Robust Regression Model for Lasso-like Algorithms
288
Domain Generalization via Invariant Feature Representation
289
Hiring as Cultural Matching
290
On causal and anticausal learning
291
Representation Learning: A Review and New Perspectives
292
Generalizing from Several Related Classification Tasks to a New Unlabeled Sample
293
Data preprocessing techniques for classification without discrimination
294
Fairness through awareness
295
A Survey on Transfer Learning
296
Frustratingly Easy Semi-Supervised Domain Adaptation
297
Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems
298
A theory of learning from different domains
299
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part II: Analysis and Extensions
300
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation
301
Domain Adaptation via Transfer Component Analysis
302
ImageNet: A large-scale hierarchical image database
303
Dataset Shift in Machine Learning
304
Robust Regression and Lasso
305
PREDICTIVE LEARNING VIA RULE ENSEMBLES
306
Random Features for Large-Scale Kernel Machines
307
Greedy Layer-Wise Training of Deep Networks
308
Fast stable direct fitting and smoothness selection for generalized additive models
309
The Elements of Statistical Learning
310
Multi–objective Evolutionary Algorithms for the Risk–return Trade–off in Bank Loan Management
311
An overview of statistical learning theory
312
Robust Solutions to Least-Squares Problems with Uncertain Data
313
Principles of Risk Minimization for Learning Theory
314
Probabilistic reasoning in intelligent systems: Networks of plausible inference
315
Another Look at Tests of Equality between Sets of Coefficients in Two Linear Regressions
316
Measure the Predictive Heterogeneity
317
Wasserstein Distributionally Robust Linear-Quadratic Estimation under Martingale Constraints
318
Bridging the gap: Neural collapse inspired prompt tuning for generalization under class imbalance
319
Prompt-based Distribution Alignment for Domain Generalization in Text Classification
320
Distributionally Robust Optimization with Data Geometry
321
Learning Substructure Invariance for Out-of-Distribution Molecular Representations
322
MetaNorm: Learning to Normalize Few-Shot Batches Across Domains
323
Structure by Architecture: Disentangled Representations without Regularization
324
Systematic generalisation with group invariant predictions
325
Domain-Irrelevant Representation Learning for Unsupervised Domain Generalization
326
OoD-Bench: Benchmarking and Understanding Out-of-Distribution Generalization Datasets and Algorithms
327
Domain Generalization via Entropy Regularization
328
received his BE degree from the Department of Computer Science and Technology
330
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
331
Distributionally Robust Losses Against Mixture Covariate Shifts
332
Deep Domain Generalization With Structured Low-Rank Constraint
333
Multiaccuracy : Black-Box Post-Processing for Fairness in Classi cation
334
research interests include causal inference, stable prediction under selection bias and interpretability of machine learning
335
Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences
336
Feature Space Independent Semi-Supervised Domain Adaptation via Kernel Matching
337
Gradient-based learning applied to document recognition
338
Regression Shrinkage and Selection via the Lasso
339
Inherent Tradeoffs in the Fair Determination of Risk Scores
340
His research interests focus on invariant learning and distributionally
341
How we analyzed the compas recidivism algorithm
