44 citations · 183 across the 34 of their papers we have counts for
10 papers · 1 filter
Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization
Alexandre Ramé, Kartik Ahuja, Jianyu Zhang +3
Foundation models are redefining how AI systems are built. Practitioners now follow a standard procedure to build their machine learning solutions: from a pre-trained foundation mo…
Empirical Study on Optimizer Selection for Out-of-Distribution Generalization
Hiroki Naganuma, Kartik Ahuja, Shiro Takagi +5
Modern deep learning systems do not generalize well when the test data distribution is slightly different to the training data distribution. While much promising work has been acco…
FL Games: A Federated Learning Framework for Distribution Shifts
Sharut Gupta, Kartik Ahuja, Mohammad Havaei +2
Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server. However, participating clients typically each h…
Interventional Causal Representation Learning
Kartik Ahuja, Divyat Mahajan, Yixin Wang +1
Causal representation learning seeks to extract high-level latent factors from low-level sensory data. Most existing methods rely on observational data and structural assumptions (…
Weakly Supervised Representation Learning with Sparse Perturbations
Kartik Ahuja, Jason Hartford, Yoshua Bengio
The theory of representation learning aims to build methods that provably invert the data generating process with minimal domain knowledge or any source of supervision. Most prior…
FL Games: A federated learning framework for distribution shifts
Sharut Gupta, Kartik Ahuja, Mohammad Havaei +2
Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server. However, participating clients typically each h…
Why does Throwing Away Data Improve Worst-Group Error?
Kamalika Chaudhuri, Kartik Ahuja, Martin Arjovsky +1
When facing data with imbalanced classes or groups, practitioners follow an intriguing strategy to achieve best results. They throw away examples until the classes or groups are ba…
Towards efficient representation identification in supervised learning
Kartik Ahuja, Divyat Mahajan, Vasilis Syrgkanis +1
Humans have a remarkable ability to disentangle complex sensory inputs (e.g., image, text) into simple factors of variation (e.g., shape, color) without much supervision. This abil…
WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series
Jean-Christophe Gagnon-Audet, Kartik Ahuja, Mohammad-Javad Darvishi-Bayazi +3
Machine learning models often fail to generalize well under distributional shifts. Understanding and overcoming these failures have led to a research field of Out-of-Distribution (…
Locally Invariant Explanations: Towards Stable and Unidirectional Explanations through Local Invariant Learning
Amit Dhurandhar, Karthikeyan Ramamurthy, Kartik Ahuja +1
Locally interpretable model agnostic explanations (LIME) method is one of the most popular methods used to explain black-box models at a per example level. Although many variants h…