research
∙
09/18/2023
Context is Environment
Two lines of work are taking the central stage in AI research. On the on...
share
research
∙
06/28/2023
Identifiability of Discretized Latent Coordinate Systems via Density Landmarks Detection
Disentanglement aims to recover meaningful latent ground-truth factors f...
research
∙
05/26/2023
A Closer Look at In-Context Learning under Distribution Shifts
In-context learning, a capability that enables a model to learn from inp...
research
∙
02/21/2023
Reusable Slotwise Mechanisms
Agents that can understand and reason over the dynamics of objects can h...
research
∙
12/20/2022
Recycling diverse models for out-of-distribution generalization
Foundation models are redefining how AI systems are built. Practitioners...
research
∙
11/15/2022
Empirical Study on Optimizer Selection for Out-of-Distribution Generalization
Modern deep learning systems are fragile and do not generalize well unde...
research
∙
06/02/2022
Weakly Supervised Representation Learning with Sparse Perturbations
The theory of representation learning aims to build methods that provabl...
research
∙
05/23/2022
FL Games: A federated learning framework for distribution shifts
Federated learning aims to train predictive models for data that is dist...
research
∙
04/10/2022
Towards efficient representation identification in supervised learning
Humans have a remarkable ability to disentangle complex sensory inputs (...
research
∙
03/18/2022
WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series Tasks
Machine learning models often fail to generalize well under distribution...
research
∙
01/28/2022
Locally Invariant Explanations: Towards Stable and Unidirectional Explanations through Local Invariant Learning
Locally interpretable model agnostic explanations (LIME) method is one o...
research
∙
10/29/2021
Properties from Mechanisms: An Equivariance Perspective on Identifiable Representation Learning
A key goal of unsupervised representation learning is "inverting" a data...
research
∙
06/22/2021
Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge
Treatment effect estimation from observational data is a fundamental pro...
research
∙
06/11/2021
Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization
The invariance principle from causality is at the heart of notable appro...
research
∙
06/05/2021
Can Subnetwork Structure be the Key to Out-of-Distribution Generalization?
Can models with particular structure avoid being biased towards spurious...
research
∙
06/04/2021
SAND-mask: An Enhanced Gradient Masking Strategy for the Discovery of Invariances in Domain Generalization
A major bottleneck in the real-world applications of machine learning mo...
research
∙
03/13/2021
Treatment Effect Estimation using Invariant Risk Minimization
Inferring causal individual treatment effect (ITE) from observational da...
research
∙
12/22/2020
Learning to Initialize Gradient Descent Using Gradient Descent
Non-convex optimization problems are challenging to solve; the success a...
research
∙
10/30/2020
Empirical or Invariant Risk Minimization? A Sample Complexity Perspective
Recently, invariant risk minimization (IRM) was proposed as a promising ...
research
∙
10/28/2020
Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions
Recently, invariant risk minimization (IRM) (Arjovsky et al.) was propos...
research
∙
02/11/2020
Invariant Risk Minimization Games
The standard risk minimization paradigm of machine learning is brittle w...
research
∙
05/02/2019
Estimating Kullback-Leibler Divergence Using Kernel Machines
Recently, a method called the Mutual Information Neural Estimator (MINE)...
research
∙
11/02/2018
Risk-Stratify: Confident Stratification Of Patients Based On Risk
A clinician desires to use a risk-stratification method that achieves co...
research
∙
10/26/2018
Generalized Concordance for Competing Risks
Existing metrics in competing risks survival analysis such as concordanc...
research
∙
06/27/2018
