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09/18/2023
Context is Environment
Two lines of work are taking the central stage in AI research. On the on...
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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...
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12/20/2022
Recycling diverse models for out-of-distribution generalization
Foundation models are redefining how AI systems are built. Practitioners...
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11/03/2022
ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations
Deep learning vision systems are widely deployed across applications whe...
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07/20/2022
Measuring and signing fairness as performance under multiple stakeholder distributions
As learning machines increase their influence on decisions concerning hu...
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05/23/2022
Throwing Away Data Improves Worst-Class Error in Imbalanced Classification
Class imbalances pervade classification problems, yet their treatment di...
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03/24/2022
Rich Feature Construction for the Optimization-Generalization Dilemma
There often is a dilemma between ease of optimization and robust out-of-...
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10/27/2021
Simple data balancing achieves competitive worst-group-accuracy
We study the problem of learning classifiers that perform well across (k...
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07/13/2021
What classifiers know what they don't?
Being uncertain when facing the unknown is key to intelligent decision m...
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07/02/2020
In Search of Lost Domain Generalization
The goal of domain generalization algorithms is to predict well on distr...
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02/19/2020
Using Hindsight to Anchor Past Knowledge in Continual Learning
In continual learning, the learner faces a stream of data whose distribu...
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07/05/2019
Invariant Risk Minimization
We introduce Invariant Risk Minimization (IRM), a learning paradigm to e...
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03/09/2019
Interpolation Consistency Training for Semi-Supervised Learning
We introduce Interpolation Consistency Training (ICT), a simple and comp...
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02/22/2019
Learning about an exponential amount of conditional distributions
We introduce the Neural Conditioner (NC), a self-supervised machine able...
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11/02/2018
Frequentist uncertainty estimates for deep learning
We provide frequentist estimates of aleatoric and epistemic uncertainty ...
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03/13/2018
SAM: Structural Agnostic Model, Causal Discovery and Penalized Adversarial Learning
We present the Structural Agnostic Model (SAM), a framework to estimate ...
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02/05/2018
Adversarial Vulnerability of Neural Networks Increases With Input Dimension
Over the past four years, neural networks have proven vulnerable to adve...
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12/21/2017
Geometrical Insights for Implicit Generative Modeling
Learning algorithms for implicit generative models can optimize a variet...
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11/24/2017
Causal Generative Neural Networks
We introduce CGNN, a framework to learn functional causal models as gene...
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10/25/2017
mixup: Beyond Empirical Risk Minimization
Large deep neural networks are powerful, but exhibit undesirable behavio...
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09/15/2017
Learning Functional Causal Models with Generative Neural Networks
We introduce a new approach to functional causal modeling from observati...
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07/18/2017
Optimizing the Latent Space of Generative Networks
Generative Adversarial Networks (GANs) have been shown to be able to sam...
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06/26/2017
Gradient Episodic Memory for Continual Learning
One major obstacle towards AI is the poor ability of models to solve new...
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02/23/2017
Causal Discovery Using Proxy Variables
Discovering causal relations is fundamental to reasoning and intelligenc...
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10/20/2016
Revisiting Classifier Two-Sample Tests
The goal of two-sample tests is to assess whether two samples, S_P ∼ P^n...
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07/12/2016
From Dependence to Causation
Machine learning is the science of discovering statistical dependencies ...
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05/26/2016
Discovering Causal Signals in Images
This paper establishes the existence of observable footprints that revea...
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02/09/2016
Minimax Lower Bounds for Realizable Transductive Classification
Transductive learning considers a training set of m labeled samples and ...
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11/11/2015
Unifying distillation and privileged information
Distillation (Hinton et al., 2015) and privileged information (Vapnik & ...
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08/12/2015
No Regret Bound for Extreme Bandits
Algorithms for hyperparameter optimization abound, all of which work wel...
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02/09/2015
Towards a Learning Theory of Cause-Effect Inference
We pose causal inference as the problem of learning to classify probabil...
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02/01/2014
Randomized Nonlinear Component Analysis
Classical methods such as Principal Component Analysis (PCA) and Canonic...
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04/29/2013
The Randomized Dependence Coefficient
We introduce the Randomized Dependence Coefficient (RDC), a measure of n...
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02/16/2013
Gaussian Process Vine Copulas for Multivariate Dependence
Copulas allow to learn marginal distributions separately from the multiv...
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01/01/2013