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Found 23 papers, 14 papers with code
Knowledge-Aware Meta-learning for Low-Resource Text Classification
Meta-learning has achieved great success in leveraging the historical learned knowledge to facilitate the learning process of the new task.
A Field Guide to Federated Optimization
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection.
On Data Efficiency of Meta-learning
Meta-learning has enabled learning statistical models that can be quickly adapted to new prediction tasks.
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms
Federated learning is typically approached as an optimization problem, where the goal is to minimize a global loss function by distributing computation across client devices that possess local data and specify different parts of the global objective.
Learning from Imperfect Annotations
Many machine learning systems today are trained on large amounts of human-annotated data.
Learning from Imperfect Annotations: An End-to-End Approach
Many machine learning systems today are trained on large amounts of human-annotated data.
Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version)
Most of the work on interpretable machine learning has focused on designing either inherently interpretable models, which typically trade-off accuracy for interpretability, or post-hoc explanation systems, which lack guarantees about their explanation quality.
Consistency by Agreement in Zero-shot Neural Machine Translation
Generalization and reliability of multilingual translation often highly depend on the amount of available parallel data for each language pair of interest.
Regularizing Black-box Models for Improved Interpretability
Most of the work on interpretable machine learning has focused on designing either inherently interpretable models, which typically trade-off accuracy for interpretability, or post-hoc explanation systems, whose explanation quality can be unpredictable.
On the Complexity of Exploration in Goal-Driven Navigation
Next, we propose to measure the complexity of each environment by constructing dependency graphs between the goals and analytically computing \emph{hitting times} of a random walk in the graph.
A Better Baseline for Second Order Gradient Estimation in Stochastic Computation Graphs
To improve the sample efficiency of DiCE, we propose a new baseline term for higher order gradient estimation.
DiCE: The Infinitely Differentiable Monte Carlo Estimator
Lastly, to match the first-order gradient under differentiation, SL treats part of the cost as a fixed sample, which we show leads to missing and wrong terms for estimators of higher-order derivatives.
Learning Policy Representations in Multiagent Systems
Modeling agent behavior is central to understanding the emergence of complex phenomena in multiagent systems.
DiCE: The Infinitely Differentiable Monte-Carlo Estimator
Lastly, to match the first-order gradient under differentiation, SL treats part of the cost as a fixed sample, which we show leads to missing and wrong terms for estimators of higher-order derivatives.
The Intriguing Properties of Model Explanations
Linear approximations to the decision boundary of a complex model have become one of the most popular tools for interpreting predictions.
Personalized Survival Prediction with Contextual Explanation Networks
Accurate and transparent prediction of cancer survival times on the level of individual patients can inform and improve patient care and treatment practices.
Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments
Ability to continuously learn and adapt from limited experience in nonstationary environments is an important milestone on the path towards general intelligence.
Learning with Opponent-Learning Awareness
We also show that the LOLA update rule can be efficiently calculated using an extension of the policy gradient estimator, making the method suitable for model-free RL.
Contextual Explanation Networks
Our results on image and text classification and survival analysis tasks demonstrate that CENs are not only competitive with the state-of-the-art methods but also offer additional insights behind each prediction, that can be valuable for decision support.
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Image Classification
Interpretability Techniques for Deep Learning
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Learning Scalable Deep Kernels with Recurrent Structure
To model such structure, we propose expressive closed-form kernel functions for Gaussian processes.
Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices
Recently, there has been a surge of interest in using spectral methods for estimating latent variable models.
Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines
Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex.
Learning Non-deterministic Representations with Energy-based Ensembles
These mappings are encoded in a distribution over a (possibly infinite) collection of models.
