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Found 9 papers, 3 papers with code
Simple Mechanisms for Representing, Indexing and Manipulating Concepts
Deep networks typically learn concepts via classifiers, which involves setting up a model and training it via gradient descent to fit the concept-labeled data.
Ambient Diffusion: Learning Clean Distributions from Corrupted Data
We present the first diffusion-based framework that can learn an unknown distribution using only highly-corrupted samples.
RISAN: Robust Instance Specific Abstention Network
In this paper, we propose deep architectures for learning instance specific abstain (reject option) binary classifiers.
Learning and Generalization in Overparameterized Normalizing Flows
In supervised learning, it is known that overparameterized neural networks with one hidden layer provably and efficiently learn and generalize, when trained using stochastic gradient descent with a sufficiently small learning rate and suitable initialization.
Rawlsian Fair Adaptation of Deep Learning Classifiers
Given any score function or feature representation and only its second-order statistics on the sensitive sub-populations, we seek a threshold classifier on the given score or a linear threshold classifier on the given feature representation that achieves the Rawls error rate restricted to this hypothesis class.
Learning and Generalization in Univariate Overparameterized Normalizing Flows
In supervised learning, it is known that overparameterized neural networks with one hidden layer provably and efficiently learn and generalize, when trained using Stochastic Gradient Descent (SGD).
Online Active Learning of Reject Option Classifiers
In this paper, we propose novel algorithms for active learning of reject option classifiers.
PLUME: Polyhedral Learning Using Mixture of Experts
In this paper, we propose a novel mixture of expert architecture for learning polyhedral classifiers.
Sparse Reject Option Classifier Using Successive Linear Programming
We also show that the excess risk of loss $L_d$ is upper bounded by the excess risk of $L_{dr}$.
