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Found 22 papers, 14 papers with code
(Almost) Provable Error Bounds Under Distribution Shift via Disagreement Discrepancy
Estimating the bound requires optimizing one multiclass classifier to disagree with another, for which some prior works have used sub-optimal proxy losses; we devise a "disagreement loss" which is theoretically justified and performs better in practice.
Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms
In the supervised setting, we must both learn a classifier and adapt to the dynamically evolving class marginals given only labeled online data.
RLSbench: Domain Adaptation Under Relaxed Label Shift
Despite the emergence of principled methods for domain adaptation under label shift, their sensitivity to shifts in class conditional distributions is precariously under explored.
Rapid-Motion-Track: Markerless Tracking of Fast Human Motion with Deeper Learning
RMT and a range of DLC models were applied to the video data with tapping frequencies up to 8Hz to extract movement features.
Disentangling the Mechanisms Behind Implicit Regularization in SGD
A number of competing hypotheses have been proposed to explain why small-batch Stochastic Gradient Descent (SGD)leads to improved generalization over the full-batch regime, with recent work crediting the implicit regularization of various quantities throughout training.
Characterizing Datapoints via Second-Split Forgetting
Popular metrics derived from these dynamics include (i) the epoch at which examples are first correctly classified; (ii) the number of times their predictions flip during training; and (iii) whether their prediction flips if they are held out.
Downstream Datasets Make Surprisingly Good Pretraining Corpora
In experiments addressing both ELECTRA and RoBERTa models and 10 distinct downstream classification datasets, we observe that self-pretraining rivals standard pretraining on the BookWiki corpus (despite using around $10\times$--$500\times$ less data), outperforming the latter on $7$ and $5$ datasets, respectively.
Unsupervised Learning under Latent Label Shift
Thus motivated, we introduce a practical algorithm that leverages domain-discriminative models as follows: (i) push examples through domain discriminator $p(d|\mathbf{x})$; (ii) discretize the data by clustering examples in $p(d|\mathbf{x})$ space; (iii) perform non-negative matrix factorization on the discrete data; (iv) combine the recovered $p(y|d)$ with the discriminator outputs $p(d|\mathbf{x})$ to compute $p_d(y|x) \; \forall d$.
Domain Adaptation under Open Set Label Shift
We introduce the problem of domain adaptation under Open Set Label Shift (OSLS) where the label distribution can change arbitrarily and a new class may arrive during deployment, but the class-conditional distributions p(x|y) are domain-invariant.
A Comprehensive Review on Deep Supervision: Theories and Applications
Deep supervision, or known as 'intermediate supervision' or 'auxiliary supervision', is to add supervision at hidden layers of a neural network.
Deconstructing Distributions: A Pointwise Framework of Learning
In this work, we propose a new approach: we measure the performance of a collection of models when evaluated on a $\textit{single input point}$.
Leveraging Unlabeled Data to Predict Out-of-Distribution Performance
Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions that may cause performance drops.
Parallel Multi-Scale Networks with Deep Supervision for Hand Keypoint Detection
Keypoint detection plays an important role in a wide range of applications.
Mixture Proportion Estimation and PU Learning: A Modern Approach
Formally, this task is broken down into two subtasks: (i) Mixture Proportion Estimation (MPE) -- determining the fraction of positive examples in the unlabeled data; and (ii) PU-learning -- given such an estimate, learning the desired positive-versus-negative classifier.
Mixture Proportion Estimation and PU Learning:A Modern Approach
Formally, this task is broken down into two subtasks: (i) Mixture Proportion Estimation (MPE)---determining the fraction of positive examples in the unlabeled data; and (ii) PU-learning---given such an estimate, learning the desired positive-versus-negative classifier.
RATT: Leveraging Unlabeled Data to Guarantee Generalization
To assess generalization, machine learning scientists typically either (i) bound the generalization gap and then (after training) plug in the empirical risk to obtain a bound on the true risk; or (ii) validate empirically on holdout data.
Applications of Artificial Intelligence to aid detection of dementia: a narrative review on current capabilities and future directions
With populations ageing, the number of people with dementia worldwide is expected to triple to 152 million by 2050.
On Proximal Policy Optimization's Heavy-tailed Gradients
In this paper, we present a detailed empirical study to characterize the heavy-tailed nature of the gradients of the PPO surrogate reward function.
A Unified View of Label Shift Estimation
Our contributions include (i) consistency conditions for MLLS, which include calibration of the classifier and a confusion matrix invertibility condition that BBSE also requires; (ii) a unified framework, casting BBSE as roughly equivalent to MLLS for a particular choice of calibration method; and (iii) a decomposition of MLLS's finite-sample error into terms reflecting miscalibration and estimation error.
Code-switched Language Models Using Dual RNNs and Same-Source Pretraining
This work focuses on building language models (LMs) for code-switched text.
Dual Language Models for Code Switched Speech Recognition
Since code-switching is a blend of two or more different languages, a standard bilingual language model can be improved upon by using structures of the monolingual language models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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