80 citations · 243 across the 11 of their papers we have counts for
11 papers
TSPulse: Tiny Pre-Trained Models with Disentangled Representations for Rapid Time-Series Analysis
Vijay Ekambaram, Subodh Kumar, Arindam Jati +5
Time-series tasks often benefit from signals expressed across multiple representation spaces (e.g., time vs. frequency) and at varying abstraction levels (e.g., local patterns vs.…
Predictive Optimization with Zero-Shot Domain Adaptation
Tomoya Sakai, Naoto Ohsaka
Prediction in a new domain without any training sample, called zero-shot domain adaptation (ZSDA), is an important task in domain adaptation. While prediction in a new domain has g…
Regret Minimization for Causal Inference on Large Treatment Space
Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi +1
Predicting which action (treatment) will lead to a better outcome is a central task in decision support systems. To build a prediction model in real situations, learning from biase…
Do We Need Zero Training Loss After Achieving Zero Training Error?
Takashi Ishida, Ikko Yamane, Tomoya Sakai +2
Overparameterized deep networks have the capacity to memorize training data with zero \emph{training error}. Even after memorization, the \emph{training loss} continues to approach…
Robust modal regression with direct log-density derivative estimation
Hiroaki Sasaki, Tomoya Sakai, Takafumi Kanamori
Modal regression is aimed at estimating the global mode (i.e., global maximum) of the conditional density function of the output variable given input variables, and has led to regr…
Binary Matrix Completion Using Unobserved Entries
Masayoshi Hayashi, Tomoya Sakai, Masashi Sugiyama
A matrix completion problem, which aims to recover a complete matrix from its partial observations, is one of the important problems in the machine learning field and has been stud…
Information-Theoretic Representation Learning for Positive-Unlabeled Classification
Tomoya Sakai, Gang Niu, Masashi Sugiyama
Recent advances in weakly supervised classification allow us to train a classifier only from positive and unlabeled (PU) data. However, existing PU classification methods typically…
Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning
Tomoya Sakai, Gang Niu, Masashi Sugiyama
Maximizing the area under the receiver operating characteristic curve (AUC) is a standard approach to imbalanced classification. So far, various supervised AUC optimization methods…
Convex Formulation of Multiple Instance Learning from Positive and Unlabeled Bags
Han Bao, Tomoya Sakai, Issei Sato +1
Multiple instance learning (MIL) is a variation of traditional supervised learning problems where data (referred to as bags) are composed of sub-elements (referred to as instances)…
Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data
Tomoya Sakai, Marthinus Christoffel du Plessis, Gang Niu +1
Most of the semi-supervised classification methods developed so far use unlabeled data for regularization purposes under particular distributional assumptions such as the cluster a…
Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning
Gang Niu, Marthinus Christoffel du Plessis, Tomoya Sakai +2
In PU learning, a binary classifier is trained from positive (P) and unlabeled (U) data without negative (N) data. Although N data is missing, it sometimes outperforms PN learning…