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Found 218 papers, 67 papers with code
Variational Imitation Learning with Diverse-quality Demonstrations
Learning from demonstrations can be challenging when the quality of demonstrations is diverse, and even more so when the quality is unknown and there is no additional information to estimate the quality.
Accelerating the diffusion-based ensemble sampling by non-reversible dynamics
Compared with the naive parallel-chain SGLD that updates multiple particles independently, ensemble methods update particles with their interactions.
Universal approximation property of invertible neural networks
Invertible neural networks (INNs) are neural network architectures with invertibility by design.
Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients
We propose federation of unsupervised learning (FedUL), where the unlabeled data are transformed into surrogate labeled data for each of the clients, a modified model is trained by supervised FL, and the wanted model is recovered from the modified model.
On the Effectiveness of Adversarial Training against Backdoor Attacks
To explore whether adversarial training could defend against backdoor attacks or not, we conduct extensive experiments across different threat models and perturbation budgets, and find the threat model in adversarial training matters.
Adversarial Attacks and Defense for Non-Parametric Two-Sample Tests
First, we theoretically show that an adversary can upper-bound the distributional shift which guarantees the attack's invisibility.
Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification
There is a fundamental limitation in the prediction performance that a machine learning model can achieve due to the inevitable uncertainty of the prediction target.
Towards Adversarially Robust Deep Image Denoising
Secondly, to robustify DIDs, we propose an adversarial training strategy, hybrid adversarial training ({\sc HAT}), that jointly trains DIDs with adversarial and non-adversarial noisy data to ensure that the reconstruction quality is high and the denoisers around non-adversarial data are locally smooth.
Learning with Proper Partial Labels
Recently, various approaches on partial-label learning have been proposed under different generation models of candidate label sets.
Rethinking Importance Weighting for Transfer Learning
A key assumption in supervised learning is that training and test data follow the same probability distribution.
Can Label-Noise Transition Matrix Help to Improve Sample Selection and Label Correction?
Existing methods for learning with noisy labels can be generally divided into two categories: (1) sample selection and label correction based on the memorization effect of neural networks; (2) loss correction with the transition matrix.
Rethinking Class-Prior Estimation for Positive-Unlabeled Learning
Hitherto, the distributional-assumption-free CPE methods rely on a critical assumption that the support of the positive data distribution cannot be contained in the support of the negative data distribution.
Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum
Specifically, we disentangle the effects of Adaptive Learning Rate and Momentum of the Adam dynamics on saddle-point escaping and flat minima selection.
Unsupervised Federated Learning is Possible
We propose federation of unsupervised learning (FedUL), where the unlabeled data are transformed into surrogate labeled data for each of the clients, a modified model is trained by supervised FL, and the wanted model is recovered from the modified model.
Collaborate to Defend Against Adversarial Attacks
However, an ensemble still wastes the limited capacity of multiple models.
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data
In this paper, we demystify assumptions behind L2DNC and find that high-level semantic features should be shared among the seen and unseen classes.
Exploiting Class Activation Value for Partial-Label Learning
As the first contribution, we empirically show that the class activation map (CAM), a simple technique for discriminating the learning patterns of each class in images, is surprisingly better at making accurate predictions than the model itself on selecting the true label from candidate labels.
Active Refinement for Multi-Label Learning: A Pseudo-Label Approach
The goal of multi-label learning (MLL) is to associate a given instance with its relevant labels from a set of concepts.
Does Adversarial Robustness Really Imply Backdoor Vulnerability?
Based on thorough experiments, we find that such trade-off ignores the interactions between the perturbation budget of adversarial training and the magnitude of the backdoor trigger.
Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences
In this paper, we consider the task of predicting $Y$ from $X$ when we have no paired data of them, but we have two separate, independent datasets of $X$ and $Y$ each observed with some mediating variable $U$, that is, we have two datasets $S_X = \{(X_i, U_i)\}$ and $S_Y = \{(U'_j, Y'_j)\}$.
Positive-Unlabeled Classification under Class-Prior Shift: A Prior-invariant Approach Based on Density Ratio Estimation
Learning from positive and unlabeled (PU) data is an important problem in various applications.
Seeing Differently, Acting Similarly: Imitation Learning with Heterogeneous Observations
So in this work, we model the observation mismatch in the imitation learning problem with the above two challenges as a two-phase learning process, namely Heterogeneously Observable Imitation Learning (HOIL).
Multi-Class Classification from Single-Class Data with Confidences
We show that without any assumptions on the loss functions, models, and optimizers, we can successfully learn a multi-class classifier from only data of a single class with a rigorous consistency guarantee when confidences (i. e., the class-posterior probabilities for all the classes) are available.
Probabilistic Margins for Instance Reweighting in Adversarial Training
Reweighting adversarial data during training has been recently shown to improve adversarial robustness, where data closer to the current decision boundaries are regarded as more critical and given larger weights.
On the Robustness of Average Losses for Partial-Label Learning
Partial-label (PL) learning is a typical weakly supervised classification problem, where a PL of an instance is a set of candidate labels such that a fixed but unknown candidate is the true label.
Loss function based second-order Jensen inequality and its application to particle variational inference
First, we provide a new second-order Jensen inequality, which has the repulsion term based on the loss function.
To Smooth or Not? When Label Smoothing Meets Noisy Labels
We provide understandings for the properties of LS and NLS when learning with noisy labels.
Ranked #4 on
Learning with noisy labels
on CIFAR-10N-Aggregate
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels
In this way, we also give large-loss but less selected data a try; then, we can better distinguish between the cases (a) and (b) by seeing if the losses effectively decrease with the uncertainty after the try.
Ranked #20 on
Image Classification
on mini WebVision 1.0
Instance Correction for Learning with Open-set Noisy Labels
Lots of approaches, e. g., loss correction and label correction, cannot handle such open-set noisy labels well, since they need training data and test data to share the same label space, which does not hold for learning with open-set noisy labels.
NoiLIn: Do Noisy Labels Always Hurt Adversarial Training?
Adversarial training (AT) based on minimax optimization is a popular learning style that enhances the model's adversarial robustness.
A unified view of likelihood ratio and reparameterization gradients
Reparameterization (RP) and likelihood ratio (LR) gradient estimators are used to estimate gradients of expectations throughout machine learning and reinforcement learning; however, they are usually explained as simple mathematical tricks, with no insight into their nature.
Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization
It is well-known that stochastic gradient noise (SGN) acts as implicit regularization for deep learning and is essentially important for both optimization and generalization of deep networks.
Approximating Instance-Dependent Noise via Instance-Confidence Embedding
Label noise in multiclass classification is a major obstacle to the deployment of learning systems.
Discovering Diverse Solutions in Deep Reinforcement Learning by Maximizing State-Action-Based Mutual Information
In our method, a policy conditioned on a continuous or discrete latent variable is trained by directly maximizing the variational lower bound of the mutual information, instead of using the mutual information as unsupervised rewards as in previous studies.
Lower-Bounded Proper Losses for Weakly Supervised Classification
To this end, we derive a representation theorem for proper losses in supervised learning, which dualizes the Savage representation.
LocalDrop: A Hybrid Regularization for Deep Neural Networks
In neural networks, developing regularization algorithms to settle overfitting is one of the major study areas.
Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation
Causal graphs (CGs) are compact representations of the knowledge of the data generating processes behind the data distributions.
Guided Interpolation for Adversarial Training
To enhance adversarial robustness, adversarial training learns deep neural networks on the adversarial variants generated by their natural data.
Learning from Similarity-Confidence Data
Weakly supervised learning has drawn considerable attention recently to reduce the expensive time and labor consumption of labeling massive data.
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection
By comparing \textit{non-robust} (normally trained) and \textit{robustified} (adversarially trained) models, we observe that adversarial training (AT) robustifies CNNs by aligning the channel-wise activations of adversarial data with those of their natural counterparts.
Demystifying Assumptions in Learning to Discover Novel Classes
In learning to discover novel classes (L2DNC), we are given labeled data from seen classes and unlabeled data from unseen classes, and we train clustering models for the unseen classes.
Understanding the Interaction of Adversarial Training with Noisy Labels
A recent adversarial training (AT) study showed that the number of projected gradient descent (PGD) steps to successfully attack a point (i. e., find an adversarial example in its proximity) is an effective measure of the robustness of this point.
Provably End-to-end Label-Noise Learning without Anchor Points
In label-noise learning, the transition matrix plays a key role in building statistically consistent classifiers.
Ranked #10 on
Learning with noisy labels
on CIFAR-100N
Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization
To estimate the transition matrix from noisy data, existing methods often need to estimate the noisy class-posterior, which could be unreliable due to the overconfidence of neural networks.
Learning Diverse-Structured Networks for Adversarial Robustness
In adversarial training (AT), the main focus has been the objective and optimizer while the model has been less studied, so that the models being used are still those classic ones in standard training (ST).
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification
SSC can be solved by a standard (multi-class) classification method, and we use the SSC solution to obtain the final binary classifier through a certain linear-fractional transformation.
Source-free Domain Adaptation via Distributional Alignment by Matching Batch Normalization Statistics
In this setting, we cannot access source data during adaptation, while unlabeled target data and a model pretrained with source data are given.
A Symmetric Loss Perspective of Reliable Machine Learning
When minimizing the empirical risk in binary classification, it is a common practice to replace the zero-one loss with a surrogate loss to make the learning objective feasible to optimize.
On the Role of Pre-training for Meta Few-Shot Learning
One is to (pre-)train a classifier with examples from known classes, and then transfer the pre-trained classifier to unknown classes using the new examples.
Combinatorial Pure Exploration with Full-bandit Feedback and Beyond: Solving Combinatorial Optimization under Uncertainty with Limited Observation
Combinatorial optimization is one of the fundamental research fields that has been extensively studied in theoretical computer science and operations research.
Understanding and Scheduling Weight Decay
Our work aims at theoretically understanding novel behaviors of weight decay and designing schedulers for weight decay in deep learning.
On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective
In this paper, we first prove that the focal loss is classification-calibrated, i. e., its minimizer surely yields the Bayes-optimal classifier and thus the use of the focal loss in classification can be theoretically justified.
Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting
Thus it motivates us to design a similar mechanism named {\it artificial neural variability} (ANV), which helps artificial neural networks learn some advantages from ``natural'' neural networks.
A Survey of Label-noise Representation Learning: Past, Present and Future
Classical machine learning implicitly assumes that labels of the training data are sampled from a clean distribution, which can be too restrictive for real-world scenarios.
Binary classification with ambiguous training data
This problem is substantially different from semi-supervised learning since unlabeled samples are not necessarily difficult samples.
Classification with Rejection Based on Cost-sensitive Classification
The goal of classification with rejection is to avoid risky misclassification in error-critical applications such as medical diagnosis and product inspection.
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks
However, it has been shown that the MMD test is unaware of adversarial attacks -- the MMD test failed to detect the discrepancy between natural and adversarial data.
Robust Imitation Learning from Noisy Demonstrations
Robust learning from noisy demonstrations is a practical but highly challenging problem in imitation learning.
Geometry-aware Instance-reweighted Adversarial Training
The belief was challenged by recent studies where we can maintain the robustness and improve the accuracy.
Pointwise Binary Classification with Pairwise Confidence Comparisons
To alleviate the data requirement for training effective binary classifiers in binary classification, many weakly supervised learning settings have been proposed.
Stable Weight Decay Regularization
\citet{loshchilov2018decoupled} demonstrated that $L_{2}$ regularization is not identical to weight decay for adaptive gradient methods, such as Adaptive Momentum Estimation (Adam), and proposed Adam with Decoupled Weight Decay (AdamW).
Provably Consistent Partial-Label Learning
Partial-label learning (PLL) is a multi-class classification problem, where each training example is associated with a set of candidate labels.
A One-step Approach to Covariate Shift Adaptation
A default assumption in many machine learning scenarios is that the training and test samples are drawn from the same probability distribution.
Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels
In weakly supervised learning, unbiased risk estimator(URE) is a powerful tool for training classifiers when training and test data are drawn from different distributions.
Adai: Separating the Effects of Adaptive Learning Rate and Momentum Inertia
Specifically, we disentangle the effects of Adaptive Learning Rate and Momentum of the Adam dynamics on saddle-point escaping and minima selection.
Online Dense Subgraph Discovery via Blurred-Graph Feedback
Dense subgraph discovery aims to find a dense component in edge-weighted graphs.
Generalisation Guarantees for Continual Learning with Orthogonal Gradient Descent
In this framework, we prove that OGD is robust to Catastrophic Forgetting then derive the first generalisation bound for SGD and OGD for Continual Learning.
Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators
We answer this question by showing a convenient criterion: a CF-INN is universal if its layers contain affine coupling and invertible linear functions as special cases.
Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring
We investigate finite stochastic partial monitoring, which is a general model for sequential learning with limited feedback.
LFD-ProtoNet: Prototypical Network Based on Local Fisher Discriminant Analysis for Few-shot Learning
The prototypical network (ProtoNet) is a few-shot learning framework that performs metric learning and classification using the distance to prototype representations of each class.
Part-dependent Label Noise: Towards Instance-dependent Label Noise
Learning with the \textit{instance-dependent} label noise is challenging, because it is hard to model such real-world noise.
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning
By this intermediate class, the original transition matrix can then be factorized into the product of two easy-to-estimate transition matrices.
$γ$-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator
Approximate Bayesian computation (ABC) is a likelihood-free inference method that has been employed in various applications.
Pairwise Supervision Can Provably Elicit a Decision Boundary
A classifier built upon the representations is expected to perform well in downstream classification; however, little theory has been given in literature so far and thereby the relationship between similarity and classification has remained elusive.
Rethinking Importance Weighting for Deep Learning under Distribution Shift
Under distribution shift (DS) where the training data distribution differs from the test one, a powerful technique is importance weighting (IW) which handles DS in two separate steps: weight estimation (WE) estimates the test-over-training density ratio and weighted classification (WC) trains the classifier from weighted training data.
Calibrated Surrogate Losses for Adversarially Robust Classification
Adversarially robust classification seeks a classifier that is insensitive to adversarial perturbations of test patterns.
Learning from Aggregate Observations
We study the problem of learning from aggregate observations where supervision signals are given to sets of instances instead of individual instances, while the goal is still to predict labels of unseen individuals.
Do Public Datasets Assure Unbiased Comparisons for Registration Evaluation?
In this study, we use the variogram to screen the manually annotated landmarks in two datasets used to benchmark registration in image-guided neurosurgeries.
Time-varying Gaussian Process Bandit Optimization with Non-constant Evaluation Time
If the black-box function varies with time, then time-varying Bayesian optimization is a promising framework.
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger
Adversarial training based on the minimax formulation is necessary for obtaining adversarial robustness of trained models.
Do We Need Zero Training Loss After Achieving Zero Training Error?
We experimentally show that flooding improves performance and, as a byproduct, induces a double descent curve of the test loss.
Progressive Identification of True Labels for Partial-Label Learning
Partial-label learning (PLL) is a typical weakly supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the true label.
A Diffusion Theory For Deep Learning Dynamics: Stochastic Gradient Descent Exponentially Favors Flat Minima
Stochastic Gradient Descent (SGD) and its variants are mainstream methods for training deep networks in practice.
Towards Mixture Proportion Estimation without Irreducibility
It is worthwhile to change the problem: we prove that if the assumption holds, our method will not affect anything; if the assumption does not hold, the bias from problem changing is less than the bias from violation of the irreducible assumption in the original problem.
Few-shot Domain Adaptation by Causal Mechanism Transfer
We take the structural equations in causal modeling as an example and propose a novel DA method, which is shown to be useful both theoretically and experimentally.
Learning from Noisy Similar and Dissimilar Data
We perform a detailed investigation of this problem under two realistic noise models and propose two algorithms to learn from noisy S-D data.
Binary Classification from Positive Data with Skewed Confidence
Positive-confidence (Pconf) classification [Ishida et al., 2018] is a promising weakly-supervised learning method which trains a binary classifier only from positive data equipped with confidence.
Confidence Scores Make Instance-dependent Label-noise Learning Possible
We find with the help of confidence scores, the transition distribution of each instance can be approximately estimated.
Learning with Multiple Complementary Labels
In this paper, we propose a novel problem setting to allow MCLs for each example and two ways for learning with MCLs.
Where is the Bottleneck of Adversarial Learning with Unlabeled Data?
Deep neural networks (DNNs) are incredibly brittle due to adversarial examples.
Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning
We consider the situation in which a user has collected a small set of documents on a cohesive topic, and they want to retrieve additional documents on this topic from a large collection.
Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach
The recently proposed unlabeled-unlabeled (UU) classification method allows us to train a binary classifier only from two unlabeled datasets with different class priors.
A unified view of likelihood ratio and reparameterization gradients and an optimal importance sampling scheme
Reparameterization (RP) and likelihood ratio (LR) gradient estimators are used throughout machine and reinforcement learning; however, they are usually explained as simple mathematical tricks without providing any insight into their nature.
Learning Only from Relevant Keywords and Unlabeled Documents
We consider a document classification problem where document labels are absent but only relevant keywords of a target class and unlabeled documents are given.
Learning from Indirect Observations
Weakly-supervised learning is a paradigm for alleviating the scarcity of labeled data by leveraging lower-quality but larger-scale supervision signals.
Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics
Finally, we investigate the application of multi-agent methods to high-dimensional robotic tasks and show that our approach can be used to learn decentralized policies in this domain.
Wildly Unsupervised Domain Adaptation and Its Powerful and Efficient Solution
Hence, we consider a new, more realistic and more challenging problem setting, where classifiers have to be trained with noisy labeled data from SD and unlabeled data from TD---we name it wildly UDA (WUDA).
Unsupervised Domain Adaptation
Wildly Unsupervised Domain Adaptation
VILD: Variational Imitation Learning with Diverse-quality Demonstrations
However, the quality of demonstrations in reality can be diverse, since it is easier and cheaper to collect demonstrations from a mix of experts and amateurs.
Constraint Learning for Control Tasks with Limited Duration Barrier Functions
When deploying autonomous agents in unstructured environments over sustained periods of time, adaptability and robustness oftentimes outweigh optimality as a primary consideration.
Are Registration Uncertainty and Error Monotonically Associated
Probabilistic image registration (PIR) methods provide measures of registration uncertainty, which could be a surrogate for assessing the registration error.
Classification from Triplet Comparison Data
Although learning from triplet comparison data has been considered in many applications, an important fundamental question of whether we can learn a classifier only from triplet comparison data has remained unanswered.
Direction Matters: On Influence-Preserving Graph Summarization and Max-cut Principle for Directed Graphs
On the other hand, compressing the vertices while preserving the directed edge information provides a way to learn the small-scale representation of a directed graph.
Are Anchor Points Really Indispensable in Label-Noise Learning?
Existing theories have shown that the transition matrix can be learned by exploiting \textit{anchor points} (i. e., data points that belong to a specific class almost surely).
Ranked #14 on
Learning with noisy labels
on CIFAR-10N-Aggregate
Uncoupled Regression from Pairwise Comparison Data
We propose two practical methods for uncoupled regression from pairwise comparison data and show that the learned regression model converges to the optimal model with the optimal parametric convergence rate when the target variable distributes uniformly.
Fast and Robust Rank Aggregation against Model Misspecification
In rank aggregation, preferences from different users are summarized into a total order under the homogeneous data assumption.
Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification
A clue to tackle their direct optimization is a calibrated surrogate utility, which is a tractable lower bound of the true utility function representing a given metric.
Solving NP-Hard Problems on Graphs with Extended AlphaGo Zero
There have been increasing challenges to solve combinatorial optimization problems by machine learning.
Butterfly: One-step Approach towards Wildly Unsupervised Domain Adaptation
Hence, we consider a new, more realistic and more challenging problem setting, where classifiers have to be trained with noisy labeled data from SD and unlabeled data from TD -- we name it wildly UDA (WUDA).
Unsupervised Domain Adaptation
Wildly Unsupervised Domain Adaptation
Classification from Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization
In this paper, we derive an unbiased risk estimator which can handle all of similarities/dissimilarities and unlabeled data.
A Pseudo-Label Method for Coarse-to-Fine Multi-Label Learning with Limited Supervision
To address the need, we propose a special weakly supervised MLL problem that not only focuses on the situation of limited fine-grained supervision but also leverages the hierarchical relationship between the coarse concepts and the fine-grained ones.
Zero-shot Domain Adaptation Based on Attribute Information
In this paper, we propose a novel domain adaptation method that can be applied without target data.
Polynomial-time Algorithms for Multiple-arm Identification with Full-bandit Feedback
Based on our approximation algorithm, we propose novel bandit algorithms for the top-k selection problem, and prove that our algorithms run in polynomial time.
Online Multiclass Classification Based on Prediction Margin for Partial Feedback
We consider the problem of online multiclass classification with partial feedback, where an algorithm predicts a class for a new instance in each round and only receives its correctness.
Semi-Supervised Ordinal Regression Based on Empirical Risk Minimization
We further provide an estimation error bound to show that our risk estimator is consistent.
New Tricks for Estimating Gradients of Expectations
We introduce a family of pairwise stochastic gradient estimators for gradients of expectations, which are related to the log-derivative trick, but involve pairwise interactions between samples.
On the Calibration of Multiclass Classification with Rejection
First, we consider an approach based on simultaneous training of a classifier and a rejector, which achieves the state-of-the-art performance in the binary case.
Domain Discrepancy Measure for Complex Models in Unsupervised Domain Adaptation
Appropriately evaluating the discrepancy between domains is essential for the success of unsupervised domain adaptation.
Revisiting Sample Selection Approach to Positive-Unlabeled Learning: Turning Unlabeled Data into Positive rather than Negative
May there be a new sample selection method that can outperform the latest importance reweighting method in the deep learning age?
Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric
We consider the problem of learning a binary classifier from only positive and unlabeled observations (called PU learning).
Imitation Learning from Imperfect Demonstration
Imitation learning (IL) aims to learn an optimal policy from demonstrations.
On Symmetric Losses for Learning from Corrupted Labels
This paper aims to provide a better understanding of a symmetric loss.
Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks using PAC-Bayesian Analysis
However, existing definitions of the flatness are known to be sensitive to the rescaling of parameters.
How does Disagreement Help Generalization against Label Corruption?
Learning with noisy labels is one of the hottest problems in weakly-supervised learning.
Ranked #9 on
Learning with noisy labels
on CIFAR-10N-Worst
Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization
However, identifying the hierarchical policy structure that enhances the performance of RL is not a trivial task.
Active Deep Q-learning with Demonstration
In this work, we propose Active Reinforcement Learning with Demonstration (ARLD), a new framework to streamline RL in terms of demonstration efforts by allowing the RL agent to query for demonstration actively during training.
Complementary-Label Learning for Arbitrary Losses and Models
In contrast to the standard classification paradigm where the true class is given to each training pattern, complementary-label learning only uses training patterns each equipped with a complementary label, which only specifies one of the classes that the pattern does not belong to.
Ranked #21 on
Image Classification
on Kuzushiji-MNIST
Classification from Positive, Unlabeled and Biased Negative Data
In binary classification, there are situations where negative (N) data are too diverse to be fully labeled and we often resort to positive-unlabeled (PU) learning in these scenarios.
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust
Given data with noisy labels, over-parameterized deep networks can gradually memorize the data, and fit everything in the end.
Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels
To handle these issues, by using the memorization effects of deep neural networks, we may train deep neural networks on the whole dataset only the first few iterations.
Improving Generative Adversarial Imitation Learning with Non-expert Demonstrations
Imitation learning aims to learn an optimal policy from expert demonstrations and its recent combination with deep learning has shown impressive performance.
Positive-Unlabeled Classification under Class Prior Shift and Asymmetric Error
Based on the analysis of the Bayes optimal classifier, we show that given a test class prior, PU classification under class prior shift is equivalent to PU classification with asymmetric error.
Alternate Estimation of a Classifier and the Class-Prior from Positive and Unlabeled Data
In this paper, we propose a novel unified approach to estimating the class-prior and training a classifier alternately.
Dueling Bandits with Qualitative Feedback
We formulate and study a novel multi-armed bandit problem called the qualitative dueling bandit (QDB) problem, where an agent observes not numeric but qualitative feedback by pulling each arm.
Clipped Matrix Completion: A Remedy for Ceiling Effects
On the other hand, matrix completion (MC) methods can recover a low-rank matrix from various information deficits by using the principle of low-rank completion.
Unsupervised Domain Adaptation Based on Source-guided Discrepancy
A previously proposed discrepancy that does not use the source domain labels requires high computational cost to estimate and may lead to a loose generalization error bound in the target domain.
On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data
In this paper, we study training arbitrary (from linear to deep) binary classifier from only unlabeled (U) data by ERM.
Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces
Motivated by the success of reinforcement learning (RL) for discrete-time tasks such as AlphaGo and Atari games, there has been a recent surge of interest in using RL for continuous-time control of physical systems (cf.
Matrix Co-completion for Multi-label Classification with Missing Features and Labels
We consider a challenging multi-label classification problem where both feature matrix $\X$ and label matrix $\Y$ have missing entries.
Masking: A New Perspective of Noisy Supervision
It is important to learn various types of classifiers given training data with noisy labels.
Ranked #29 on
Image Classification
on Clothing1M
(using extra training data)
Bayesian posterior approximation via greedy particle optimization
Another example is the Stein points (SP) method, which minimizes kernelized Stein discrepancy directly.
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
Deep learning with noisy labels is practically challenging, as the capacity of deep models is so high that they can totally memorize these noisy labels sooner or later during training.
Ranked #5 on
Learning with noisy labels
on CIFAR-10N-Random1
A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation
Kernels of the GP are estimated by using variograms and a discrete grid search method.
On the Applicability of Registration Uncertainty
For probabilistic image registration (PIR), the predominant way to quantify the registration uncertainty is using summary statistics of the distribution of transformation parameters.
Uplift Modeling from Separate Labels
Uplift modeling is aimed at estimating the incremental impact of an action on an individual's behavior, which is useful in various application domains such as targeted marketing (advertisement campaigns) and personalized medicine (medical treatments).
Binary Matrix Completion Using Unobserved Entries
In this paper, motivated by a semi-supervised classification method recently proposed by Sakai et al. (2017), we develop a method for the BMC problem which can use all of positive, negative, and unobserved entries, by combining the risks of Davenport et al. (2014) and Hsieh et al. (2015).
Variational Inference for Gaussian Process with Panel Count Data
We present the first framework for Gaussian-process-modulated Poisson processes when the temporal data appear in the form of panel counts.
Active Feature Acquisition with Supervised Matrix Completion
Feature missing is a serious problem in many applications, which may lead to low quality of training data and further significantly degrade the learning performance.
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model
In this paper, we derive a minimax error rate under more practical setting for a broader class of crowdsourcing models including the DS model as a special case.
Classification from Pairwise Similarity and Unlabeled Data
Supervised learning needs a huge amount of labeled data, which can be a big bottleneck under the situation where there is a privacy concern or labeling cost is high.
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks
High sensitivity of neural networks against malicious perturbations on inputs causes security concerns.
Gaussian Process Classification with Privileged Information by Soft-to-Hard Labeling Transfer
A state-of-the-art method of Gaussian process classification (GPC) with privileged information is GPC+, which incorporates privileged information into a noise term of the likelihood.
Generative Local Metric Learning for Kernel Regression
This paper shows how metric learning can be used with Nadaraya-Watson (NW) kernel regression.
Hierarchical Policy Search via Return-Weighted Density Estimation
Learning an optimal policy from a multi-modal reward function is a challenging problem in reinforcement learning (RL).
Binary Classification from Positive-Confidence Data
Can we learn a binary classifier from only positive data, without any negative data or unlabeled data?
Variational Inference based on Robust Divergences
In this paper, based on Zellner's optimization and variational formulation of Bayesian inference, we propose an outlier-robust pseudo-Bayesian variational method by replacing the Kullback-Leibler divergence used for data fitting to a robust divergence such as the beta- and gamma-divergences.
Good Arm Identification via Bandit Feedback
We consider a novel stochastic multi-armed bandit problem called {\em good arm identification} (GAI), where a good arm is defined as an arm with expected reward greater than or equal to a given threshold.
Fully adaptive algorithm for pure exploration in linear bandits
We propose the first fully-adaptive algorithm for pure exploration in linear bandits---the task to find the arm with the largest expected reward, which depends on an unknown parameter linearly.
Information-Theoretic Representation Learning for Positive-Unlabeled Classification
Recent advances in weakly supervised classification allow us to train a classifier only from positive and unlabeled (PU) data.
Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios
Based on the proposed estimator, novel methods both for mode-seeking clustering and density ridge estimation are developed, and the respective convergence rates to the mode and ridge of the underlying density are also established.
Expectation Propagation for t-Exponential Family Using Q-Algebra
Exponential family distributions are highly useful in machine learning since their calculation can be performed efficiently through natural parameters.
Learning from Complementary Labels
Collecting complementary labels would be less laborious than collecting ordinary labels, since users do not have to carefully choose the correct class from a long list of candidate classes.
Guide Actor-Critic for Continuous Control
First, we show that GAC updates the guide actor by performing second-order optimization in the action space where the curvature matrix is based on the Hessians of the critic.
Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling
We model the intensity of each sequence as an infinite mixture of latent functions, each of which is obtained using a function drawn from a Gaussian process.
Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning
Maximizing the area under the receiver operating characteristic curve (AUC) is a standard approach to imbalanced classification.
Stochastic Divergence Minimization for Biterm Topic Model
As the emergence and the thriving development of social networks, a huge number of short texts are accumulated and need to be processed.
Misdirected Registration Uncertainty
Since the transformation is such an essential component of registration, most existing researches conventionally quantify the registration uncertainty, which is the confidence in the estimated spatial correspondences, by the transformation uncertainty.
Convex Formulation of Multiple Instance Learning from Positive and Unlabeled Bags
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) and only bag labels are available.
Positive-Unlabeled Learning with Non-Negative Risk Estimator
From only positive (P) and unlabeled (U) data, a binary classifier could be trained with PU learning, in which the state of the art is unbiased PU learning.
Learning Discrete Representations via Information Maximizing Self-Augmented Training
Learning discrete representations of data is a central machine learning task because of the compactness of the representations and ease of interpretation.
Ranked #3 on
Unsupervised Image Classification
on SVHN
(using extra training data)
Policy Search with High-Dimensional Context Variables
A naive application of unsupervised dimensionality reduction methods to the context variables, such as principal component analysis, is insufficient as task-relevant input may be ignored.
Does Distributionally Robust Supervised Learning Give Robust Classifiers?
Since the DRSL is explicitly formulated for a distribution shift scenario, we naturally expect it to give a robust classifier that can aggressively handle shifted distributions.
Class-prior Estimation for Learning from Positive and Unlabeled Data
Under the assumption that an additional labeled dataset is available, the class prior can be estimated by fitting a mixture of class-wise data distributions to the unlabeled data distribution.
Geometry-aware stationary subspace analysis
The classic SSA method finds a matrix that projects the data onto a stationary subspace by optimizing a cost function based on a matrix divergence.
Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data
Most of the semi-supervised classification methods developed so far use unlabeled data for regularization purposes under particular distributional assumptions such as the cluster assumption.
Reinterpreting the Transformation Posterior in Probabilistic Image Registration
Meanwhile, summary statistics of the posterior are employed to evaluate the registration uncertainty, that is the trustworthiness of the registered image.
Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning
In PU learning, a binary classifier is trained from positive (P) and unlabeled (U) data without negative (N) data.
Whitening-Free Least-Squares Non-Gaussian Component Analysis
Non-Gaussian component analysis (NGCA) is an unsupervised linear dimension reduction method that extracts low-dimensional non-Gaussian "signals" from high-dimensional data contaminated with Gaussian noise.
Non-Gaussian Component Analysis with Log-Density Gradient Estimation
Non-Gaussian component analysis (NGCA) is aimed at identifying a linear subspace such that the projected data follows a non-Gaussian distribution.
Condition for Perfect Dimensionality Recovery by Variational Bayesian PCA
In this paper, we clarify the behavior of VB learning in probabilistic PCA (or fully-observed matrix factorization).
Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions
We also give a convergence-rate analysis of our method and many other previous methods which exploit the geometry of the space.