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Found 274 papers, 94 papers with code
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.
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.
Parallel simulation for sampling under isoperimetry and score-based diffusion models
Our work highlights the potential advantages of simulation methods in scientific computation for dynamics-based sampling and diffusion models.
Beyond Simple Sum of Delayed Rewards: Non-Markovian Reward Modeling for Reinforcement Learning
In this paper, we introduce the problem of RL from Composite Delayed Reward (RLCoDe), which generalizes traditional RL from delayed rewards by eliminating the strong assumption.
Sharpness-Aware Black-Box Optimization
To address those problems in black-box optimization, we propose a novel Sharpness-Aware Black-box Optimization (SABO) algorithm, which applies a sharpness-aware minimization strategy to improve the model generalization.
On Unsupervised Prompt Learning for Classification with Black-box Language Models
In this paper, we propose unsupervised prompt learning for classification with black-box LLMs, where the learning parameters are the prompt itself and the pseudo labels of unlabeled data.
Vision-Language Model Fine-Tuning via Simple Parameter-Efficient Modification
Recent advances in fine-tuning Vision-Language Models (VLMs) have witnessed the success of prompt tuning and adapter tuning, while the classic model fine-tuning on inherent parameters seems to be overlooked.
Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning
In this paper, we propose a dual-perspective method to generate high-quality pseudo-labels.
Unlearning with Control: Assessing Real-world Utility for Large Language Model Unlearning
The compelling goal of eradicating undesirable data behaviors, while preserving usual model functioning, underscores the significance of machine unlearning within the domain of large language models (LLMs).
Decoupling the Class Label and the Target Concept in Machine Unlearning
Previous studies showed that class-wise unlearning is successful in forgetting the knowledge of a target class, through gradient ascent on the forgetting data or fine-tuning with the remaining data.
Slight Corruption in Pre-training Data Makes Better Diffusion Models
They benefit significantly from extensive pre-training on large-scale datasets, including web-crawled data with paired data and conditions, such as image-text and image-class pairs.
Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization
However, the local loss landscapes may not accurately reflect the flatness of global loss landscape in heterogeneous environments; as a result, minimizing local sharpness and calculating perturbations on client data might not align the efficacy of SAM in FL with centralized training.
Multi-Player Approaches for Dueling Bandits
Various approaches have emerged for multi-armed bandits in distributed systems.
Offline Reinforcement Learning from Datasets with Structured Non-Stationarity
Current Reinforcement Learning (RL) is often limited by the large amount of data needed to learn a successful policy.
Balancing Similarity and Complementarity for Federated Learning
Our framework aims to approximate an optimal cooperation network for each client by optimizing a weighted sum of model similarity and feature complementarity.
Leveraging Domain-Unlabeled Data in Offline Reinforcement Learning across Two Domains
In this paper, we investigate an offline reinforcement learning (RL) problem where datasets are collected from two domains.
Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training
In this paper, we provide a causal inference framework to show that the correlative features caused by the target object and its co-occurring objects can be regarded as a mediator, which has both positive and negative impacts on model predictions.
Reinforcement Learning with Options and State Representation
The current thesis aims to explore the reinforcement learning field and build on existing methods to produce improved ones to tackle the problem of learning in high-dimensional and complex environments.
Learning with Noisy Foundation Models
Foundation models are usually pre-trained on large-scale datasets and then adapted to downstream tasks through tuning.
VEC-SBM: Optimal Community Detection with Vectorial Edges Covariates
Social networks are often associated with rich side information, such as texts and images.
Generating Chain-of-Thoughts with a Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought
To improve the ability of the large language model (LLMs) to tackle complex reasoning problems, chain-of-thoughts (CoT) methods were proposed to guide LLMs to reason step-by-step, enabling problem solving from simple to complex.
Reinforcement Learning from Bagged Reward
Theoretically, we demonstrate that RLBR can be addressed by solving a standard MDP with properly redistributed bagged rewards allocated to each instance within a bag.
A General Framework for Learning from Weak Supervision
Weakly supervised learning generally faces challenges in applicability to various scenarios with diverse weak supervision and in scalability due to the complexity of existing algorithms, thereby hindering the practical deployment.
Direct Distillation between Different Domains
Then, we build a fusion-activation mechanism to transfer the valuable domain-invariant knowledge to the student network, while simultaneously encouraging the adapter within the teacher network to learn the domain-specific knowledge of the target data.
Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical
Existing consistent approaches have relied on the uniform distribution assumption to model the generation of complementary labels, or on an ordinary-label training set to estimate the transition matrix in non-uniform cases.
Fixed-Budget Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit
We show that the upper bound of the probability of error of the CSA algorithm matches a lower bound up to a logarithmic factor in the exponent.
Atom-Motif Contrastive Transformer for Molecular Property Prediction
As motifs in a molecule are significant patterns that are of great importance for determining molecular properties (e. g., toxicity and solubility), overlooking motif interactions inevitably hinders the effectiveness of MPP.
Thompson Exploration with Best Challenger Rule in Best Arm Identification
This paper studies the fixed-confidence best arm identification (BAI) problem in the bandit framework in the canonical single-parameter exponential models.
Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks
This paper aims to understand the nature of noise in pre-training datasets and to mitigate its impact on downstream tasks.
Unified Risk Analysis for Weakly Supervised Learning
The analysis component of the framework, viewed as a decontamination process, provides a systematic method of conducting risk rewrite.
Thompson Sampling for Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit
We introduce an algorithm named the Generalized Thompson Sampling Explore (GenTS-Explore) algorithm, which is the first algorithm that can work even when the size of the action set is exponentially large in $d$.
Multi-Label Knowledge Distillation
Existing knowledge distillation methods typically work by imparting the knowledge of output logits or intermediate feature maps from the teacher network to the student network, which is very successful in multi-class single-label learning.
Distribution Shift Matters for Knowledge Distillation with Webly Collected Images
Knowledge distillation aims to learn a lightweight student network from a pre-trained teacher network.
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation
In this paper, we propose a diversity-enhancing generative network (DEG-Net) for the FHA problem, which can generate diverse unlabeled data with the help of a kernel independence measure: the Hilbert-Schmidt independence criterion (HSIC).
An Optimal Algorithm for the Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit
In such a case, the R-CPE-MAB can be seen as a special case of the so-called transductive linear bandits.
Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision
Training a classifier exploiting a huge amount of supervised data is expensive or even prohibited in a situation, where the labeling cost is high.
BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning
To address the challenge posed by BadLabel, we further propose a robust LNL method that perturbs the labels in an adversarial manner at each epoch to make the loss values of clean and noisy labels again distinguishable.
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations
In this paper, we introduce imprecise label learning (ILL), a framework for the unification of learning with various imprecise label configurations.
Ranked #1 on
Learning with noisy labels
on mini WebVision 1.0
Enriching Disentanglement: From Logical Definitions to Quantitative Metrics
Disentangling the explanatory factors in complex data is a promising approach for generalizable and data-efficient representation learning.
Enhancing Label Sharing Efficiency in Complementary-Label Learning with Label Augmentation
Our analysis reveals that the efficiency of implicit label sharing is closely related to the performance of existing CLL models.
A Category-theoretical Meta-analysis of Definitions of Disentanglement
Disentangling the factors of variation in data is a fundamental concept in machine learning and has been studied in various ways by different researchers, leading to a multitude of definitions.
Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning
Pseudo-labeling has emerged as a popular and effective approach for utilizing unlabeled data.
Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization
To improve transferability, the existing work introduced the standard invariant regularization (SIR) to impose style-independence property to SCL, which can exempt the impact of nuisance style factors in the standard representation.
Fairness Improves Learning from Noisily Labeled Long-Tailed Data
Both long-tailed and noisily labeled data frequently appear in real-world applications and impose significant challenges for learning.
The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models
Although the uniform prior is shown to be optimal, we highlight the inherent limitation of its optimality, which is limited to specific parameterizations and emphasizes the significance of the invariance property of priors.
Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection
Adversarial contrastive learning (ACL) does not require expensive data annotations but outputs a robust representation that withstands adversarial attacks and also generalizes to a wide range of downstream tasks.
Adapting to Continuous Covariate Shift via Online Density Ratio Estimation
Our density ratio estimation method is proven to perform well by enjoying a dynamic regret bound, which finally leads to an excess risk guarantee for the predictor.
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks
While leveraging additional training data is well established to improve adversarial robustness, it incurs the unavoidable cost of data collection and the heavy computation to train models.
Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits
In addition to the empirical performance, TS has been shown to achieve asymptotic problem-dependent lower bounds in several models.
Robust computation of optimal transport by $β$-potential regularization
Optimal transport (OT) has become a widely used tool in the machine learning field to measure the discrepancy between probability distributions.
Representation Learning for Continuous Action Spaces is Beneficial for Efficient Policy Learning
Meanwhile, we divide the whole learning task into learning with the large-scale representation models in an unsupervised manner and learning with the small-scale policy model in the RL manner. The small policy model facilitates policy learning, while not sacrificing generalization and expressiveness via the large representation model.
Generalized Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses
\emph{Classification with rejection} (CwR) refrains from making a prediction to avoid critical misclassification when encountering test samples that are difficult to classify.
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks
Adversarial training (AT) with imperfect supervision is significant but receives limited attention.
Equivariant Disentangled Transformation for Domain Generalization under Combination Shift
To formally analyze this issue, we provide a unique algebraic formulation of the combination shift problem based on the concepts of homomorphism, equivariance, and a refined definition of disentanglement.
Adapting to Online Label Shift with Provable Guarantees
In this paper, we formulate and investigate the problem of \emph{online label shift} (OLaS): the learner trains an initial model from the labeled offline data and then deploys it to an unlabeled online environment where the underlying label distribution changes over time but the label-conditional density does not.
Multi-class Classification from Multiple Unlabeled Datasets with Partial Risk Regularization
Recent years have witnessed a great success of supervised deep learning, where predictive models were trained from a large amount of fully labeled data.
The Survival Bandit Problem
For that purpose, we identify two key components in the survival regret: the regret given no ruin (which corresponds to the regret in the MAB), and the probability that the procedure is interrupted, called the probability of ruin.
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation
In label-noise learning, estimating the transition matrix has attracted more and more attention as the matrix plays an important role in building statistically consistent classifiers.
Excess risk analysis for epistemic uncertainty with application to variational inference
Bayesian deep learning plays an important role especially for its ability evaluating epistemic uncertainty (EU).
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 Attack and Defense for Non-Parametric Two-Sample Tests
Furthermore, we theoretically find that the adversary can also degrade the lower bound of a TST's test power, which enables us to iteratively minimize the test criterion in order to search for adversarial pairs.
Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification
In contrast to others, our method is model-free and even instance-free.
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.
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.
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.
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.
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.
Collaborate to Defend Against Adversarial Attacks
However, an ensemble still wastes the limited capacity of multiple models.
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.
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.
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: Heterogeneously Observable Imitation Learning
In many real-world imitation learning tasks, the demonstrator and the learner have to act under different observation spaces.
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 learning (PLL) utilizes instances with PLs, where a PL includes several candidate labels but only one is the true label (TL).
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 #9 on
Learning with noisy labels
on CIFAR-10N-Random3
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 #26 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: Improving Adversarial Training and Correcting Stereotype of Noisy Labels
First, we thoroughly investigate noisy labels (NLs) injection into AT's inner maximization and outer minimization, respectively and obtain the observations on when NL injection benefits AT.
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.
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data
In this paper, we demystify assumptions behind NCD and find that high-level semantic features should be shared among the seen and 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 #14 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.
On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective
Weight decay is a simple yet powerful regularization technique that has been very widely used in training of deep neural networks (DNNs).
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.
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.
Geometry-aware Instance-reweighted Adversarial Training
The belief was challenged by recent studies where we can maintain the robustness and improve the accuracy.
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.
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.
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.
$γ$-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.
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.
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.
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.
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 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.
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.
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.
Multi-agent Reinforcement Learning
Reinforcement Learning
+1
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 #18 on
Learning with noisy labels
on CIFAR-10N-Random3
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.
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.
Fast and Robust Rank Aggregation against Model Misspecification
Specifically, the properties of our CoarsenRank are summarized as follows: (1) CoarsenRank is designed for mild model misspecification, which assumes there exist the ideal preferences (consistent with model assumption) that locates in a neighborhood of the actual preferences.
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.