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Found 58 papers, 20 papers with code
EventKE: Event-Enhanced Knowledge Graph Embedding
Relations in most of the traditional knowledge graphs (KGs) only reflect static and factual connections, but fail to represent the dynamic activities and state changes about entities.
DD-TIG at Constraint@ACL2022: Multimodal Understanding and Reasoning for Role Labeling of Entities in Hateful Memes
The memes serve as an important tool in online communication, whereas some hateful memes endanger cyberspace by attacking certain people or subjects.
Algorithms and Theory for Supervised Gradual Domain Adaptation
We thus study the problem of supervised gradual domain adaptation, where labeled data from shifting distributions are available to the learner along the trajectory, and we aim to learn a classifier on a target data distribution of interest.
Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond
Gradual domain adaptation (GDA), on the other hand, assumes a path of $(T-1)$ unlabeled intermediate domains bridging the source and target, and aims to provide better generalization in the target domain by leveraging the intermediate ones.
Model-Agnostic Multitask Fine-tuning for Few-shot Vision-Language Transfer Learning
Due to the uniform task sampling procedure, MAMF consistently outperforms the classical fine-tuning method for few-shot transfer learning on five benchmark datasets.
Conditional Contrastive Learning with Kernel
Conditional contrastive learning frameworks consider the conditional sampling procedure that constructs positive or negative data pairs conditioned on specific variables.
Provable Domain Generalization via Invariant-Feature Subspace Recovery
Our first algorithm, ISR-Mean, can identify the subspace spanned by invariant features from the first-order moments of the class-conditional distributions, and achieve provable domain generalization with $d_s+1$ training environments under the data model of Rosenfeld et al. (2021).
Model-free Nearly Optimal Control of Constrained-Input Nonlinear Systems Based on Synchronous Reinforcement Learning
In this paper a novel model-free algorithm is proposed.
Towards Return Parity in Markov Decision Processes
We first provide a decomposition theorem for return disparity, which decomposes the return disparity of any two MDPs sharing the same state and action spaces into the distance between group-wise reward functions, the discrepancy of group policies, and the discrepancy between state visitation distributions induced by the group policies.
A Nearly Optimal Chattering Reduction Method of Sliding Mode Control With an Application to a Two-wheeled Mobile Robot
First, the deficiency of chattering in traditional SMC and the quasi-SMC method are analyzed in this paper.
FedMM: Saddle Point Optimization for Federated Adversarial Domain Adaptation
Federated adversary domain adaptation is a unique distributed minimax training task due to the prevalence of label imbalance among clients, with each client only seeing a subset of the classes of labels required to train a global model.
Learning Invariant Representations on Multilingual Language Models for Unsupervised Cross-Lingual Transfer
Recent advances in neural modeling have produced deep multilingual language models capable of extracting cross-lingual knowledge from unparallel texts, as evidenced by their decent zero-shot transfer performance.
Robust and Personalized Federated Learning with Spurious Features: an Adversarial Approach
To this end, we propose a strategy to mitigate the effect of spurious features based on our observation that the global model in the federated learning step has a low accuracy disparity due to statistical heterogeneity.
Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation
Despite the subtle difference between MTL and meta-learning in the problem formulation, both learning paradigms share the same insight that the shared structure between existing training tasks could lead to better generalization and adaptation.
Ranked #13 on
Few-Shot Image Classification
on FC100 5-way (1-shot)
Costs and Benefits of Fair Regression
In this paper, we characterize the inherent tradeoff between statistical parity and accuracy in the regression setting by providing a lower bound on the error of any fair regressor.
Invariant Information Bottleneck for Domain Generalization
IIB significantly outperforms IRM on synthetic datasets, where the pseudo-invariant features and geometric skews occur, showing the effectiveness of proposed formulation in overcoming failure modes of IRM.
Online Continual Adaptation with Active Self-Training
Our theoretical results show that OSAMD can fast adapt to changing environments with active queries.
Quantifying and Improving Transferability in Domain Generalization
We then prove that our transferability can be estimated with enough samples and give a new upper bound for the target error based on our transferability.
Conditional Contrastive Learning: Removing Undesirable Information in Self-Supervised Representations
However, data sometimes contains certain information that may be undesirable for downstream tasks.
Online Adaptive Optimal Control Algorithm Based on Synchronous Integral Reinforcement Learning With Explorations
In this paper, we present a novel algorithm named synchronous integral Q-learning, which is based on synchronous policy iteration, to solve the continuous-time infinite horizon optimal control problems of input-affine system dynamics.
Adaptive Fractional-Order Sliding Mode Controller with Neural Network Compensator for an Ultrasonic Motor
Ultrasonic motors (USMs) are commonly used in aerospace, robotics, and medical devices, where fast and precise motion is needed.
Self-supervised Representation Learning with Relative Predictive Coding
This paper introduces Relative Predictive Coding (RPC), a new contrastive representation learning objective that maintains a good balance among training stability, minibatch size sensitivity, and downstream task performance.
Understanding and Mitigating Accuracy Disparity in Regression
With the widespread deployment of large-scale prediction systems in high-stakes domains, e. g., face recognition, criminal justice, etc., disparity in prediction accuracy between different demographic subgroups has called for fundamental understanding on the source of such disparity and algorithmic intervention to mitigate it.
Acoustic Structure Inverse Design and Optimization Using Deep Learning
From ancient to modern times, acoustic structures have been used to control the propagation of acoustic waves.
On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections
Disparate impact has raised serious concerns in machine learning applications and its societal impacts.
Fundamental Limits and Tradeoffs in Invariant Representation Learning
A wide range of machine learning applications such as privacy-preserving learning, algorithmic fairness, and domain adaptation/generalization among others, involve learning \emph{invariant representations} of the data that aim to achieve two competing goals: (a) maximize information or accuracy with respect to a target response, and (b) maximize invariance or independence with respect to a set of protected features (e. g.\ for fairness, privacy, etc).
Model-based Policy Optimization with Unsupervised Model Adaptation
However, due to the potential distribution mismatch between simulated data and real data, this could lead to degraded performance.
Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation
First, we provide a finite sample bound for both classification and regression problems under Semi-DA.
Information Obfuscation of Graph Neural Networks
While the advent of Graph Neural Networks (GNNs) has greatly improved node and graph representation learning in many applications, the neighborhood aggregation scheme exposes additional vulnerabilities to adversaries seeking to extract node-level information about sensitive attributes.
DynamicVAE: Decoupling Reconstruction Error and Disentangled Representation Learning
Existing methods, such as $\beta$-VAE and FactorVAE, assign a large weight to the KL-divergence term in the objective function, leading to high reconstruction errors for the sake of better disentanglement.
A Review of Single-Source Deep Unsupervised Visual Domain Adaptation
To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled or unlabeled target domain.
On Learning Language-Invariant Representations for Universal Machine Translation
The goal of universal machine translation is to learn to translate between any pair of languages, given a corpus of paired translated documents for \emph{a small subset} of all pairs of languages.
Neural Methods for Point-wise Dependency Estimation
Since its inception, the neural estimation of mutual information (MI) has demonstrated the empirical success of modeling expected dependency between high-dimensional random variables.
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift
However, recent work has shown limitations of this approach when label distributions differ between the source and target domains.
Continual Learning with Adaptive Weights (CLAW)
Approaches to continual learning aim to successfully learn a set of related tasks that arrive in an online manner.
Conditional Learning of Fair Representations
We propose a novel algorithm for learning fair representations that can simultaneously mitigate two notions of disparity among different demographic subgroups in the classification setting.
Adversarial Privacy Preservation under Attribute Inference Attack
With the prevalence of machine learning services, crowdsourced data containing sensitive information poses substantial privacy challenges.
Learning Neural Networks with Adaptive Regularization
Feed-forward neural networks can be understood as a combination of an intermediate representation and a linear hypothesis.
Inherent Tradeoffs in Learning Fair Representations
On the upside, we prove that if the group-wise Bayes optimal classifiers are close, then learning fair representations leads to an alternative notion of fairness, known as the accuracy parity, which states that the error rates are close between groups.
Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation
Meanwhile, it is clear that in general there is a tension between minimizing information leakage and maximizing task accuracy.
Learning From the Experience of Others: Approximate Empirical Bayes in Neural Networks
Learning deep neural networks could be understood as the combination of representation learning and learning halfspaces.
On Learning Invariant Representation for Domain Adaptation
Our result characterizes a fundamental tradeoff between learning invariant representations and achieving small joint error on both domains when the marginal label distributions differ from source to target.
Adversarial Multiple Source Domain Adaptation
In this paper we propose new generalization bounds and algorithms under both classification and regression settings for unsupervised multiple source domain adaptation.
On Strategyproof Conference Peer Review
We then empirically show that the requisite property on the authorship graph is indeed satisfied in the submission data from the ICLR conference, and further demonstrate a simple trick to make the partitioning method more practically appealing for conference peer review.
Convolutional-Recurrent Neural Networks for Speech Enhancement
By incorporating prior knowledge of speech signals into the design of model structures, we build a model that is more data-efficient and achieves better generalization on both seen and unseen noise.
Active Learning of Strict Partial Orders: A Case Study on Concept Prerequisite Relations
Strict partial order is a mathematical structure commonly seen in relational data.
Multiple Source Domain Adaptation with Adversarial Learning
We propose a new generalization bound for domain adaptation when there are multiple source domains with labeled instances and one target domain with unlabeled instances.
Discovering Order in Unordered Datasets: Generative Markov Networks
The assumption that data samples are independently identically distributed is the backbone of many learning algorithms.
Learning Markov Chain in Unordered Dataset
In this technical report, we introduce OrderNet that can be used to extract the order of data instances in an unsupervised way.
Frank-Wolfe Optimization for Symmetric-NMF under Simplicial Constraint
Symmetric nonnegative matrix factorization has found abundant applications in various domains by providing a symmetric low-rank decomposition of nonnegative matrices.
Multiple Source Domain Adaptation with Adversarial Training of Neural Networks
As a step toward bridging the gap, we propose a new generalization bound for domain adaptation when there are multiple source domains with labeled instances and one target domain with unlabeled instances.
Principled Hybrids of Generative and Discriminative Domain Adaptation
This provides us a very general way to interpolate between generative and discriminative extremes through different choices of priors.
Linear Time Computation of Moments in Sum-Product Networks
We propose a dynamic programming method to further reduce the computation of the moments of all the edges in the graph from quadratic to linear.
Efficient Multitask Feature and Relationship Learning
In this paper, we consider a formulation of multitask learning that learns the relationships both between tasks and between features, represented through a task covariance and a feature covariance matrix, respectively.
A Unified Approach for Learning the Parameters of Sum-Product Networks
We present a unified approach for learning the parameters of Sum-Product networks (SPNs).
Self-Adaptive Hierarchical Sentence Model
The ability to accurately model a sentence at varying stages (e. g., word-phrase-sentence) plays a central role in natural language processing.
Ranked #5 on
Subjectivity Analysis
on SUBJ
On the Relationship between Sum-Product Networks and Bayesian Networks
We conclude the paper with some discussion of the implications of the proof and establish a connection between the depth of an SPN and a lower bound of the tree-width of its corresponding BN.
A Sober Look at Spectral Learning
In contrast, maximum likelihood estimates may get trapped in local optima due to the non-convex nature of the likelihood function of latent variable models.
