Search Results for author:
Found 42 papers, 10 papers with code
Pairwise Instance Relation Augmentation for Long-tailed Multi-label Text Classification
In response, we propose a Pairwise Instance Relation Augmentation Network (PIRAN) to augment tailed-label documents for balancing tail labels and head labels.
Multi Label Text Classification
Multi-Label Text Classification
+2
Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies
We consider infinite-horizon discounted Markov decision processes and study the convergence rates of the natural policy gradient (NPG) and the Q-NPG methods with the log-linear policy class.
Faster Last-iterate Convergence of Policy Optimization in Zero-Sum Markov Games
Multi-Agent Reinforcement Learning (MARL) -- where multiple agents learn to interact in a shared dynamic environment -- permeates across a wide range of critical applications.
Grad-GradaGrad? A Non-Monotone Adaptive Stochastic Gradient Method
The classical AdaGrad method adapts the learning rate by dividing by the square root of a sum of squared gradients.
BiT: Robustly Binarized Multi-distilled Transformer
Modern pre-trained transformers have rapidly advanced the state-of-the-art in machine learning, but have also grown in parameters and computational complexity, making them increasingly difficult to deploy in resource-constrained environments.
On Continual Model Refinement in Out-of-Distribution Data Streams
Real-world natural language processing (NLP) models need to be continually updated to fix the prediction errors in out-of-distribution (OOD) data streams while overcoming catastrophic forgetting.
FedShuffle: Recipes for Better Use of Local Work in Federated Learning
The practice of applying several local updates before aggregation across clients has been empirically shown to be a successful approach to overcoming the communication bottleneck in Federated Learning (FL).
Federated Learning with Partial Model Personalization
We consider two federated learning algorithms for training partially personalized models, where the shared and personal parameters are updated either simultaneously or alternately on the devices.
On the Convergence Rates of Policy Gradient Methods
We consider infinite-horizon discounted Markov decision problems with finite state and action spaces and study the convergence rates of the projected policy gradient method and a general class of policy mirror descent methods, all with direct parametrization in the policy space.
Importance Estimation from Multiple Perspectives for Keyphrase Extraction
Keyphrase extraction is a fundamental task in Natural Language Processing, which usually contains two main parts: candidate keyphrase extraction and keyphrase importance estimation.
Cross-Platform Simulation Architecture with application to truck platooning impact assessment
Simulation-based traffic impact assessment studies of advanced technologies such as truck platooning need to be carried out to ascertain their benefits for traffic efficiency, safety and environment.
Does Head Label Help for Long-Tailed Multi-Label Text Classification
To address the challenge of insufficient training data on tail label classification, we propose a Head-to-Tail Network (HTTN) to transfer the meta-knowledge from the data-rich head labels to data-poor tail labels.
Improving Self-supervised Pre-training via a Fully-Explored Masked Language Model
We prove, from a theoretical perspective, that the gradients derived from this new masking schema have a smaller variance and can lead to more efficient self-supervised training.
Ranked #1 on
Sentence Classification
on ACL-ARC
Hyperbolic Capsule Networks for Multi-Label Classification
Second, Hyperbolic Dynamic Routing (HDR) is introduced to aggregate hyperbolic capsules in a label-aware manner, so that the label-level discriminative information can be preserved along the depth of neural networks.
Jointly Modeling Intra- and Inter-transaction Dependencies with Hierarchical Attentive Transaction Embeddings for Next-item Recommendation
Most existing TBRSs recommend next item by only modeling the intra-transaction dependency within the current transaction while ignoring inter-transaction dependency with recent transactions that may also affect the next item.
Statistical Adaptive Stochastic Gradient Methods
We propose a statistical adaptive procedure called SALSA for automatically scheduling the learning rate (step size) in stochastic gradient methods.
Label-Specific Document Representation for Multi-Label Text Classification
Multi-label text classification (MLTC) aims to tag most relevant labels for the given document.
Ranked #1 on
Multi-Label Text Classification
on AAPD
Understanding the Role of Momentum in Stochastic Gradient Methods
The use of momentum in stochastic gradient methods has become a widespread practice in machine learning.
Statistical Adaptive Stochastic Optimization
We investigate statistical methods for automatically scheduling the learning rate (step size) in stochastic optimization.
Using Statistics to Automate Stochastic Optimization
Despite the development of numerous adaptive optimizers, tuning the learning rate of stochastic gradient methods remains a major roadblock to obtaining good practical performance in machine learning.
Multi-Level Composite Stochastic Optimization via Nested Variance Reduction
We consider multi-level composite optimization problems where each mapping in the composition is the expectation over a family of random smooth mappings or the sum of some finite number of smooth mappings.
From low probability to high confidence in stochastic convex optimization
Standard results in stochastic convex optimization bound the number of samples that an algorithm needs to generate a point with small function value in expectation.
A Stochastic Composite Gradient Method with Incremental Variance Reduction
We show that this method achieves the same orders of complexity as the best known first-order methods for minimizing expected-value and finite-sum nonconvex functions, despite the additional outer composition which renders the composite gradient estimator biased.
Hyperbolic Interaction Model For Hierarchical Multi-Label Classification
The main reason is that the tree-likeness of the hyperbolic space matches the complexity of symbolic data with hierarchical structures.
Label-aware Document Representation via Hybrid Attention for Extreme Multi-Label Text Classification
Extreme multi-label text classification (XMTC) aims at tagging a document with most relevant labels from an extremely large-scale label set.
Ranked #1 on
Multi-Label Text Classification
on Amazon-12K
Coupled Variational Bayes via Optimization Embedding
This flexible function class couples the variational distribution with the original parameters in the graphical models, allowing end-to-end learning of the graphical models by back-propagation through the variational distribution.
Learning SMaLL Predictors
We present a new machine learning technique for training small resource-constrained predictors.
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
When function approximation is used, solving the Bellman optimality equation with stability guarantees has remained a major open problem in reinforcement learning for decades.
Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes
In sequential decision making, it is often important and useful for end users to understand the underlying patterns or causes that lead to the corresponding decisions.
DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization
Machine learning with big data often involves large optimization models.
Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms
We consider empirical risk minimization of linear predictors with convex loss functions.
Stochastic Variance Reduction Methods for Policy Evaluation
Policy evaluation is a crucial step in many reinforcement-learning procedures, which estimates a value function that predicts states' long-term value under a given policy.
End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture
We develop a fully discriminative learning approach for supervised Latent Dirichlet Allocation (LDA) model using Back Propagation (i. e., BP-sLDA), which maximizes the posterior probability of the prediction variable given the input document.
Variational Gram Functions: Convex Analysis and Optimization
We propose a new class of convex penalty functions, called \emph{variational Gram functions} (VGFs), that can promote pairwise relations, such as orthogonality, among a set of vectors in a vector space.
Communication-Efficient Distributed Optimization of Self-Concordant Empirical Loss
We consider distributed convex optimization problems originated from sample average approximation of stochastic optimization, or empirical risk minimization in machine learning.
An Accelerated Proximal Coordinate Gradient Method
We develop an accelerated randomized proximal coordinate gradient (APCG) method, for solving a broad class of composite convex optimization problems.
Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization
We consider a generic convex optimization problem associated with regularized empirical risk minimization of linear predictors.
A Proximal Stochastic Gradient Method with Progressive Variance Reduction
We consider the problem of minimizing the sum of two convex functions: one is the average of a large number of smooth component functions, and the other is a general convex function that admits a simple proximal mapping.
Online Classification Using a Voted RDA Method
We propose a voted dual averaging method for online classification problems with explicit regularization.
A Randomized Nonmonotone Block Proximal Gradient Method for a Class of Structured Nonlinear Programming
When the problem under consideration is convex, we show that the expected objective values generated by RNBPG converge to the optimal value of the problem.
On the Complexity Analysis of Randomized Block-Coordinate Descent Methods
In this paper we analyze the randomized block-coordinate descent (RBCD) methods proposed in [8, 11] for minimizing the sum of a smooth convex function and a block-separable convex function.
Dual Averaging Method for Regularized Stochastic Learning and Online Optimization
We consider regularized stochastic learning and online optimization problems, where the objective function is the sum of two convex terms: one is the loss function of the learning task, and the other is a simple regularization term such as L1-norm for sparsity.
