arXiv research

FAST improves fast and stable task adaptation in DNNs.

Paper investigates robustness to interference as a new training signal for meta-learning.

Sparse Meta Networks adapt deep neural networks incrementally for fast learning.

FSNet improves online time series forecasting by balancing fast adaptation and old knowledge.

Efficient CF approach using fast adaptive PCA for recommender systems.

FAWMF adapts weights for implicit feedback recommendation efficiently.

A concise review of recent few-shot meta-learning methods.

MAML adapts faster with deeper architectures, especially in shallow tasks.

This paper proposes a new method for estimating sparse precision matrices in the high dimensional setting. It has been popular to study fast computation and adaptive procedures for this problem. We propose a novel approach, called Sparse Column-wise Inverse Operator, to address these two issues. We analyze an adaptive …

Paper presents a fast and adaptive filter for SI suppression in full-duplex transceivers.

M-L2O adapts fast to new tasks by self-adapting during test-time.

A fast method estimates group-adaptive elastic net penalties using co-data.

Fast algorithms developed for adaptive and fully adaptive submodular maximization problems.

Error bound conditions (EBC) are properties that characterize the growth of an objective function when a point is moved away from the optimal set. They have recently received increasing attention in the field of optimization for developing optimization algorithms with fast convergence. However, the studies of EBC in st…

MetaVRF learns adaptive kernels for fast few-shot learning.

Adaptive gradient methods, which adopt historical gradient information to automatically adjust the learning rate, despite the nice property of fast convergence, have been observed to generalize worse than stochastic gradient descent (SGD) with momentum in training deep neural networks. This leaves how to close the gene…

In this work, we are concerned with the spherical quasiconformal parameterization of genus-0 closed surfaces. Given a genus-0 closed triangulated surface and an arbitrary user-defined quasiconformal distortion, we propose a fast algorithm for computing a spherical parameterization of the surface that satisfies the pres…

Novel Bayesian meta-reinforcement learning framework improves traffic signal control robustness.

Deep neural networks have excelled on a wide range of problems, from vision to language and game playing. Neural networks very gradually incorporate information into weights as they process data, requiring very low learning rates. If the training distribution shifts, the network is slow to adapt, and when it does adapt…

New algorithm reduces best-in-class regret in contextual bandits.

In this paper we describe a new algorithm called Fast Adaptive Sequencing Technique (FAST) for maximizing a monotone submodular function under a cardinality constraint $k$ whose approximation ratio is arbitrarily close to $1-1/e$, is $O(\log(n) \log^2(\log k))$ adaptive, and uses a total of $O(n \log\log(k))$ queries. …

AdaX improves Adam by exponentially accumulating past gradients, leading to better performance in machine learning tasks.

A new method improves EEG classification across subjects efficiently.

In this letter, we propose an algorithm for recovery of sparse and low rank components of matrices using an iterative method with adaptive thresholding. In each iteration, the low rank and sparse components are obtained using a thresholding operator. This algorithm is fast and can be implemented easily. We compare it w…

Adaptive workflow combines fast amortized inference with MCMC for many datasets.

The high-order relations between the content in social media sharing platforms are frequently modeled by a hypergraph. Either hypergraph Laplacian matrix or the adjacency matrix is a big matrix. Randomized algorithms are used for low-rank factorizations in order to approximately decompose and eventually invert such big…

A new algorithm reduces the time for ordinal embedding, making it faster and more scalable.

New method learns robust meta-representations for fast task adaptation.

New guarantees for ERM with adaptively collected data.

Linearized neural networks provide a fast and interpretable way to adapt models to new settings.

Improved meta-learning framework using Runge-Kutta methods.

CoDA adapts dynamics models to new physical systems by conditioning on context.

This paper addresses classification tasks on a particular target domain in which labeled training data are only available from source domains different from (but related to) the target. Two closely related frameworks, domain adaptation and domain generalization, are concerned with such tasks, where the only difference …

In this paper, we consider the problem of fast and efficient indexing techniques for sequences evolving in non-Euclidean spaces. This problem has several applications in the areas of human activity analysis, where there is a need to perform fast search, and recognition in very high dimensional spaces. The problem is ma…

Improved RL algorithm stabilizes unknown linear systems with polynomial regret.

Adapts DPMM for fast streaming data clustering.

FLAML automates model selection and hyperparameter tuning with low resource cost.

Stochastic methods with coordinate-wise adaptive stepsize (such as RMSprop and Adam) have been widely used in training deep neural networks. Despite their fast convergence, they can generalize worse than stochastic gradient descent. In this paper, by revisiting the design of Adagrad, we propose to split the network par…

In most adaptive signal processing applications, system linearity is assumed and adaptive linear filters are thus used. The traditional class of supervised adaptive filters rely on error-correction learning for their adaptive capability. The kernel method is a powerful nonparametric modeling tool for pattern analysis a…

We consider first order gradient methods for effectively optimizing a composite objective in the form of a sum of smooth and, potentially, non-smooth functions. We present accelerated and adaptive gradient methods, called FLAG and FLARE, which can offer the best of both worlds. They can achieve the optimal convergence …

PDTS improves robustness in sequential decision-making.

FASE-AL uses active learning to reduce labeling costs for data streams.

AdaBelief optimizes deep learning models with faster convergence and better stability.

We present an approach towards convex optimization that relies on a novel scheme which converts online adaptive algorithms into offline methods. In the offline optimization setting, our derived methods are shown to obtain favourable adaptive guarantees which depend on the harmonic sum of the queried gradients. We furth…

Flexible empirical Bayes for large-scale multiple linear regression.

Meta-learning bounds derived using PAC-Bayes theory for improved generalization.

New approach shows AI can adapt like toddlers by correcting old knowledge.

During recent years there has been an increased interest in stochastic adaptations of limited memory quasi-Newton methods, which compared to pure gradient-based routines can improve the convergence by incorporating second order information. In this work we propose a direct least-squares approach conceptually similar to…