A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper analyzes regret bounds for unconstrained online optimization.
OGD proves robustness to Catastrophic Forgetting in Continual Learning.
SketchOGD improves memory efficiency for continual learning.
We propose a novel online learning algorithm for Restricted Boltzmann Machines (RBM), namely, the Online Generative Discriminative Restricted Boltzmann Machine (OGD-RBM), that provides the ability to build and adapt the network architecture of RBM according to the statistics of streaming data. The OGD-RBM is trained in…
Improved online learning for hidden-convex losses achieves optimal regret.
Paper analyzes convergence rates for multi-agent learning in games.
We consider online learning with linear models, where the algorithm predicts on sequentially revealed instances (feature vectors), and is compared against the best linear function (comparator) in hindsight. Popular algorithms in this framework, such as Online Gradient Descent (OGD), have parameters (learning rates), wh…
Paper tackles online adaptation to changing label distributions.
Layer-wise preconditioning methods improve neural network optimization and feature learning.
We provide an online convex optimization algorithm with regret that interpolates between the regret of an algorithm using an optimal preconditioning matrix and one using a diagonal preconditioning matrix. Our regret bound is never worse than that obtained by diagonal preconditioning, and in certain setting even surpass…
Universal preconditioning reduces sequential prediction regret.
The paper tackles CF in CL by analyzing NTK overlap matrix and proposing OGD.
TDprop uses Jacobi preconditioning to improve adaptive optimizers in Deep RL.
Optimal preconditioning improves Langevin sampling efficiency.
Preconditioned neural posterior estimation improves reliability in misspecified models.
Preconditioned gradient methods are among the most general and powerful tools in optimization. However, preconditioning requires storing and manipulating prohibitively large matrices. We describe and analyze a new structure-aware preconditioning algorithm, called Shampoo, for stochastic optimization over tensor spaces.…
In this work, we study data preconditioning, a well-known and long-existing technique, for boosting the convergence of first-order methods for regularized loss minimization. It is well understood that the condition number of the problem, i.e., the ratio of the Lipschitz constant to the strong convexity modulus, has a h…
Neural networks are achieving state of the art and sometimes super-human performance on learning tasks across a variety of domains. Whenever these problems require learning in a continual or sequential manner, however, neural networks suffer from the problem of catastrophic forgetting; they forget how to solve previous…
Bias correction improves language model training performance.
Preconditioned non-convex gradient descent improves noisy matrix estimation.
Unified framework for understanding and optimizing training acceleration.
This paper optimizes diagonal preconditioning to improve matrix condition numbers.
Two methods solve kernel ridge regression problems efficiently.
Randomized block-diagonal preconditioning improves parallel learning convergence.
PolarGrad optimizes deep learning models by considering matrix structure, outperforming Adam and Muon.
New insights link no-regret learning to online conformal prediction in adversarial settings.
New method speeds up solving orthogonality constrained problems.
In this paper, we analyze different preconditionings designed to enhance robustness of pure-pixel search algorithms, which are used for blind hyperspectral unmixing and which are equivalent to near-separable nonnegative matrix factorization algorithms. Our analysis focuses on the successive projection algorithm (SPA), …
New sampling method using regularized Wasserstein proximal for Gibbs distributions.
A new method improves convergence in low-rank approximation.
Preconditioned NFs speed up sampling from complex posterior distributions in inverse problems.
New research shows how preconditioning can solve sparse linear regression problems efficiently.
Paper develops a discounted algorithm for online convex optimization that adapts to unknown discount factors.
A new method reduces complexity of normalizing flows for MCMC preconditioning.
Adaptive learning rate algorithms such as RMSProp are widely used for training deep neural networks. RMSProp offers efficient training since it uses first order gradients to approximate Hessian-based preconditioning. However, since the first order gradients include noise caused by stochastic optimization, the approxima…
Improved convergence for overparameterized low-rank matrix sensing.
The paper explores efficient sampling for Bayesian wide neural networks.
Nonnegative matrix factorization (NMF) under the separability assumption can provably be solved efficiently, even in the presence of noise, and has been shown to be a powerful technique in document classification and hyperspectral unmixing. This problem is referred to as near-separable NMF and requires that there exist…
SignSGD analysis quantifies its effects in high dimensions.
When the design matrix has orthonormal columns, "soft thresholding" the ordinary least squares (OLS) solution produces the Lasso solution [Tibshirani, 1996]. If one uses the Puffer preconditioned Lasso [Jia and Rohe, 2012], then this result generalizes from orthonormal designs to full rank designs (Theorem 1). Theorem …
Standard gradient descent methods are susceptible to a range of issues that can impede training, such as high correlations and different scaling in parameter space.These difficulties can be addressed by second-order approaches that apply a pre-conditioning matrix to the gradient to improve convergence. Unfortunately, s…
Stochastic Gradient Langevin Dynamics infuses isotropic gradient noise to SGD to help navigate pathological curvature in the loss landscape for deep networks. Isotropic nature of the noise leads to poor scaling, and adaptive methods based on higher order curvature information such as Fisher Scoring have been proposed t…
Adaptive regularization methods pre-multiply a descent direction by a preconditioning matrix. Due to the large number of parameters of machine learning problems, full-matrix preconditioning methods are prohibitively expensive. We show how to modify full-matrix adaptive regularization in order to make it practical and e…
We propose a novel Riemannian manifold preconditioning approach for the tensor completion problem with rank constraint. A novel Riemannian metric or inner product is proposed that exploits the least-squares structure of the cost function and takes into account the structured symmetry that exists in Tucker decomposition…
In this paper we introduce a parameter dependent class of Krylov-based methods, namely CD, for the solution of symmetric linear systems. We give evidence that in our proposal we generate sequences of conjugate directions, extending some properties of the standard Conjugate Gradient (CG) method, in order to preserve the…
State-of-the-art models are now trained with billions of parameters, reaching hardware limits in terms of memory consumption. This has created a recent demand for memory-efficient optimizers. To this end, we investigate the limits and performance tradeoffs of memory-efficient adaptively preconditioned gradient methods.…
Independent Component Analysis (ICA) is a technique for unsupervised exploration of multi-channel data that is widely used in observational sciences. In its classic form, ICA relies on modeling the data as linear mixtures of non-Gaussian independent sources. The maximization of the corresponding likelihood is a challen…
Gradient-based MCMC for discrete spaces improves sampling performance.