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.
The paper bounds generalization error for iterative learning with bounded updates.
The paper analyzes when credal sets stabilize under iterative updates in machine learning.
We propose and analyze a new parallel coordinate descent method---`NSync---in which at each iteration a random subset of coordinates is updated, in parallel, allowing for the subsets to be chosen non-uniformly. We derive convergence rates under a strong convexity assumption, and comment on how to assign probabilities t…
Adversarial training is a technique for training robust machine learning models. To encourage robustness, it iteratively computes adversarial examples for the model, and then re-trains on these examples via some update rule. This work analyzes the performance of adversarial training on linearly separable data, and prov…
Improved ADMM for convex distributed learning with differential privacy.
Accelerates optimization in asynchronous systems with sparse updates.
We develop methods for parameter estimation in settings with large-scale data sets, where traditional methods are no longer tenable. Our methods rely on stochastic approximations, which are computationally efficient as they maintain one iterate as a parameter estimate, and successively update that iterate based on a si…
SUMER updates deployed models with new data, improving performance.
In this paper we consider the problem of minimizing a convex function using a randomized block coordinate descent method. One of the key steps at each iteration of the algorithm is determining the update to a block of variables. Existing algorithms assume that in order to compute the update, a particular subproblem is …
Iterative procedures for parameter estimation based on stochastic gradient descent allow the estimation to scale to massive data sets. However, in both theory and practice, they suffer from numerical instability. Moreover, they are statistically inefficient as estimators of the true parameter value. To address these tw…
Iterative tilting fine-tunes diffusion models for reward-tilted distributions.
New method controls sparse feature updates in deep networks.
AuON is a linear-time optimizer that improves upon Muon's performance without approximate orthogonal matrices.
In this paper, we study the randomized distributed coordinate descent algorithm with quantized updates. In the literature, the iteration complexity of the randomized distributed coordinate descent algorithm has been characterized under the assumption that machines can exchange updates with an infinite precision. We con…
In statistical learning theory, generalization error is used to quantify the degree to which a supervised machine learning algorithm may overfit to training data. Recent work [Xu and Raginsky (2017)] has established a bound on the generalization error of empirical risk minimization based on the mutual information $I(S;…
We analyze SA with Markovian data and nonlinear updates, overcoming prior limitations.
Improves inference-time alignment for diffusion models without updating weights.
We develop a functional encoder-decoder approach to supervised meta-learning, where labeled data is encoded into an infinite-dimensional functional representation rather than a finite-dimensional one. Furthermore, rather than directly producing the representation, we learn a neural update rule resembling functional gra…
We establish decoupled functional CLTs for two-time-scale stochastic approximation.
New method speeds up distributed linear regression.
Self-training improves generalization by fitting to reliable pseudo-labels or gradually improving the classification plane.
We investigate finite-time decoupled convergence in nonlinear two-time-scale stochastic approximation.
Minimizing the empirical risk is a popular training strategy, but for learning tasks where the data may be noisy or heavy-tailed, one may require many observations in order to generalize well. To achieve better performance under less stringent requirements, we introduce a procedure which constructs a robust approximati…
Paper achieves sample complexity for actor-critic methods with minimal assumptions.
Paper proves SHB convergence with biased gradients and approximate step sizes.
IDS improves RLHF by smoothing reward data, enhancing model performance.
A technique identifies memoryless algorithms approximating memory-dependent optimization methods.
Paper proves CLTs for Q-learning with asynchronous updates.
The stochastic gradient (SG) method can minimize an objective function composed of a large number of differentiable functions, or solve a stochastic optimization problem, to a moderate accuracy. The block coordinate descent/update (BCD) method, on the other hand, handles problems with multiple blocks of variables by up…
U-Clip clips gradients to reduce bias without discarding them.
Paper analyzes iterative learning for concept classes and learns half-spaces.
We present a unifying framework for adapting the update direction in gradient-based iterative optimization methods. As natural special cases we re-derive classical momentum and Nesterov's accelerated gradient method, lending a new intuitive interpretation to the latter algorithm. We show that a new algorithm, which we …
Efficient CD algorithms on matrix manifolds for optimization problems.
We propose a general formalism of iterated random functions with semigroup property, under which exact and approximate Bayesian posterior updates can be viewed as specific instances. A convergence theory for iterated random functions is presented. As an application of the general theory we analyze convergence behaviors…
The paper analyzes generalization of noisy, iterative algorithms using maximal leakage.
Accelerates policy optimization in RL with optimistic and adaptive updates.
We investigate the framework of privacy amplification by iteration, recently proposed by Feldman et al., from an information-theoretic lens. We demonstrate that differential privacy guarantees of iterative mappings can be determined by a direct application of contraction coefficients derived from strong data processing…
Recently, a novel class of Approximate Policy Iteration (API) algorithms have demonstrated impressive practical performance (e.g., ExIt from [2], AlphaGo-Zero from [27]). This new family of algorithms maintains, and alternately optimizes, two policies: a fast, reactive policy (e.g., a deep neural network) deployed at t…
Motivated by applications in Game Theory, Optimization, and Generative Adversarial Networks, recent work of Daskalakis et al \cite{DISZ17} and follow-up work of Liang and Stokes \cite{LiangS18} have established that a variant of the widely used Gradient Descent/Ascent procedure, called "Optimistic Gradient Descent/Asce…
LF-IBIS learns optimal policies online without explicit likelihood.
Approximate Message Passing (AMP) has been shown to be a superior method for inference problems, such as the recovery of signals from sets of noisy, lower-dimensionality measurements, both in terms of reconstruction accuracy and in computational efficiency. However, AMP suffers from serious convergence issues in contex…
The study provides statistical guarantees for Bayesian variational boosting.
New method prevents RLHF alignment collapse by accounting for policy's influence on reward model updates.
Single-timescale actor-critic finds globally optimal policy.
Developed efficient distributed logistic regression for large datasets.
A new approach RA improves stochastic optimization by executing multiple steps between subsample updates.
The question of how to parallelize the stochastic gradient descent (SGD) method has received much attention in the literature. In this paper, we focus instead on batch methods that use a sizeable fraction of the training set at each iteration to facilitate parallelism, and that employ second-order information. In order…
Byzantine-resilient federated learning with local iterations and robust mean estimation.