arXiv research

Proposes methods to recover labels from shuffled networks using graph averages.

FedMA improves federated learning for neural nets, matching and averaging model elements.

ScoreMatchingRiesz improves debiased machine learning and policy effects estimation.

In this paper, we propose deep learning techniques for econometrics, specifically for causal inference and for estimating individual as well as average treatment effects. The contribution of this paper is twofold: 1. For generalized neighbor matching to estimate individual and average treatment effects, we analyze the …

Gaussian and bootstrap methods improve ATE estimator accuracy.

Matched Machine Learning combines machine learning and matching for causal inference.

We propose and analyze a variant of the classic Polyak-Ruppert averaging scheme, broadly used in stochastic gradient methods. Rather than a uniform average of the iterates, we consider a weighted average, with weights decaying in a geometric fashion. In the context of linear least squares regression, we show that this …

Develops a framework for clustering and distribution matching with bandit feedback.

In this paper, a standard PDE for the pricing of arithmetic average strike Asian call option is presented. A Crank-Nicolson Implicit Method and a Higher Order Compact finite difference scheme for this pricing problem is derived. Both these schemes were implemented for various values of risk free rate and volatility. Th…

Efficiently matches random graphs with inhomogeneous edge probabilities.

Sharp bounds established for Federated Averaging (FedAvg), improving convergence rates.

In causal inference, a variety of causal effect estimands have been studied, including the sample, uncensored, target, conditional, optimal subpopulation, and optimal weighted average treatment effects. Ad-hoc methods have been developed for each estimand based on inverse probability weighting (IPW) and on outcome regr…

A classical problem in causal inference is that of matching, where treatment units need to be matched to control units based on covariate information. In this work, we propose a method that computes high quality almost-exact matches for high-dimensional categorical datasets. This method, called FLAME (Fast Large-scale …

Estimates returns for dollar cost averaging using geometric Brownian motion.

New stability analysis improves generalization of multipass SGD.

FAST selects coresets more efficiently by matching distributions in the frequency domain.

This paper proposes a simple but effective graph-based agglomerative algorithm, for clustering high-dimensional data. We explore the different roles of two fundamental concepts in graph theory, indegree and outdegree, in the context of clustering. The average indegree reflects the density near a sample, and the average…

Tennis is a popular sport worldwide, boasting millions of fans and numerous national and international tournaments. Like many sports, tennis has benefitted from the popularity of rigorous record-keeping of game and player information, as well as the growth of machine learning methods for use in sports analytics. Of par…

Paper proposes a new method for training diffusion models using Markov operators.

SGD in linear regression overfits but performs well due to bias-variance trade-off.

Low precision operations can provide scalability, memory savings, portability, and energy efficiency. This paper proposes SWALP, an approach to low precision training that averages low-precision SGD iterates with a modified learning rate schedule. SWALP is easy to implement and can match the performance of full-precisi…

Orthogonal Matching Pursuit (OMP) has long been considered a powerful heuristic for attacking compressive sensing problems; however, its theoretical development is, unfortunately, somewhat lacking. This paper presents an improved Restricted Isometry Property (RIP) based performance guarantee for T-sparse signal reconst…

In this paper, we study the information-theoretic limits of community detection in the symmetric two-community stochastic block model, with intra-community and inter-community edge probabilities $\frac{a}{n}$ and $\frac{b}{n}$ respectively. We consider the sparse setting, in which $a$ and $b$ do not scale with $n$, and…

New bounds for agnostic learning with average smoothness.

Study Q-learning with averaging for reinforcement learning, proving efficient inference and error bounds.

GPA improves LLM training speed by 8.71% for Llama-160M models.

A higher-order Runge-Kutta optimizer performs poorly compared to Adam when evaluated fairly.

Model-based reinforcement learning (MBRL) aims to learn a dynamic model to reduce the number of interactions with real-world environments. However, due to estimation error, rollouts in the learned model, especially those of long horizons, fail to match the ones in real-world environments. This mismatching has seriously…

A new model-free algorithm achieves near-optimal regret for infinite-horizon MDPs.

In many statistical problems, a more coarse-grained model may be suitable for population-level behaviour, whereas a more detailed model is appropriate for accurate modelling of individual behaviour. This raises the question of how to integrate both types of models. Methods such as posterior regularization follow the id…

We analyze a new spectral graph matching algorithm, GRAph Matching by Pairwise eigen-Alignments (GRAMPA), for recovering the latent vertex correspondence between two unlabeled, edge-correlated weighted graphs. Extending the exact recovery guarantees established in the companion paper for Gaussian weights, in this work,…

Flow matching improves synthetic data generation for tabular data.

Unified theory for causal inference using various methods.

Paper solves graph matching for correlated Erdős--Rényi graphs.

New matching estimators correct bias in multivariate settings without smoothing parameters.

Stochastic Gradient Descent (SGD) is one of the simplest and most popular stochastic optimization methods. While it has already been theoretically studied for decades, the classical analysis usually required non-trivial smoothness assumptions, which do not apply to many modern applications of SGD with non-smooth object…

Efficient algorithm for graph matching in correlated stochastic block models.

Study shows Bergman kernels match averages on quotient spaces, proving non-vanishing of Poincaré series.

STORM-PG uses momentum for faster policy gradient updates.

New averaging technique speeds up Newton method convergence.

UCRL2-VTR achieves nearly optimal regret for learning MDPs with linear function approximation.

New algorithm reduces sample complexity for constrained MDPs.

TRACE improves conformal prediction for multi-dimensional outputs.

In many high-dimensional estimation problems the main task consists in minimizing a cost function, which is often strongly non-convex when scanned in the space of parameters to be estimated. A standard solution to flatten the corresponding rough landscape consists in summing the losses associated to different data poin…

New algorithm learns optimal policy for average reward MDPs with sample complexity matching lower bound.

The performance of Orthogonal Matching Pursuit (OMP) for variable selection is analyzed for random designs. When contrasted with the deterministic case, since the performance is here measured after averaging over the distribution of the design matrix, one can have far less stringent sparsity constraints on the coeffici…

Proposes a new method to estimate continuous treatment policies and match treatments effectively.

Enhances community detection in correlated networks with node attributes.