arXiv research

Paper develops efficient algorithms for robust optimization across multiple groups.

This paper shows how path spaces on two-level manifolds can be Hilbert manifold structures.

Recently, over-parameterized neural networks have been extensively analyzed in the literature. However, the previous studies cannot satisfactorily explain why fully trained neural networks are successful in practice. In this paper, we present a new theoretical framework for analyzing over-parameterized neural networks …

This work analyzes generalization in federated learning using information theory.

As a contribution to interpretable machine learning research, we develop a novel optimization framework for learning accurate and sparse two-level Boolean rules. We consider rules in both conjunctive normal form (AND-of-ORs) and disjunctive normal form (OR-of-ANDs). A principled objective function is proposed to trade …

Fast BATLLNN speeds up verification of TLL NNs by 400x.

Develops a two-level monotonic multistage recommender system for better user-specific prediction.

Variable selection is a fundamental task in statistical data analysis. Sparsity-inducing regularization methods are a popular class of methods that simultaneously perform variable selection and model estimation. The central problem is a quadratic optimization problem with an l0-norm penalty. Exactly enforcing the l0-no…

This work improves interpretability in deep learning models by introducing a two-level concept discovery framework.

LARF improves random forests with attention mechanisms and contamination models.

New NN design for nonlinear systems control with guarantees.

In Maslov (2003), a two level model of the occurrence of financial pyramid (bubbles) has been considered. We also considered the mathematical analogy of this model to Bose condensation. In the present paper, we explain why Ponzi schemes and bubbles result in a crisis in real economics. In Maslov (2005), the law of incr…

This paper examines the valuation of American capped call options with two-level caps. The structure of the immediate exercise region is significantly more complex than in the classical case with constant cap. When the cap grows over time, making extensive use of probabilistic arguments and local time, we show that the…

Playing repeated matrix games (RMG) while maximizing the cumulative returns is a basic method to evaluate multi-agent learning (MAL) algorithms. Previous work has shown that $UCB$, $M3$, $S$ or $Exp3$ algorithms have good behaviours on average in RMG. Besides, hedging algorithms have been shown to be effective on predi…

We study the quantum synchronization between a pair of two-level systems inside two coupled cavities. By using a digital-analog decomposition of the master equation that rules the system dynamics, we show that this approach leads to quantum synchronization between both two-level systems. Moreover, we can identify in th…

Nonlinear optimal control problems are often solved with numerical methods that require knowledge of system's dynamics which may be difficult to infer, and that carry a large computational cost associated with iterative calculations. We present a novel neurobiologically inspired hierarchical learning framework, Reinfor…

A new method predicts stock ranking uncertainty to improve trading performance during regime shifts.

The stochastic variational inference (SVI) paradigm, which combines variational inference, natural gradients, and stochastic updates, was recently proposed for large-scale data analysis in conjugate Bayesian models and demonstrated to be effective in several problems. This paper studies a family of Bayesian latent vari…

Analysis of over-parameterized neural networks has drawn significant attention in recentyears. It was shown that such systems behave like convex systems under various restrictedsettings, such as for two-level neural networks, and when learning is only restricted locally inthe so-called neural tangent kernel space aroun…

To answer the existence of optimal swimmer learning/teaching strategies, this work introduces a two-level clustering in order to analyze temporal dynamics of motor learning in breaststroke swimming. Each level have been performed through Sparse Fisher-EM, a unsupervised framework which can be applied efficiently on lar…

A2SGD reduces distributed SGD communication to O(1) per worker.

The paper designs neural networks with assurance for controlling nonlinear systems.

Two new algorithms solve nonconvex-strongly concave problems efficiently.

Paper explores generalization of AID-based bi-level optimization methods.

A novel decentralized algorithm improves minimax optimization in federated learning.

We investigate the use of Minimax distances to extract in a nonparametric way the features that capture the unknown underlying patterns and structures in the data. We develop a general-purpose and computationally efficient framework to employ Minimax distances with many machine learning methods that perform on numerica…

Distillation is an effective knowledge-transfer technique that uses predicted distributions of a powerful teacher model as soft targets to train a less-parameterized student model. A pre-trained high capacity teacher, however, is not always available. Recently proposed online variants use the aggregated intermediate pr…

Specialized Deep Learning (DL) acceleration stacks, designed for a specific set of frameworks, model architectures, operators, and data types, offer the allure of high performance while sacrificing flexibility. Changes in algorithms, models, operators, or numerical systems threaten the viability of specialized hardware…

Many tasks in modern machine learning can be formulated as finding equilibria in \emph{sequential} games. In particular, two-player zero-sum sequential games, also known as minimax optimization, have received growing interest. It is tempting to apply gradient descent to solve minimax optimization given its popularity a…

This paper analyzes saddle points and minimax points in non-convex smooth games.

Polynomial-time reachability for LTI systems with TLL NN controllers is achieved.

Paper explores generalization of minimax learners, proposing a new metric.

A new method solves complex constrained minimax problems.

New algorithm solves federated minimax optimization problems.

Paper proposes an algorithm to solve complex minimax problems efficiently.

A new decentralized method solves minimax problems with reduced communication and sample complexity.

Efficient sampling reduces memory usage for Minimax distance analysis.

Study on estimating invertible functions with minimax analysis.

Minimax defense improves neural network security against gradient-based attacks.

Minimax linkage was first introduced by Ao et al. [3] in 2004, as an alternative to standard linkage methods used in hierarchical clustering. Minimax linkage relies on distances to a prototype for each cluster; this prototype can be thought of as a representative object in the cluster, hence improving the interpretabil…

New bounds on minimax regret for sequential probability assignment using logarithmic loss.

Paper proposes algorithms for solving nonconvex-nonconcave problems with complexity guarantees.

New algorithm AG-OG optimizes separable convex-concave problems efficiently.

Develops shuffling gradient-based methods for nonconvex-concave minimax optimization.

Paper improves risk bounds for nonconvex-strongly-concave minimax problems.

Optimally estimate distances on surfaces using reconstructed meshes.

Study minimax linear regression under quantile risk, improving existing bounds and providing new results.

A theorem connects integral of second-order derivatives to function rise.