arXiv research

Study normal curves in sub-Finsler Lie groups with specific norms, focusing on branching and face stability.

This paper analyzes the stability and generalization of triplet learning algorithms.

We introduce a new notion of the stability of computations, which holds under post-processing and adaptive composition. We show that the notion is both necessary and sufficient to ensure generalization in the face of adaptivity, for any computations that respond to bounded-sensitivity linear queries while providing acc…

Recent work shows unequal performance of commercial face classification services in the gender classification task across intersectional groups defined by skin type and gender. Accuracy on dark-skinned females is significantly worse than on any other group. In this paper, we conduct several analyses to try to uncover t…

Efficient algorithm for unknown linear systems with convex costs.

This paper empirically analyses risk in the Euro relative to other currencies. Comparisons are made between a sub period encompassing the final transitional stage to full monetary union with a sub period prior to this. Stability in the face of speculative attack is examined using Extreme Value Theory to obtain estimate…

Emerging economies use countercyclical policies to manage crises and dominant currencies.

Feature Quantization improves GAN training stability.

New method stabilizes saddle-point optimization with unbounded gradients.

This paper introduces a method to improve GNN stability and robustness.

This paper analyzes HTL using stability theory for binary classification.

We introduce the hemicubic codes, a family of quantum codes obtained by associating qubits with the $p$-faces of the $n$-cube (for $n>p$) and stabilizer constraints with faces of dimension $(p\pm1)$. The quantum code obtained by identifying antipodal faces of the resulting complex encodes one logical qubit into $N = 2^…

DHLNN improves deep hedging for financial derivatives with faster convergence and better stability.

The seriousness of the current crisis urgently demands new economic thinking that breaks the austerity vs. deficit spending circle in economic policy. The core tenet of the paper is that the most important problems that natural and social science are facing today are inverse problems, and that a new approach that goes …

This paper introduces Libra to analyze and optimize generalization in Federated Learning.

MARL improves LBM stability and accuracy across scales.

Proposes AEGAN for stable GAN training.

We develop a model of stable assets, including non-custodial stablecoins backed by cryptocurrencies. Such stablecoins are popular methods for bootstrapping price stability within public blockchain settings. We derive fundamental results about dynamics and liquidity in stablecoin markets, demonstrate that these markets …

Efficient algorithm reduces control system regret to sqrt(t).

The performance of fully synchronized distributed systems has faced a bottleneck due to the big data trend, under which asynchronous distributed systems are becoming a major popularity due to their powerful scalability. In this paper, we study the generalization performance of stochastic gradient descent (SGD) on a dis…

Enhanced neural network framework improves constraint satisfaction with topological conditioning.

Study how firm liquidation regimes affect shareholder value and stability.

Paper introduces Influence Function to assess OOD generalization stability.

Optimizes master faces for 2D and 3D face verification using evolutionary algorithms and neural networks.

While biomanufacturing plays a significant role in supporting the economy and ensuring public health, it faces critical challenges, including complexity, high variability, lengthy lead time, and very limited process data, especially for personalized new cell and gene biotherapeutics. Driven by these challenges, we prop…

Face recognition systems are vulnerable to composite face reconstruction attacks.

Sparse attention model reduces long-context inference time with exponential accuracy guarantees.

SAIL improves AIL by weighting adversarial rewards with support estimation.

Humans are able to imagine a person's voice from the person's appearance and imagine the person's appearance from his/her voice. In this paper, we make the first attempt to develop a method that can convert speech into a voice that matches an input face image and generate a face image that matches the voice of the inpu…

We consider universal adversarial patches for faces -- small visual elements whose addition to a face image reliably destroys the performance of face detectors. Unlike previous work that mostly focused on the algorithmic design of adversarial examples in terms of improving the success rate as an attacker, in this work …

Despite the remarkable progress in face recognition related technologies, reliably recognizing faces across ages still remains a big challenge. The appearance of a human face changes substantially over time, resulting in significant intra-class variations. As opposed to current techniques for age-invariant face recogni…

DDCD uses diffusion models to learn causal structures from noisy data.

Dropout helps a stable network learn new tasks without forgetting old ones.

Faces of quasi-arithmetic Coxeter polytopes are also quasi-arithmetic.

In this paper, we study face vectors of simplicial posets that are the face posets of cell decompositions of topological manifolds without boundary. We characterize all possible face vectors of simplicial posets whose geometric realizations are homeomorphic to the product of spheres. As a corollary, we obtain the chara…

The paper explores how word embeddings affect the stability of downstream NLP models.

We are interested in attribute-guided face generation: given a low-res face input image, an attribute vector that can be extracted from a high-res image (attribute image), our new method generates a high-res face image for the low-res input that satisfies the given attributes. To address this problem, we condition the …

Study on critical faces convergence in a Poisson point process.

In this paper, we tackle the problem of online semi-supervised learning (SSL). When data arrive in a stream, the dual problems of computation and data storage arise for any SSL method. We propose a fast approximate online SSL algorithm that solves for the harmonic solution on an approximate graph. We show, both empiric…

New approach uses RS levels to improve stock trading RL model efficiency and stability.

Training deep belief networks (DBNs) requires optimizing a non-convex function with an extremely large number of parameters. Naturally, existing gradient descent (GD) based methods are prone to arbitrarily poor local minima. In this paper, we rigorously show that such local minima can be avoided (upto an approximation …

Bayesian optimization generates personalized face stimuli for cognitive neuroscience.

Motivated principally by the low-rank matrix completion problem, we present an extension of the Frank-Wolfe method that is designed to induce near-optimal solutions on low-dimensional faces of the feasible region. This is accomplished by a new approach to generating ``in-face" directions at each iteration, as well as t…

The paper is motivated by the study of graded representations of Takiff algebras, cominuscule parabolics, and their generalizations. We study certain special subsets of the set of weights (and of their convex hull) of the generalized Verma modules (or GVM's) of a semisimple Lie algebra $\lie g$. In particular, we exten…

Formula for manifold Euler characteristic using even faces.

Algorithm reduces regret in partially observable systems by learning dynamics and using optimistic control.

Face recall is a basic human cognitive process performed routinely, e.g., when meeting someone and determining if we have met that person before. Assisting a subject during face recall by suggesting candidate faces can be challenging. One of the reasons is that the search space - the face space - is quite large and lac…

New flows represent Thurston norm ball faces, differing by veering mutations.