arXiv research

A new method predicts student skill success rates in real-time.

New method forecasts workforce reintegration success rates.

Study improves efficiency of MIMO systems' sum rate estimation.

Machine learning predicts TV show success based on factors like characters and direction.

The muti-layer information bottleneck (IB) problem, where information is propagated (or successively refined) from layer to layer, is considered. Based on information forwarded by the preceding layer, each stage of the network is required to preserve a certain level of relevance with regards to a specific hidden variab…

Proposes a new framework to manage venture capital portfolio risk by focusing on deal-level correlations.

System recommends workouts and predicts success rates using RNNs.

GAIL with neural networks converges to global optima and has a known rate.

The state-of-the-art performance of deep learning algorithms has led to a considerable increase in the utilization of machine learning in security-sensitive and critical applications. However, it has recently been shown that a small and carefully crafted perturbation in the input space can completely fool a deep model.…

This paper studies a class of optimal multiple stopping problems driven by Lévy processes. Our model allows for a negative effective discount rate, which arises in a number of financial applications, including stock loans and real options, where the strike price can potentially grow at a higher rate than the original d…

Deep neural networks (DNNs) are known vulnerable to adversarial attacks. That is, adversarial examples, obtained by adding delicately crafted distortions onto original legal inputs, can mislead a DNN to classify them as any target labels. This work provides a solution to hardening DNNs under adversarial attacks through…

Deep neural networks outperform traditional methods in high-dimensional classification.

The paper analyzes how momentum affects convergence in stochastic gradient methods.

Success conditioning optimizes policies by imitating successful trajectories, solving a trust-region optimization problem.

Deep 3D models are vulnerable to isometry transformations under adversarial attacks.

Paper presents a defense framework against adversarial examples.

The convergence rate and final performance of common deep learning models have significantly benefited from heuristics such as learning rate schedules, knowledge distillation, skip connections, and normalization layers. In the absence of theoretical underpinnings, controlled experiments aimed at explaining these strate…

AdversarialPSO uses fewer queries to create high-success-rate adversarial examples.

Paper proposes a new Hessian-aware zeroth-order optimization for improving black-box adversarial attacks.

New learning rate schedule improves deep learning performance.

Study tackles discretization problem in crafting adversarial examples for discrete integer domains.

The paper provides convergence guarantees for multicalibration gradient boosting.

The paper defines successful active management and introduces a framework.

Paper establishes convergence rates and concentration bounds for stochastic approximation and reinforcement learning with Markovian noise.

AI-GAN generates realistic adversarial examples efficiently.

Tool manipulation is vital for facilitating robots to complete challenging task goals. It requires reasoning about the desired effect of the task and thus properly grasping and manipulating the tool to achieve the task. Task-agnostic grasping optimizes for grasp robustness while ignoring crucial task-specific constrain…

The paper analyzes RLVR's training dynamics, proving convergence depends on aligning update direction with Gradient Gap.

Recent work has established an empirically successful framework for adapting learning rates for stochastic gradient descent (SGD). This effectively removes all needs for tuning, while automatically reducing learning rates over time on stationary problems, and permitting learning rates to grow appropriately in non-stati…

New convergence rates for shuffling gradient methods without strong convexity.

TREK uses distillation to help students solve hard problems.

Proposes a method to select features for deep learning in noisy, high-dimensional data.

Vanguard uses AI to create personalized financial plans.

E-valuator converts verifier scores into reliable decision rules.

Cyclical learning rate improves neural machine translation performance.

In many professons employees are rewarded according to their relative performance. Corresponding economy can be modeled by taking $N$ independent agents who gain from the market with a rate which depends on their current gain. We argue that this simple realistic rate generates a scale free distribution even though intr…

Large learning rates enhance model robustness and compressibility.

The Dirichlet random walk on manifolds has a positive escape rate if the cover is non-amenable.

We describe and analyze a new boosting algorithm for deep learning called SelfieBoost. Unlike other boosting algorithms, like AdaBoost, which construct ensembles of classifiers, SelfieBoost boosts the accuracy of a single network. We prove a $\log(1/ε)$ convergence rate for SelfieBoost under some "SGD success" assumpti…

A new method to stabilize training by reducing the variance of adaptive learning rates.

The paper introduces SuccessProbaMax to optimize policy success probability in online advertising.

We present a novel optimization method, named the Combined Optimization Method (COM), for the joint optimization of two or more cost functions. Unlike the conventional joint optimization schemes, which try to find minima in a weighted sum of cost functions, the COM explores search space for common minima shared by all …

Random forests' performance is analyzed with rates of convergence and asymptotic normality established.

Theoretical analysis of DQN and its variants.

Defense against DL-based lithographic hotspot detectors backdooring attacks reduces success rate from 84% to ~0%

Inspired by the unsupervised learning or self-organization in the machine learning context, here we attempt to draw `learning curve' for the collective behavior of job-seeking `zero-intelligence' labors in successive job-hunting processes. Our labor market is supposed to be opened especially for university graduates in…

Sparse-RS framework efficiently attacks models with sparse perturbations.

The aim of this work is to provide fast and accurate approximation schemes for the Monte-Carlo pricing of derivatives in the Lévy LIBOR model of Eberlein and Özkan (2005). Standard methods can be applied to solve the stochastic differential equations of the successive LIBOR rates but the methods are generally slow. We …

Graph attacks can be successful with just a few bad nodes.