Learning Problem

Learning problems encompass the development of algorithms that enable machines to learn from data, aiming to improve performance on specific tasks. Current research heavily focuses on addressing challenges like adversarial attacks (where malicious inputs mislead models), distributed learning (collaborative learning across multiple agents), and the need for explainable and robust models. Key approaches involve adversarial training, generative adversarial networks (GANs), and optimization techniques tailored to specific model architectures (e.g., neural networks, classification trees). These advancements are crucial for improving the reliability and security of machine learning systems across diverse applications, from malware detection to secure communication networks.

Papers

May 23, 2024

A New Formulation for Zeroth-Order Optimization of Adversarial EXEmples in Malware Detection
Marco Rando, Luca Demetrio, Lorenzo Rosasco, Fabio Roli
Adversarial Example Malware Detection Zeroth Order New Formulation Learning Problem

May 17, 2024

Distributed Event-Based Learning via ADMM
Guner Dilsad Er, Sebastian Trimpe, Michael Muehlebach
LeArning Abstract Convergence Rate Objective Function Learning Problem Accelerated Convergence Unfolded D ADMM Lasso Problem

February 6, 2024

Explainable Adversarial Learning Framework on Physical Layer Secret Keys Combating Malicious Reconfigurable Intelligent Surface
Zhuangkun Wei, Wenxiu Hu, Weisi Guo
Generative Adversarial Network Physical Layer Reconfigurable Intelligent Surface Learning Problem

November 29, 2023

GSE: Group-wise Sparse and Explainable Adversarial Attacks
Shpresim Sadiku, Moritz Wagner, Sebastian Pokutta
Adversarial Attack Learning Problem Structural Regularization Group Sparsity Sparse Attack Sparse Adversarial Attack

November 9, 2023

Counter-Empirical Attacking based on Adversarial Reinforcement Learning for Time-Relevant Scoring System
Xiangguo Sun, Hong Cheng, Hang Dong, Bo Qiao, Si Qin, Qingwei Lin
Credit Scoring Adversarial Reinforcement Learning Learning Problem Scoring Function

October 23, 2023

Revisiting Implicit Differentiation for Learning Problems in Optimal Control
Ming Xu, Timothy Molloy, Stephen Gould
Optimal Control Discrete Time Optimal Trajectory Linear Quadratic Regulator Implicit Differentiation Learning Problem

October 2, 2023

Adversarial Client Detection via Non-parametric Subspace Monitoring in the Internet of Federated Things
Xianjian Xie, Xiaochen Xian, Dan Li, Andi Wang
Adversarial Attack Adversarial Behavior Learning Problem Subspace Identification

July 20, 2023

On the Convergence of Bounded Agents
David Abel, André Barreto, Hado van Hasselt, Benjamin Van Roy, Doina Precup, Satinder Singh
Early Stage Convergence Reinforcement Learning Problem Learning Problem Convergence Behavior Bounded Rational Future Behavior

May 13, 2023

A Novel Memetic Strategy for Optimized Learning of Classification Trees
Tommaso Aldinucci
Interpretable Machine Learning Greedy Approach Learning Problem Optimal Learning Classification Tree

April 28, 2023

On the existence of solutions to adversarial training in multiclass classification
Nicolas Garcia Trillos, Matt Jacobs, Jakwang Kim
Adversarial Training Adversarial Perturbation Solution Path Multiclass Classification Robust Classifier Learning Problem Agnostic Adversarial

March 12, 2023

Data Dependent Regret Guarantees Against General Comparators for Full or Bandit Feedback
Kaan Gokcesu, Hakan Gokcesu
Contextual Bandit Bandit Feedback Regret Guarantee Learning Problem Loss Surface Dependent Regret Optimal Bandit

February 6, 2023

Find a witness or shatter: the landscape of computable PAC learning
Valentino Delle Rose, Alexander Kozachinskiy, Cristobal Rojas, Tomasz Steifer
Sample Complexity Landscape Image PAC Learning Learning Problem Interview Data

January 9, 2023

On adversarial robustness and the use of Wasserstein ascent-descent dynamics to enforce it
Camilo Garcia Trillos, Nicolas Garcia Trillos
Adversarial Robustness Greater Public Use Mean Field Wasserstein Gradient Flow Adversarial Risk Learning Problem Mean Field Limit

October 27, 2022

How To Overcome Richness Axiom Fallacy
Mieczysław A. Kłopotek, Robert A. Kłopotek
Axiomatic Approach Constrained Learning Learning Problem

October 18, 2022

SQ Lower Bounds for Learning Single Neurons with Massart Noise
Ilias Diakonikolas, Daniel M. Kane, Lisheng Ren, Yuxin Sun
Learning Problem Single Neuron Square Loss Massart Noise Statistical Query Lower Bound

October 10, 2022

Do you pay for Privacy in Online learning?
Amartya Sanyal, Giorgia Ramponi
Differential Privacy Online Learning Privacy Policy Learning Problem

September 30, 2022

Understanding Pure CLIP Guidance for Voxel Grid NeRF Models
Han-Hung Lee, Angel X. Chang
Image Augmentation 3D Object Generation Learning Problem Adversarial Generation Grid Based NeRF

September 14, 2022

TSFool: Crafting Highly-Imperceptible Adversarial Time Series through Multi-Objective Attack
Yanyun Wang, Dehui Du, Haibo Hu, Zi Liang, Yuanhao Liu
Recurrent Neural Network Learning Problem Imperceptible Adversarial

September 10, 2022

Code Compliance Assessment as a Learning Problem
Neela Sawant, Srinivasan H. Sengamedu
Regulatory Compliance Learning Problem Policy Violation Compliance Monitoring Code Review

August 1, 2022

Towards Computing an Optimal Abstraction for Structural Causal Models
Fabio Massimo Zennaro, Paolo Turrini, Theodoros Damoulas
Causal Model Structural Causal Model Digital Computing Cognitive Abstraction Metric Aware Abstraction Learning Problem Information Loss