A Natural Actor-Critic Framework for Zero-Sum Markov Games

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Image steganalysis, detecting hidden data in digital images, is essential for enhancing digital security. Traditional steganalysis methods typically rely on large, pre-labeled image datasets, which are difficult and costly to compile. To address this, this ...

Machine learning is increasingly integrated with decision making by powering automated systems and informing human decision makers in applications such as CV screening, portfolio allocation, loan risk assessment, and insurance approval. In this thesis, I e ...

This work presents a graph neural network (GNN) framework for solving the maximum independent set (MIS) problem, inspired by dynamic programming (DP). Specifically, given a graph, we propose a DP-like recursive algorithm based on GNNs that firstly construc ...

Robustness of medical image classification models is limited by its exposure to the candidate disease classes. Generalized zero shot learning (GZSL) aims at correctly predicting seen and unseen classes and most current GZSL approaches have focused on the s ...

Despite the huge success of deep convolutional neural networks in face recognition (FR) tasks, current methods lack explainability for their predictions because of their ``black-box'' nature. In recent years, studies have been carried out to give an interp ...

We address multi-robot safe mission planning in uncertain dynamic environments. This problem arises in several applications including safety-critical exploration, surveillance, and emergency rescue missions. Computation of a multi-robot optimal control pol ...

In this paper we propose an unsupervised feature extraction method to capture temporal information on monocular videos, where we detect and encode subject of interest in each frame and leverage contrastive self-supervised (CSS) learning to extract rich lat ...

One of the main goal of Artificial Intelligence is to develop models capable of providing valuable predictions in real-world environments. In particular, Machine Learning (ML) seeks to design such models by learning from examples coming from this same envi ...

Recent years have witnessed a rise in real-world data captured with rich structural information that can be better depicted by multi-relational or heterogeneous graphs. However, research on relational representation learning has so far mostly focused on th ...

In this paper, we propose a novel unsupervised approach for sequence matching by explicitly accounting for the locality properties in the sequences. In contrast to conventional approaches that rely on frame-to-frame matching, we conduct matching using sequ ...

Learning socially-aware motion representations is at the core of recent advances in multi-agent problems, such as human motion forecasting and robot navigation in crowds. Despite promising progress, existing representations learned with neural networks sti ...

The current information landscape is characterised by a vast amount of relatively semantically homogeneous, when observed in isolation, data silos that are, however, drastically semantically fragmented when considered as a whole. Within each data silo, inf ...

Little is known about the biological functions which are exerted by hormone receptors in physiological conditions. Here, we made use of the Mouse INtraDuctal (MIND) model, an innovative patient-derived xenograft (PDX) model, to characterize global gene exp ...

Recent advances in transfer learning and few-shot learning largely rely on annotated data related to the goal task during (pre-)training. However, collecting sufficiently similar and annotated data is often infeasible. Building on advances in self-supervis ...

Many decision problems in science, engineering, and economics are affected by uncertainty, which is typically modeled by a random variable governed by an unknown probability distribution. For many practical applications, the probability distribution is onl ...

The target assignment methods applied to robotic fiber positioners are only applicable to homogeneous sets of targets to be observed. However, different batches of robotic fiber positioners available at the focal plane of a telescope can be simultaneously ...

This paper presents an ultra-low-power voice activity detector (VAD). It uses analog signal processing for acoustic feature extraction (AFE) directly on the microphone output, approximate event-driven analog-to-digital conversion (ED-ADC), and digital deep ...

For decades, neuroscientists and psychologists have observed that animal performance on spatial navigation tasks suggests an internal learned map of the environment. More recently, map-based (or model-based) reinforcement learning has become a highly activ ...

Recent advances in statistical learning and convex optimization have inspired many successful practices. Standard theories assume smoothness---bounded gradient, Hessian, etc.--- and strong convexity of the loss function. Unfortunately, such conditions may ...

Synthesis from examples enables non-expert users to generate programs by specifying examples of their behavior. A domain-specific form of such synthesis has been recently deployed in a widely used spreadsheet software product. In this paper we contribute t ...

A Natural Actor-Critic Framework for Zero-Sum Markov Games

Chat with Graph Search

Image steganalysis using active learning and hyperparameter optimization

Machine Learning in Decision-Making Systems: Fairness, Robustness, and Data Bias

Maximum Independent Set: Self-Training through Dynamic Programming

Class Specific Feature Disentanglement and Text Embeddings for Multi-label Generalized Zero Shot CXR Classification

Discriminative Deep Feature Visualization for Explainable Face Recognition

Multi-robot task allocation for safe planning against stochastic hazard dynamics

Temporal Representation Learning on Monocular Videos for 3D Human Pose Estimation

Predicting in Uncertain Environments: Methods for Robust Machine Learning

Representation Learning for Multi-relational Data

Matching Seqlets: An Unsupervised Approach for Locality Preserving Sequence Matching

Social NCE: Contrastive Learning of Socially-aware Motion Representations

Approaching Ontology Alignment through Representation Learning to Bridge the Semantic Gap in Engineering Applications

Variably Scaled Kernels Improve Classification of Hormonally-Treated Patient-Derived Xenografts

Self-Supervised Prototypical Transfer Learning for Few-Shot Classification

Wasserstein Distributionally Robust Learning

Heterogeneous Target Assignment to Robotic Fiber Positioner Systems

Design of an Always-On Deep Neural Network-Based 1-mu W Voice Activity Detector Aided With a Customized Software Model for Analog Feature Extraction

Model-based reinforcement learning and navigation in animals and machines

Learning without Smoothness and Strong Convexity

Proactive Synthesis of Recursive Tree-to-String Functions from Examples