Publications related to Laboratory for Information and Inference Systems

Safe non-smooth black-box optimization with application to policy search

Maryam Kamgarpour, Andreas Krause, Ilnura Usmanova

For safety-critical black-box optimization tasks, observations of the constraints and the objective are often noisy and available only for the feasible points. We propose an approach based on log barriers to find a local solution of a non-convex non-smooth ...

PMLR2020

Tracking Performance of Online Stochastic Learners

Ali H. Sayed, Stefan Vlaski, Elsa Rizk

The utilization of online stochastic algorithms is popular in large-scale learning settings due to their ability to compute updates on the fly, without the need to store and process data in large batches. When a constant step-size is used, these algorithms ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Square Attack: a query-efficient black-box adversarial attack via random search

Nicolas Henri Bernard Flammarion, Maksym Andriushchenko, Francesco Croce

We propose the Square Attack, a new score-based black-box

l_2

and

l_\infty

adversarial attack that does not rely on local gradient information and thus is not affected by gradient masking. The Square Attack is based on a randomized search scheme where ...

2020

A memory of motion for visual predictive control tasks

Sylvain Calinon, Antonio Paolillo, Teguh Santoso Lembono

This paper addresses the problem of efficiently achieving visual predictive control tasks. To this end, a memory of motion, containing a set of trajectories built off-line, is used for leveraging precomputation and dealing with difficult visual tasks. Stan ...

2020

Byzantine machine learning

Rachid Guerraoui, Mahsa Taziki, El Mahdi El Mhamdi, Sébastien Louis Alexandre Rouault

The present invention concerns computer-implemented methods for training a machine learning model using Stochastic Gradient Descent, SGD. In one embodiment, the method is performed by a first computer in a distributed computing environment and comprises pe ...

2020

Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks

Nicolas Henri Bernard Flammarion, Maksym Andriushchenko, Francesco Croce

A large body of research has focused on adversarial attacks which require to modify all input features with small l2- or l∞-norms. In this paper we instead focus on query-efficient sparse attacks in the black-box setting. Our versatile framework, Sparse-RS ...

2020

pyannote.audio: neural building blocks for speaker diarization

We introduce pyannote.audio, an open-source toolkit written in Python for speaker diarization. Based on PyTorch machine learning framework, it provides a set of trainable end-to-end neural building blocks that can be combined and jointly optimized to build ...

2020

Disinformation from the Inside: Combining Machine Learning and Journalism to Investigate Sockpuppet Campaigns

Rebekah Jean Overdorf

This paper brings together machine learning and investigative journalism to examine sockpuppets accounts, a historical breed of fake accounts that are non-automated and human-controlled. Due to their flexible and human-centered nature, sockpuppets pose a c ...

ASSOC COMPUTING MACHINERY2020

When Is Amplification Necessary for Composition in Randomized Query Complexity?

Mika Tapani Göös

Suppose we have randomized decision trees for an outer function f and an inner function g. The natural approach for obtaining a randomized decision tree for the composed function (f∘ gⁿ)(x¹,…,xⁿ) = f(g(x¹),…,g(xⁿ)) involves amplifying the success probabili ...

Schloss Dagstuhl - Leibniz-Zentrum für Informatik2020

Linear Speedup In Saddle-Point Escape For Decentralized Non-Convex Optimization

Ali H. Sayed, Stefan Vlaski

Under appropriate cooperation protocols and parameter choices, fully decentralized solutions for stochastic optimization have been shown to match the performance of centralized solutions and result in linear speedup (in the number of agents) relative to no ...

IEEE2020

Laboratory for Information and Inference Systems

Graph Chatbot

Chat with Graph Search

Safe non-smooth black-box optimization with application to policy search

Tracking Performance of Online Stochastic Learners

Square Attack: a query-efficient black-box adversarial attack via random search

A memory of motion for visual predictive control tasks

Byzantine machine learning

Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks

pyannote.audio: neural building blocks for speaker diarization

Disinformation from the Inside: Combining Machine Learning and Journalism to Investigate Sockpuppet Campaigns

When Is Amplification Necessary for Composition in Randomized Query Complexity?

Linear Speedup In Saddle-Point Escape For Decentralized Non-Convex Optimization

Disinformation from the Inside: Combining Machine Learning and Journalism to Investigate Sockpuppet Campaigns

pyannote.audio: neural building blocks for speaker diarization

When Is Amplification Necessary for Composition in Randomized Query Complexity?

Safe non-smooth black-box optimization with application to policy search

Tracking Performance of Online Stochastic Learners

Byzantine machine learning

Linear Speedup In Saddle-Point Escape For Decentralized Non-Convex Optimization

Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks

Square Attack: a query-efficient black-box adversarial attack via random search

A memory of motion for visual predictive control tasks