Publications related to Adaptive Systems Laboratory

Almost surely constrained convex optimization

We propose a stochastic gradient framework for solving stochastic composite convex optimization problems with (possibly) infinite number of linear inclusion constraints that need to be satisfied almost surely. We use smoothing and homotopy techniques to ha ...

2019

Logit Pairing Methods Can Fool Gradient-Based Attacks

Maksym Andriushchenko

Recently, Kannan et al. [2018] proposed several logit regularization methods to improve the adversarial robustness of classifiers. We show that the computationally fast methods they propose - Clean Logit Pairing (CLP) and Logit Squeezing (LSQ) - just make ...

2019

Learning Hawkes Processes from a Handful of Events

Patrick Thiran, Matthias Grossglauser, William Trouleau, Farnood Salehi

Learning the causal-interaction network of multivariate Hawkes processes is a useful task in many applications. Maximum-likelihood estimation is the most common approach to solve the problem in the presence of long observation sequences. However, when only ...

2019

It is not either or: An initial investigation into combining collaborative and individual learning using an ITS

Jennifer Kaitlyn Olsen

Research on Computer-Supported Collaborative Learning (CSCL) has provided significant insights into why collaborative learning is effective and how we can effectively provide support for it. Building on this knowledge, we can investigate when collaboration ...

2019

hm-toolbox: Matlab software for HODLR and HSS matrices

Daniel Kressner, Stefano Massei

Matrices with hierarchical low-rank structure, including HODLR and HSS matrices, constitute a versatile tool to develop fast algorithms for addressing large-scale problems. While existing software packages for such matrices often focus on linear systems, t ...

2019

MATHICSE Technical Report : On maximum volume submatrices and cross approximation for symmetric semidefinite and diagonally dominant matrices

Daniel Kressner, Stefano Massei, Alice Cortinovis

The problem of finding a

k\times k

submatrix of maximum volume of a matrix A is of interest in a variety of applications. For example, it yields a quasi-best low-rank approximation constructed from the rows and columns of A. We show that such a submatrix ...

MATHICSE2019

On the Performance of Exact Diffusion over Adaptive Networks

Ali H. Sayed, Bicheng Ying, Kun Yuan, Sulaiman A S A E Alghunaim

Various bias-correction methods such as EXTRA, DIGing, and exact diffusion have been proposed recently to solve distributed deterministic optimization problems. These methods employ constant step-sizes and converge linearly to the exact solution under prop ...

IEEE2019

Decentralized Decision-Making Over Multi-Task Networks

Ali H. Sayed, Ali Zoubir

In important applications involving multi-task networks with multiple objectives, agents in the network need to decide between these multiple objectives and reach an agreement about which single objective to follow for the network. In this work we propose ...

ELSEVIER SCIENCE BV2019

Discovery and Temporal Analysis of MOOC Study Patterns

Pierre Dillenbourg, Mina Shirvani Boroujeni

The large-scale and granular interaction data collected in online learning platforms such as massive open online courses (MOOCs) provide unique opportunities to better understand individuals' learning processes and could facilitate the design of personaliz ...

2019

Log Barriers for Safe Non-convex Black-box Optimization

Maryam Kamgarpour, Andreas Krause, Ilnura Usmanova

We address the problem of minimizing a smooth function f0(x) over a compact set D defined by smooth functional constraints fi(x)≤0, i=1,…,m given noisy value measurements of fi(x). This problem arises in safety-critical applications, where certain paramete ...

2019

Adaptive Systems Laboratory

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Almost surely constrained convex optimization

Logit Pairing Methods Can Fool Gradient-Based Attacks

Learning Hawkes Processes from a Handful of Events

It is not either or: An initial investigation into combining collaborative and individual learning using an ITS

hm-toolbox: Matlab software for HODLR and HSS matrices

MATHICSE Technical Report : On maximum volume submatrices and cross approximation for symmetric semidefinite and diagonally dominant matrices

On the Performance of Exact Diffusion over Adaptive Networks

Decentralized Decision-Making Over Multi-Task Networks

Discovery and Temporal Analysis of MOOC Study Patterns

Log Barriers for Safe Non-convex Black-box Optimization

Logit Pairing Methods Can Fool Gradient-Based Attacks

It is not either or: An initial investigation into combining collaborative and individual learning using an ITS

Learning Hawkes Processes from a Handful of Events

Almost surely constrained convex optimization

hm-toolbox: Matlab software for HODLR and HSS matrices

MATHICSE Technical Report : On maximum volume submatrices and cross approximation for symmetric semidefinite and diagonally dominant matrices

Discovery and Temporal Analysis of MOOC Study Patterns

On the Performance of Exact Diffusion over Adaptive Networks

Decentralized Decision-Making Over Multi-Task Networks

Log Barriers for Safe Non-convex Black-box Optimization