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The presence of inequity is a fundamental problem in the outcomes of decision-making systems, especially when human lives are at stake. Yet, estimating notions of unfairness or inequity is difficult, particularly if they rely on hard-to-measure concepts such as risk. Such measurements of risk can be accurately obtained when no unobserved confounders have jointly influenced past decisions and outcomes. However, in the real world, this assumption rarely holds. In this paper, we show a surprising result that one can still give meaningful bounds on treatment rates to high-risk individuals, even when entirely eliminating or relaxing the assumption that all relevant risk factors are observed. We use the fact that in many real-world settings (e.g., the release of a new treatment) we have data from prior to any allocation to derive unbiased estimates of risk. This result is of immediate practical interest: we can audit unfair outcomes of existing decision-making systems in a principled manner. For instance, in a real-world study of Paxlovid allocation, our framework provably identifies that observed racial inequity cannot be explained by unobserved confounders of the same strength as important observed covariates.

Introduced by Papadimitriou and Yannakakis in 1989, layered graph traversal is an important problem in online algorithms and mobile computing that has been studied for several decades, and which now is essentially resolved in its original formulation. In this paper, we demonstrate that what appears to be an innocuous modification of the problem actually leads to a drastic (exponential) reduction of the competitive ratio. Specifically, we present an algorithm that is $O(\log^2 w)$-competitive for traversing unweighted layered graphs of width $w$. Our technique is based on a simple entropic regularizer, which evolves as the agent progresses in the layered graph. Our algorithm is randomized and simply maintains that at all layers, the probability distribution of the position of the mobile agent maximizes the entropic regularizer.

Dimension reduction techniques usually lose information in the sense that reconstructed data are not identical to the original data. However, we argue that it is possible to have reconstructed data identically distributed as the original data, irrespective of the retained dimension or the specific mapping. This can be achieved by learning a distributional model that matches the conditional distribution of data given its low-dimensional latent variables. Motivated by this, we propose Distributional Principal Autoencoder (DPA) that consists of an encoder that maps high-dimensional data to low-dimensional latent variables and a decoder that maps the latent variables back to the data space. For reducing the dimension, the DPA encoder aims to minimise the unexplained variability of the data with an adaptive choice of the latent dimension. For reconstructing data, the DPA decoder aims to match the conditional distribution of all data that are mapped to a certain latent value, thus ensuring that the reconstructed data retains the original data distribution. Our numerical results on climate data, single-cell data, and image benchmarks demonstrate the practical feasibility and success of the approach in reconstructing the original distribution of the data. DPA embeddings are shown to preserve meaningful structures of data such as the seasonal cycle for precipitations and cell types for gene expression.

Implementing concurrent data structures is challenging and requires a deep understanding of concurrency concepts and careful design to ensure correctness, performance, and scalability. Further, composing operations on two or more concurrent data structures often requires a synchronization wrapper to ensure the operations are applied together atomically, resulting in serialization and, thereby, giving up the performance benefit of the individual data structures. DBMS provides generalized concurrency control (CC) and is a good fit for implementing concurrent data structures. However, DBMSs are over-generalized for this use case, which fails to match the performance of specialized implementations. This paper makes the case for the Declarative Concurrent Data Structures (DCDS) framework for automatically generating concurrent data structures from a serial specification. In DCDS, users declare the attributes and methods needed for their desired data structure through an embedded DSL at design time. DCDS automatically injects CC at build-time, generating a concurrent intermediate representation (IR) compiled into machine code. A declarative interface for designing data structure enables efficient composability through co-optimizing component structures; optimizations are applied to both the composed serial specification and the generated concurrent IR. We realize the DCDS framework in our prototype system Rosti and experimentally show that data structures declared in Rosti can be efficiently composed by co-optimizing their logical functionality and the generated CC protocol. Our evaluation shows that composing a map and a list to create an LRU container can benefit up to 2X performance scalability in Rosti compared to an open-source library. We demonstrate the applicability of DCDS as an in-process OLTP by comparing it with in-memory DBMS, Proteus, and showing up to 2X performance gains.

The perception of the value and propriety of modern engineered systems is changing. In addition to their functional and extra-functional properties, nowadays' systems are also evaluated by their sustainability properties. The next generation of systems will be characterized by an overall elevated sustainability -- including their post-life, driven by efficient value retention mechanisms. Current systems engineering practices fall short of supporting these ambitions and need to be revised appropriately. In this paper, we introduce the concept of circular systems engineering, a novel paradigm for systems sustainability, and define two principles to successfully implement it: end-to-end sustainability and bipartite sustainability. We outline typical organizational evolution patterns that lead to the implementation and adoption of circularity principles, and outline key challenges and research opportunities.

This book is the result of a seminar in which we reviewed multimodal approaches and attempted to create a solid overview of the field, starting with the current state-of-the-art approaches in the two subfields of Deep Learning individually. Further, modeling frameworks are discussed where one modality is transformed into the other, as well as models in which one modality is utilized to enhance representation learning for the other. To conclude the second part, architectures with a focus on handling both modalities simultaneously are introduced. Finally, we also cover other modalities as well as general-purpose multi-modal models, which are able to handle different tasks on different modalities within one unified architecture. One interesting application (Generative Art) eventually caps off this booklet.

The information bottleneck (IB) method is a technique for extracting information that is relevant for predicting the target random variable from the source random variable, which is typically implemented by optimizing the IB Lagrangian that balances the compression and prediction terms. However, the IB Lagrangian is hard to optimize, and multiple trials for tuning values of Lagrangian multiplier are required. Moreover, we show that the prediction performance strictly decreases as the compression gets stronger during optimizing the IB Lagrangian. In this paper, we implement the IB method from the perspective of supervised disentangling. Specifically, we introduce Disentangled Information Bottleneck (DisenIB) that is consistent on compressing source maximally without target prediction performance loss (maximum compression). Theoretical and experimental results demonstrate that our method is consistent on maximum compression, and performs well in terms of generalization, robustness to adversarial attack, out-of-distribution detection, and supervised disentangling.

The aim of this work is to develop a fully-distributed algorithmic framework for training graph convolutional networks (GCNs). The proposed method is able to exploit the meaningful relational structure of the input data, which are collected by a set of agents that communicate over a sparse network topology. After formulating the centralized GCN training problem, we first show how to make inference in a distributed scenario where the underlying data graph is split among different agents. Then, we propose a distributed gradient descent procedure to solve the GCN training problem. The resulting model distributes computation along three lines: during inference, during back-propagation, and during optimization. Convergence to stationary solutions of the GCN training problem is also established under mild conditions. Finally, we propose an optimization criterion to design the communication topology between agents in order to match with the graph describing data relationships. A wide set of numerical results validate our proposal. To the best of our knowledge, this is the first work combining graph convolutional neural networks with distributed optimization.

We introduce an approach for deep reinforcement learning (RL) that improves upon the efficiency, generalization capacity, and interpretability of conventional approaches through structured perception and relational reasoning. It uses self-attention to iteratively reason about the relations between entities in a scene and to guide a model-free policy. Our results show that in a novel navigation and planning task called Box-World, our agent finds interpretable solutions that improve upon baselines in terms of sample complexity, ability to generalize to more complex scenes than experienced during training, and overall performance. In the StarCraft II Learning Environment, our agent achieves state-of-the-art performance on six mini-games -- surpassing human grandmaster performance on four. By considering architectural inductive biases, our work opens new directions for overcoming important, but stubborn, challenges in deep RL.