Miracle-dz/UniFAR-FTUs huggingface dataset:
RankFlow: Joint Optimization of Multi-Stage Cascade Ranking Systems as Flows

Building a multi-stage cascade ranking system is a commonly used solution to balance the efficiency and effectiveness in modern information retrieval (IR) applications, such as recommendation and web search. Despite the popularity in practice, the literature specific on multi-stage cascade ranking systems is relatively scarce. The common practice is to train rankers of each stage independently using the same user feedback data (a.k.a., impression data), disregarding the data flow and the possible interactions between stages. This straightforward solution could lead to a sub-optimal system because of the sample selection bias (SSB) issue, which is especially damaging for cascade rankers due to the negative effect accumulated in the multiple stages. Worse still, the interactions between the rankers of each stage are not fully exploited. This paper provides an elaborate analysis of this commonly used solution to reveal its limitations. By studying the essence of cascade ranking, we propose a joint training framework named RankFlow to alleviate the SSB issue and exploit the interactions between the cascade rankers, which is the first systematic solution for this topic. We propose a paradigm of training cascade rankers that emphasizes the importance of fitting rankers on stage-specific data distributions instead of the unified user feedback distribution. We design the RankFlow framework based on this paradigm: The training data of each stage is generated by its preceding stages while the guidance signals not only come from the logs but its successors. Extensive experiments are conducted on various IR scenarios, including recommendation, web search and advertisement. The results verify the efficacy and superiority of RankFlow.
Exploring Heterogeneous Data Lake based on Unified Canonical Graphs

A data lake is a repository for massive raw and heterogeneous data, which includes multiple data models with different data schemas and query interfaces. Keyword search can extract valuable information for users without the knowledge of underlying schemas and query languages. However, conventional keyword searches are restricted to a certain data model and cannot easily adapt to a data lake. In this paper, we study a novel keyword search. To achieve high accuracy and efficiency, we introduce canonical graphs and then integrate semantically related vertices based on vertex representations. A matching entity based keyword search algorithm is presented to find answers across multiple data sources. Finally, extensive experimental study shows the effectiveness and efficiency of our solution.
DGQAN: Dual Graph Question-Answer Attention Networks for Answer Selection

Community question answering (CQA) becomes increasingly prevalent in recent years, providing platforms for users with various backgrounds to obtain information and share knowledge. However, the redundancy and lengthiness issues of crowd-sourced answers limit the performance of answer selection, thus leading to difficulties in reading or even misunderstandings for community users. To solve these problems, we propose the dual graph question-answer attention networks (DGQAN) for answer selection task. Aims to fully understand the internal structure of the question and the corresponding answer, firstly, we construct a dual-CQA concept graph with graph convolution networks using the original question and answer text. Specifically, our CQA concept graph exploits the correlation information between question-answer pairs to construct two sub-graphs (QSubject-Answer and QBody-Answer), respectively. Further, a novel dual attention mechanism is incorporated to model both the internal and external semantic relations among questions and answers. More importantly, we conduct experiment to investigate the impact of each layer in the BERT model. The experimental results show that DGQAN model achieves state-of-the-art performance on three datasets (SemEval-2015, 2016, and 2017), outperforming all the baseline models.
A Review of Personal Informatics Research for People with Motor Disabilities

Personal informatics (PI) has become an area of significant research over the past decade, maturing into a sub-field that seeks to support people from many backgrounds and life contexts in collecting and finding value in their personal data. PI research includes a focus on people with chronic conditions as a monolithic group, but currently fails to distinguish the needs of people with motor disabilities (MD). To understand how current PI literature addresses those needs, we conducted a mapping review on PI publications engaged with people with MD. We report results from 50 publications identified in the ACM DL, Pubmed, JMIR, SCOPUS, and IEEE Xplore. Our analysis shows significant incompatibilities between the needs of individuals with MD and the ways that PI literature supports them. We also found inconsistencies in the ways that disability levels are reported, that PI literature for MD excludes non-health-related data domains, and an insufficient focus on PI tools' accessibility and usability for some MD users. In contrast with Epstein et al.'s [36] recent PI review, behavior change and habit awareness were the most common motivation in these publications. Finally, many of the reviewed articles reported involvement by caregivers, trainers, healthcare providers, and researchers across the PI stages. In addition to these insights, we provide recommendations for designing PI technology through a user-centric lens that will broaden the scope of PI and include people regardless of their motor abilities.
Building envelope with variable thermal performance: Opportunities and challenges

Over the past two decades, the world’s primary energy usage has grown by 49%, with an average annual increase of 2%. Buildings account for 20% 40% of the total energy consumption. The building envelope is an important factor that could influence the building energy consumption. As the indoor and outdoor environment interface, building envelope can adjust various forms of energy flow (light, heat, sound, humidity, etc.) and offer security, privacy, access and view. The main functions of a building envelope are in the following aspects: vision, daylighting, shading, thermal insulation, heat preservation, ventilation and sound insulation. In order to reduce energy consumption and greenhouse gases emissions, usage of renewable energy in buildings is encouraged to replace conventional fuels. However, renewable energy is of low energy density and intermittent. Optimizing the structure, thermal performance and operational strategies of building envelope, and focusing on the harvest, storage and release of renewable energy is an important way to make full use of climate resource for building energy conservation. Dr. Fernández from The Massachusetts Institute of Technology wrote in Science that,
A Model-Agnostic Randomized Learning Framework based on Random Hypothesis Subspace Sampling

We propose a model-agnostic randomized learning framework based on Random Hypothesis Subspace Sampling (RHSS). Given any hypothesis class, it randomly samples k hypotheses and learns a near-optimal model from their span by simply solving a linear least square problem in O ( nk 2 ) time, where n is the number of training instances. On the theory side, we derive the performance guarantee of RHSS from a generic subspace approximation perspective, leveraging properties of metric entropy and random matrices. On the practical side, we apply the RHSS framework to learn kernel, network and tree based models. Experimental results show they converge efficiently as k increases and outperform their model-specific counterparts including random fourier feature, random vector functional link and extra tree on real-world data sets.
Versatile Dueling Bandits: Best-of-both World Analyses for Learning from Relative Preferences

We study the problem of K -armed dueling bandit for both stochastic and adversarial environments, where the goal of the learner is to aggregate information through relative preferences of pair of decision points queried in an online sequential manner. We ﬁrst propose a novel reduction from any (general) dueling bandits to multi-armed ban-dits which allows us to improve many existing results in dueling bandits. In particular, we give the ﬁrst best-of-both world result for the dueling bandits regret minimization problem —a uniﬁed framework that is guaranteed to perform optimally for both stochastic and adversarial preferences simultaneously. Moreover, our algorithm is also the ﬁrst to achieve an optimal O ( (cid:80) Ki =1 log T ∆ i ) regret bound against the Condorcet-winner benchmark, which scales optimally both in terms of the arm-size K and the instance-speciﬁc subop-timality gaps { ∆ i } Ki =1 . This resolves the long standing problem of designing an instancewise gap-dependent order optimal regret algorithm for dueling bandits (with matching lower bounds up to small constant factors). We further justify the robustness of our proposed algorithm by proving its optimal regret rate under adversarially corrupted preferences—this outperforms the existing state-of-the-art corrupted dueling results by a large margin. In summary, we believe our reduction idea will ﬁnd a broader scope in solving a diverse class of dueling bandits setting, which are otherwise studied separately from multi-armed bandits with often more complex solutions and worse guarantees. The efﬁcacy of our proposed algorithms are empirically corroborated against state-of-the-art dueling bandit methods.
Learning-based Optimisation of Particle Accelerators Under Partial Observability Without Real-World Training

In recent work, it has been shown that reinforcement learning (RL) is capable of solving a variety of problems at sometimes super-human performance levels. But despite continued advances in the field, applying RL to complex real-world con-trol and optimisation problems has proven difficult. In this contribution, we demonstrate how to successfully apply RL to the optimisation of a highly complex real-world machine – specifically a linear particle accelerator – in an only partially observable setting and without requiring training on the real machine. Our method outperforms conventional optimisation algorithms in both the achieved result and time taken as well as already achieving close to human-level performance. We expect that such automation of machine optimisation will push the limits of operability, increase machine availability and lead to a paradigm shift in how such machines are operated, ultimately facilitating advances in a variety of fields, such as science and medicine among many others.
On the Equivalence Between Temporal and Static Equivariant Graph Representations

This work formalizes the associational task of predicting node attribute evolution in temporal graphs from the perspective of learning equivariant representations. We show that node representations in temporal graphs can be cast into two distinct frameworks: (a) The most popular approach, which we denote as time-and-graph , where equivariant graph (e.g., GNN) and sequence (e.g., RNN) representations are intertwined to represent the temporal evolution of node attributes in the graph; and (b) an approach that we denote as time-then-graph , where the sequences de-scribing the node and edge dynamics are represented first, then fed as node and edge attributes into a static equivariant graph representation that comes after. Interestingly, we show that time-then-graph representations have an expressivity advantage over time-and-graph representations when both use component GNNs that are not most-expressive (e.g., 1-Weisfeiler-Lehman GNNs). Moreover, while our goal is not necessarily to obtain state-of-the-art results, our experiments show that time-then-graph methods are capable of achieving better performance and efficiency than state-of-the-art time-and-graph methods in some real-world tasks, thereby showcasing that the time-then-graph framework is a worthy addi-tion to the graph ML toolbox.
Improved StyleGAN-v2 based Inversion for Out-of-Distribution Images

Inverting an image onto the latent space of pre-trained generators, e.g., StyleGAN-v2, has emerged as a popular strategy to leverage strong image priors for ill-posed restoration. Several studies have showed that this approach is effective at inverting images similar to the data used for training. However, with out-of-distribution (OOD) data that the generator has not been exposed to, existing inversion techniques produce sub-optimal results. In this paper, we propose SPHInX (StyleGAN with Projection Heads for Inverting X), an approach for accurately embedding OOD images onto the StyleGAN latent space. SPHInX optimizes a style projection head using a novel training strategy that imposes a vicinal regularization in the StyleGAN latent space. To further enhance OOD inversion, SPHInX can additionally optimize a content projection head and noise variables in every layer. Our empirical studies on a suite of OOD data show that, in addition to producing higher quality reconstructions over the state-of-the-art inversion techniques, SPHInX is effective for ill-posed restoration tasks while offering semantic editing capabilities.
Nonlinear Feature Diffusion on Hypergraphs

Hypergraphs are a common model for multi-way relationships in data, and hypergraph semi-supervised learning is the problem of assigning labels to all nodes in a hypergraph, given labels on just a few nodes. Diffusions and label spreading are classical techniques for semi-supervised learning in the graph setting, and there are some standard ways to extend them to hypergraphs. However, these methods are linear models, and do not offer an obvious way of incorporating node features for making predictions. Here, we develop a nonlinear diffusion process on hypergraphs that spreads both features and labels following the hypergraph structure. Even though the process is nonlinear, we show global convergence to a unique limiting point for a broad class of nonlinearities and we show that such limit is the global minimum of a new regularized semi-supervised learning loss function which aims at reducing a generalized form of variance of the node features across the hyperedges. The limiting point serves as a node embedding from which we make predictions with a linear model. Our approach is competitive with popular graph and hypergraph neural network baselines, and also takes less time to train.
TSPipe: Learn from Teacher Faster with Pipelines

The teacher-student (TS) framework, training a (student) network by utilizing an auxiliary superior (teacher) network, has been adopted as a popular training paradigm in many machine learning schemes, since the seminal work—Knowledge distillation (KD) for model compression and transfer learning. Many recent self-supervised learning (SSL) schemes also adopt the TS framework, where teacher networks are maintained as the moving average of student networks, called the momentum networks. This paper presents TSPipe, a pipelined approach to accelerate the training process of any TS frameworks including KD and SSL. Under the observation that the teacher network does not need a backward pass, our main idea is to schedule the computation of the teacher and student network separately, and fully utilize the GPU during training by interleaving the computations of the two networks and relaxing their dependencies. In case the teacher network requires a momentum update, we use delayed parameter updates only on the teacher network to attain high model accuracy. Compared to existing pipeline parallelism schemes, which sacrifice either training throughput or model accuracy, TSPipe provides better performance trade-offs, achieving up to 12.15x higher throughput. 1
Optimal Private Streaming SCO in (cid:96) p -geometry with Applications in High Dimensional Online Decision Making

Differentially private (DP) stochastic convex optimization (SCO) is ubiquitous in trustworthy machine learning algorithm design. This paper studies the DP-SCO problem with streaming data sampled from a distribution and arrives sequentially. We also consider the continual release model where parameters related to private information are updated and released upon each new data. Numerous algorithms have been developed to achieve optimal excess risks in different (cid:96) p norm geometries, but none of the existing ones can be adapted to the streaming and continual release setting. We propose a private variant of the Frank-Wolfe algorithm with recursive gradients for variance reduction to update and reveal the parameters upon each data. Combined with the adaptive DP analysis, our algorithm achieves the ﬁrst optimal excess risk in linear time in the case 1 < p ≤ 2 and the state-of-the-art excess risk meeting the non-private lower ones when 2 < p ≤ ∞ . Our algorithm can also be extended to the case p = 1 to achieve nearly dimension-independent excess risk. While previous variance reduction results on recursive gradient have theoretical guarantee only in the i.i. d. setting, we establish such a guarantee in a non-stationary setting. To demonstrate the virtues of our method, we de-sign the ﬁrst DP algorithm for high-dimensional generalized linear bandits with logarithmic regret.
Revenue guarantee in public–private partnerships: a win–win model

Abstract Government guarantees are often granted to increase the viability of the Public–Private Partnership projects, although the use of these guarantees can cause problems if they are not properly set. This paper provides a useful tool to define a proper value of public subsidies. In particular, we develop a methodology to calculate the optimal values of the revenue floor (the minimum amount of revenue secured by the government in the minimum revenue guarantee) and revenue ceiling (the upper threshold of revenue that defines the excess revenue to be shared in the revenue sharing) in a way that creates a “win–win” condition for the concessionaire and the government and fairly shares risk between them. The proposed methodology operationalizes minimum revenue guarantee and revenue sharing as real options. The resulting real option-based model is applied to a real case, namely the Strait of Messina Bridge in Italy. The application shows the usefulness of the model in supporting: (a) the government’s decision-making process on assessing values of public subsidies needed to make the project attractive to private investors; and (b) both public and private parties during the negotiation in finding the fair values of the revenue floor and ceiling.
Provably Efficient Offline Reinforcement Learning for Partially Observable Markov Decision Processes

We study offline reinforcement learning (RL) for partially observable Markov decision processes (POMDPs) with possibly infinite state and observation spaces. Under the undercompleteness assumption, the transition kernel can be estimated via solving a series of confounded regression problems. To solve the confounding problem, we select a proper instrumental variable (IV) and solves the IV regression problem to construct confidence regions for the model parameters. We get the final policy via pessimistic planning within the confidence regions. We prove that the proposed algorithm attains an ϵ -optimal policy using an offline dataset containing (cid:101) O (1 /ϵ 2 ) episodes, provided that the behavior policy has good coverage over the optimal trajectory. To our best knowledge, our algorithm is the first provably sample efficient offline algorithm for POMDPs that is not tabular.
Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization

Image cartoonization is recently dominated by generative adversarial networks (GANs) from the perspective of unsupervised image-to-image translation, in which an inherent challenge is to precisely capture and sufficiently transfer characteristic cartoon styles (e.g., clear edges, smooth color shading, abstract fine structures, etc.). Existing advanced models try to enhance cartoonization effect by learning to promote edges adversarially, introducing style transfer loss, or learning to align style from multiple representation space. This paper demonstrates that more distinct and vivid cartoonization effect could be easily achieved with only basic adversarial loss. Observing that cartoon style is more evident in cartoon-texture-salient local image regions, we build a region-level adversarial learning branch in parallel with the normal image-level one, which constrains adversarial learning on cartoon-texture-salient local patches for better perceiving and transferring cartoon texture features. To this end, a novel cartoon-texture-saliency-sampler (CTSS) module is proposed to dynamically sample cartoon-texture-salient patches from training data. With extensive experiments, we demonstrate that texture saliency adaptive attention in adversarial learning, as a missing ingredient of related methods in image cartoonization, is of significant importance in facilitating and enhancing image cartoon stylization, especially for high-resolution input pictures.
Congested Bandits: Optimal Routing via Short-term Resets

For trafﬁc routing platforms, the choice of which route to recommend to a user depends on the congestion on these routes – indeed, an individual’s utility depends on the number of people using the recommended route at that instance. Motivated by this, we introduce the problem of Congested Bandits where each arm’s reward is allowed to depend on the number of times it was played in the past ∆ timesteps. This dependence on past history of actions leads to a dynamical system where an algorithm’s present choices also affect its future pay-offs, and requires an algorithm to plan for this. We study the congestion aware formulation in the multi-armed bandit (MAB) setup and in the contextual bandit setup with linear rewards. For the multi-armed setup, we propose a UCB style algorithm and show that its policy regret scales as ˜ O ( √ K ∆ T ) . For the linear contextual bandit setup, our algorithm, based on an iterative least squares planner, achieves policy regret ˜ O ( √ dT + ∆) . From an experimental stand-point, we corroborate the no-regret properties of our algorithms via a simulation study.
UniRank: Unimodal Bandit Algorithm for Online Ranking

We tackle a new emerging problem, which is finding an optimal monopartite matching in a weighted graph. The semi-bandit version, where a full matching is sampled at each iteration, has been addressed by \cite{ADMA}, creating an algorithm with an expected regret matching $O(\frac{L\log(L)}{\Delta}\log(T))$ with $2L$ players, $T$ iterations and a minimum reward gap $\Delta$. We reduce this bound in two steps. First, as in \cite{GRAB} and \cite{UniRank} we use the unimodality property of the expected reward on the appropriate graph to design an algorithm with a regret in $O(L\frac{1}{\Delta}\log(T))$. Secondly, we show that by moving the focus towards the main question `\emph{Is user $i$ better than user $j$? }' this regret becomes $O(L\frac{\Delta}{\tilde{\Delta}^2}\log(T))$, where $\Tilde{\Delta}>\Delta$ derives from a better way of comparing users. Some experimental results finally show these theoretical results are corroborated in practice.