Miracle-dz/UniFAR-FTUs huggingface dataset:
On ranking via sorting by estimated expected utility

Ranking tasks are defined through losses that measure trade-offs between different desiderata such as the relevance and the diversity of the items at the top of the list. This paper addresses the question of which of these tasks are asymptotically solved by sorting by decreasing order of expected utility, for some suitable notion of utility, or, equivalently, when is square loss regression consistent for ranking via score-andsort? We answer to this question by finding a characterization of ranking losses for which a suitable regression is consistent. This characterization has two strong corollaries. First, whenever there exists a consistent approach based on convex risk minimization, there also is a consistent approach based on regression. Second, when regression is not consistent, there are data distributions for which consistent surrogate approaches necessarily have non-trivial local minima, and for which optimal scoring function are necessarily discontinuous, even when the underlying data distribution is regular. In addition to providing a better understanding of surrogate approaches for ranking, these results illustrate the intrinsic difficulty of solving general ranking problems with the score-and-sort approach.
Learning Multi-Agent Communication through Structured Attentive Reasoning

Learning communication via deep reinforcement learning has recently been shown to be an effective way to solve cooperative multi-agent tasks. However, learning which communicated information is beneﬁcial for each agent’s decision-making process remains a challenging task. In order to address this problem, we explore relational reinforcement learning which leverages attention-based networks to learn efﬁcient and interpretable relations between entities. On the foundation of relations, we introduce a novel communication architecture that exploits a memory-based attention network that selectively reasons about the value of information received from other agents while considering its past experiences. Speciﬁcally, the model communicates by ﬁrst computing the relevance of messages received from other agents and then extracts task-relevant information from memories given the newly received information. We empirically demonstrate the strength of our model in cooperative and competitive multi-agent tasks, where inter-agent communication and reasoning over prior information substantially improves performance compared to baselines. We further show in the accompanying videos and experimental results that the agents learn a sophisticated and diverse set of cooperative behaviors to solve challenging tasks, both for discrete and continuous action spaces using on-policy and off-policy gradient methods. By developing an explicit architecture that is targeted towards communication, our work aims to open new directions to overcome important challenges in multi-agent cooperation through learned communication.
Identification and Prediction of Human Behavior through Mining of Unstructured Textual Data

The identification of human behavior can provide useful information across multiple job spectra. Recent advances in applying data-based approaches to social sciences have increased the feasibility of modeling human behavior. In particular, studying human behavior by analyzing unstructured textual data has recently received considerable attention because of the abundance of textual data. The main objective of the present study was to discuss the primary methods for identifying and predicting human behavior through the mining of unstructured textual data. Of the 823 articles analyzed, 87 met the predefined inclusion criteria and were included in the literature review. Our results show that the included articles could be symmetrically classified into two groups. The first group of articles attempted to identify the leading indicators of human behavior in unstructured textual data. In this group, the data-based approaches had three main components: (1) collecting self-reported survey data, (2) collecting data from social media and extracting data features, and (3) applying correlation analysis to evaluate the relationship between two sets of data. In contrast, the second group focused on the accuracy of data-based approaches for predicting human behavior. In this group, the data-based approaches could be categorized into (1) approaches based on labeled unstructured textual data and (2) approaches based on unlabeled unstructured textual data. The review provides a comprehensive insight into unstructured textual data mining to identify and predict human behavior and personality traits.
Revisiting Parameter Sharing for Automatic Neural Channel Number Search

Recent advances in neural architecture search inspire many channel number search algorithms (CNS) for convolutional neural networks. To improve searching ef-ﬁciency, parameter sharing is widely applied, which reuses parameters among different channel conﬁgurations. Nevertheless, it is unclear how parameter sharing affects the searching process. In this paper, we aim at providing a better understanding and exploitation of parameter sharing for CNS. Speciﬁcally, we propose afﬁne parameter sharing (APS) as a general formulation to unify and quantitatively analyze existing channel search algorithms. It is found that with parameter sharing, weight updates of one architecture can simultaneously beneﬁt other candidates. However, it also results in less conﬁdence in choosing good architectures. We thus propose a new strategy of parameter sharing towards a better balance between training efﬁciency and architecture discrimination. Extensive analysis and experiments demonstrate the superiority of the proposed strategy in channel conﬁguration against many state-of-the-art counterparts on benchmark datasets.
Discovering conflicting groups in signed networks

Signed networks are graphs where edges are annotated with a positive or negative sign, indicating whether an edge interaction is friendly or antagonistic. Signed networks can be used to study a variety of social phenomena, such as mining polarized discussions in social media, or modeling relations of trust and distrust in online review platforms. In this paper we study the problem of detecting k conﬂicting groups in a signed network. Our premise is that each group is positively connected internally and negatively connected with the other k − 1 groups. A distinguishing aspect of our formulation is that we are not searching for a complete partition of the signed network; instead, we allow a subset of nodes to be neutral with respect to the conﬂict structure we are searching. As a result, the problem we tackle differs from previously-studied problems, such as correlation clustering and k -way partitioning. To solve the conﬂicting-group discovery problem, we derive a novel formulation in which each conﬂicting group is naturally characterized by the solution to the maximum discrete Rayleigh’s quotient ( M AX -DRQ ) problem. We present two spectral methods for ﬁnding approximate solutions to the M AX -DRQ problem, which we analyze theoretically. Our experimental evaluation shows that, compared to state-of-the-art baselines, our methods ﬁnd solutions of higher quality, are faster, and recover ground-truth conﬂicting groups with higher accuracy.
Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient Space

Distilling knowledge from an ensemble of teacher models is expected to have a more promising performance than that from a single one. Current methods mainly adopt a vanilla average rule, i.e. , to simply take the average of all teacher losses for training the student network. However, this approach treats teachers equally and ignores the diversity among them. When conﬂicts or competitions exist among teachers, which is common, the inner compromise might hurt the distillation performance. In this paper, we examine the diversity of teacher models in the gradient space and regard the ensemble knowledge distillation as a multi-objective optimization problem so that we can determine a better optimization direction for the training of student network. Besides, we also introduce a tolerance parameter to accommodate disagreement among teachers. In this way, our method can be seen as a dynamic weighting method for each teacher in the ensemble. Extensive experiments validate the effectiveness of our method for both logits-based and feature-based cases.
Supplementary Material of "Bayesian Causal Structural Learning with Zero-Inﬂated Poisson Bayesian Networks"

Without loss of generality, we assume that the nodes are labeled such that there is no directed edge in E from later node to earlier node. Such labeling is also known as perfect/topological ordering of DAG G. Define a set of edges that are connected to node j and have opposite directions in E and E′, re(j) = {k ∈ V : ejk = ekj = 1} for j = 1, . , p. For k ∈ re(j), E includes an edge k → j, while E′ has the reverse edge j → k. If re(j) = ∅ for all j, there exists no pair of nodes (j, k) such that ejk = ekj = 1. This means E = E ′, because Markov equivalent DAGs have the same skeleton. We will show by mathematical induction that re(j) = ∅ for all j, which contradicts the assumption that E 6= E′. For node p that is the last element of the perfect ordering of G, we have pa(p) = pa′(p) ∪ re(p) due to the same skeleton of G and G′. Taking the difference of the equality (1) at (x1, x2, . . . , xp + 1) and (x1, x2, . . . , xp) yields, ∑
Agnostic Q -learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity

The current paper studies the problem of agnostic Q -learning with function approx-1 imation in deterministic systems where the optimal Q -function is approximable 2 by a function in the class F with approximation error δ ≥ 0 . We propose a novel 3 recursion-based algorithm and show that if δ = O (cid:0) ρ/ √ dim E (cid:1) , then one can ﬁnd 4 the optimal policy using O (dim E ) trajectories, where ρ is the gap between the 5 optimal Q -value of the best actions and that of the second-best actions and dim E 6 is the Eluder dimension of F . Our result has two implications: 7 1. In conjunction with the lower bound in [Du et al., 2020], our upper bound 8 suggests that the condition δ = (cid:101) Θ (cid:0) ρ/ √ dim E (cid:1) is necessary and sufﬁcient for 9 algorithms with polynomial sample complexity. 10 2. In conjunction with the obvious lower bound in the tabular case, our upper 11 bound suggests that the sample complexity (cid:101) Θ (dim E ) is tight in the agnostic 12 setting. 13 Therefore, we help address the open problem on agnostic Q -learning proposed 14 in [Wen and Van Roy, 2013]. We further extend our algorithm to the stochastic 15 reward setting and obtain similar results. 16
Better Full-Matrix Regret via Parameter-Free Online Learning

We provide online convex optimization algorithms that guarantee improved full-matrix regret bounds. These algorithms extend prior work in several ways. First, we seamlessly allow for the incorporation of constraints without requiring unknown oracle-tuning for any learning rate parameters. Second, we improve the regret analysis of the full-matrix AdaGrad algorithm by suggesting a better learning rate value and showing how to tune the learning rate to this value on-the-ﬂy. Third, all our bounds are obtained via a general framework for constructing regret bounds that depend on an arbitrary sequence of norms.
Minimax Bounds for Generalized Linear Models

We establish a new class of minimax prediction error bounds for generalized linear models. Our bounds significantly improve previous results when the design matrix is poorly structured, including natural cases where the matrix is wide or does not have full column rank. Apart from the typical L2 risks, we study a class of entropic risks which recovers the usual L2 prediction and estimation risks, and demonstrate that a tight analysis of Fisher information can uncover underlying structural dependency in terms of the spectrum of the design matrix. The minimax approach we take differs from the traditional metric entropy approach, and can be applied to many other settings.
UWSOD: Toward Fully-Supervised-Level Capacity Weakly Supervised Object Detection

Weakly supervised object detection (WSOD) has attracted extensive research attention due to its great ﬂexibility of exploiting large-scale dataset with only image-level annotations for detector training. Despite its great advance in recent years, WSOD still suffers limited performance, which is far below that of fully supervised object detection (FSOD). As most WSOD methods depend on object proposal algorithms to generate candidate regions and are also confronted with challenges like low-quality predicted bounding boxes and large scale variation. In this paper, we propose a uniﬁed WSOD framework, termed UWSOD, to develop a high-capacity general detection model with only image-level labels, which is self-contained and does not require external modules or additional supervision. To this end, we exploit three important components, i.e. , object proposal generation, bounding-box ﬁne-tuning and scale-invariant features. First, we propose an anchor-based self-supervised proposal generator to hypothesize object locations, which is trained end-to-end with supervision created by UWSOD for both objectness classiﬁcation and regression. Second, we develop a step-wise bounding-box ﬁne-tuning to reﬁne both detection scores and coordinates by progressively select high-conﬁdence object proposals as positive samples, which bootstraps the quality of predicted bounding boxes. Third, we construct a multi-rate resampling pyramid to aggregate multi-scale contextual information, which is the ﬁrst in-network feature hierarchy to handle scale variation in WSOD. Extensive experiments on PASCAL VOC and MS COCO show that the proposed UWSOD achieves competitive results with the state-of-the-art WSOD methods while not requiring external modules or additional supervision. Moreover, the upper-bound performance of UWSOD with class-agnostic ground-truth bounding boxes approaches Faster R-CNN, which demonstrates UWSOD has fully-supervised-level capacity. The code is available at: https://github.com/shenyunhang/UWSOD .
Task-Oriented Feature Distillation

Feature distillation, a primary method in knowledge distillation, always leads to signiﬁcant accuracy improvements. Most existing methods distill features in the teacher network through a manually designed transformation. In this paper, we propose a novel distillation method named task-oriented feature distillation (TOFD) where the transformation is convolutional layers that are trained in a data-driven manner by task loss. As a result, the task-oriented information in the features can be captured and distilled to students. Moreover, an orthogonal loss is applied to the feature resizing layer in TOFD to improve the performance of knowledge distillation. Experiments show that TOFD outperforms other distillation methods by a large margin on both image classiﬁcation and 3D classiﬁcation tasks. Codes have been released in Github 3 .
HM-ANN: Efficient Billion-Point Nearest Neighbor Search on Heterogeneous Memory

The state-of-the-art approximate nearest neighbor search (ANNS) algorithms face a fundamental tradeoff between query latency and accuracy, because of small main memory capacity: To store indices in main memory for fast query response, They have to limit the number of data points or store compressed vectors, which hurts search accuracy. The emergence of heterogeneous memory (HM) brings opportunities to largely increase memory capacity and break the above tradeoff: Using HM, billions of data points can be placed in main memory on a single machine without using any data compression. However, HM consists of both fast (but small) memory and slow (but large) memory, and using HM inappropriately slows down query time significantly. In this work, we present a novel graph-based similarity search algorithm called HM-ANN, which takes both memory and data heterogeneity into consideration and enables billion-scale similarity search on a single node without using compression. On two billion-sized datasets BIGANN and DEEP1B, HM-ANN outperforms state-of-the-art compression-based solutions such as L&C and IMI+OPQ in recall-vs-latency by a large margin, obtaining 46% higher recall under the same search latency. We also extend existing graph-based methods such as HNSW and NSG with two strong baseline implementations on HM. At billion-point scale, HM-ANN is 2X and 5.8X faster than our HNSW and NSG baselines respectively to reach the same accuracy.
Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations

Many science, engineering, and design optimization problems require balancing the trade-offs between several conflicting objectives. The objectives are often blackbox functions whose evaluations are time-consuming and costly. Multi-objective Bayesian optimization can be used to automate the process of discovering the set of optimal solutions, called Pareto-optimal, while minimizing the number of performed evaluations. To further reduce the evaluation time in the optimization process, testing of several samples in parallel can be deployed. We propose a novel multi-objective Bayesian optimization algorithm that iteratively selects the best batch of samples to be evaluated in parallel. Our algorithm approximates and analyzes a piecewise-continuous Pareto set representation. This representation allows us to introduce a batch selection strategy that optimizes for both hypervolume improvement and diversity of selected samples in order to efficiently advance promising regions of the Pareto front. Experiments on both synthetic test functions and real-world benchmark problems show that our algorithm predominantly outperforms relevant state-of-the-art methods. The code is available at https://github.com/yunshengtian/DGEMO.
PLANS: Neuro-Symbolic Program Learning from Videos

Recent years have seen the rise of statistical program learning based on neural models as an alternative to traditional rule-based systems for programming by example. Rule-based approaches offer correctness guarantees in an unsupervised way as they inherently capture logical rules, while neural models are more realistically scalable to raw, high-dimensional input, and provide resistance to noisy I/O speciﬁcations. We introduce PLANS (Program LeArning from Neurally inferred Speciﬁcations), a hybrid model for program synthesis from visual observations that gets the best of both worlds, relying on (i) a neural architecture trained to extract abstract, high-level information from each raw individual input (ii) a rule-based system using the extracted information as I/O speciﬁcations to synthesize a program capturing the different observations. In order to address the key challenge of making PLANS resistant to noise in the network’s output, we introduce a dynamic ﬁltering algorithm for I/O speciﬁcations based on selective classiﬁcation techniques. We obtain state-of-the-art performance at program synthesis from diverse demonstration videos in the Karel and ViZDoom environments, while requiring no ground-truth program for training.
Model Class Reliance for Random Forests

Variable Importance (VI) has traditionally been cast as the process of estimating each variable’s contribution to a predictive model’s overall performance. Analysis of a single model instance, however, guarantees no insight into a variables relevance to underlying generative processes. Recent research has sought to address this concern via analysis of Rashomon sets - sets of alternative model instances that exhibit equivalent predictive performance to some reference model, but which take different functional forms. Measures such as Model Class Reliance (MCR) have been proposed, that are computed against Rashomon sets, in order to ascertain how much a variable must be relied on to make robust predictions, or whether alternatives exist. If MCR range is tight, we have no choice but to use a variable; if range is high then there exists competing, perhaps fairer models, that provide alternative explanations of the phenomena being examined. Applications are wide, from enabling construction of ‘fairer’ models in areas such as recidivism to health analytics and ethical marketing. Tractable estimation of MCR for non-linear models is currently restricted to Kernel Regression under squared loss [7]. In this paper we introduce a new technique that extends computation of Model Class Reliance (MCR) to Random Forest classiﬁers and regressors. The proposed approach addresses a number of open research questions, and in contrast to prior Kernel SVM MCR estimation, runs in linearithmic rather than polynomial time. Taking a fundamentally different approach to previous work, we provide a solution for this important model class, identifying situations where irrelevant covariates do not improve predictions.
Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point

In this paper, we explore the limits of Microsoft Floating Point (MSFP), a new class of datatypes developed for production cloud-scale inferencing on custom hardware. Through the co-evolution of hardware design and algorithms, MSFP achieves accuracy comparable to or better than industry standards Bfloat16 and INT8 at 3x and 4x lower cost, respectively. MSFP incurs negligible impact to accuracy (
A User Biology Preference Prediction Model Based on the Perceptual Evaluations of Designers for Biologically Inspired Design

Biology provides a rich and novel source of inspiration for product design. An increasing number of industrial designers are gaining inspiration from nature, producing creative products by extracting, classifying, and reconstructing biological features. However, the current process of gaining biological inspiration is still limited by the prior knowledge and experience of designers, so it is necessary to investigate the designer’s perception of biological features. Herein, we investigate designer perceptions of bionic object features based on Kansei engineering, achieving a highly comprehensive structured expression of biological features forming five dimensions—Overall Feeling, Ability and Trait, Color and Texture, Apparent Tactile Sensation, and Structural Features—using factor analysis. Further, producing creative design solutions with a biologically inspired design (BID) has a risk of failing to meet user preferences and market needs. A user preference prediction support tool may address this bottleneck. We examine user preference by questionnaire and explore its association with the perceptual evaluation of designers, obtaining a user preference prediction model by conducting multiple linear regression analysis. This provides a statistical model for identifying the relative weighting of the perception dimensions of each designer in the user preference for an animal, giving the degree of contribution to the user preference. The experiment results show that the dimension “Overall Feeling” of the designer perception is positively correlated with the “like” level of the user preference and negatively correlated with the “dislike” level of the user preference, indicating that this prediction model bridges the gap caused by the asymmetry between designers and users by matching the designer perception and user preference. To a certain extent, this research solves the problems associated with the cognitive limitations of designers and the differences between designers and users, facilitating the use of biological features in product design and thereby enhancing the market importance of BID schemes.