bluuebunny/crossref_metadata_2025 huggingface dataset:
Efficient Mirror Detection via Multi-Level Heterogeneous Learning
Authors: He Ruozhen , Lin Jiaying , W.H. Lau Rynson
Published: June 2023

We present HetNet (Multi-level Heterogeneous Network), a highly efficient mirror detection network. Current mirror detection methods focus more on performance than efficiency, limiting the real-time applications (such as drones). Their lack of efficiency is aroused by the common design of adopting homogeneous modules at different levels, which ignores the difference between different levels of features. In contrast, HetNet detects potential mirror regions initially through low-level understandings (e.g., intensity contrasts) and then combines with high-level understandings (contextual discontinuity for instance) to finalize the predictions. To perform accurate yet efficient mirror detection, HetNet follows an effective architecture that obtains specific information at different stages to detect mirrors. We further propose a multi-orientation intensity-based contrasted module (MIC) and a reflection semantic logical module (RSL), equipped on HetNet, to predict potential mirror regions by low-level understandings and analyze semantic logic in scenarios by high-level understandings, respectively. Compared to the state-of-the-art method, HetNet runs 664% faster and draws an average performance gain of 8.9% on MAE, 3.1% on IoU, and 2.0% on F-measure on two mirror detection benchmarks. The code is available at https://github.com/Catherine-R-He/HetNet.
Resolving Task Confusion in Dynamic Expansion Architectures for Class Incremental Learning
Authors: Huang Bingchen , Chen Zhineng , Zhou Peng , Chen Jiayin , Wu Zuxuan
Published: June 2023

The dynamic expansion architecture is becoming popular in class incremental learning, mainly due to its advantages in alleviating catastrophic forgetting. However, task confu- sion is not well assessed within this framework, e.g., the discrepancy between classes of different tasks is not well learned (i.e., inter-task confusion, ITC), and certain prior- ity is still given to the latest class batch (i.e., old-new con- fusion, ONC). We empirically validate the side effects of the two types of confusion. Meanwhile, a novel solution called Task Correlated Incremental Learning (TCIL) is pro- posed to encourage discriminative and fair feature utilization across tasks. TCIL performs a multi-level knowledge distil- lation to propagate knowledge learned from old tasks to the new one. It establishes information flow paths at both fea- ture and logit levels, enabling the learning to be aware of old classes. Besides, attention mechanism and classifier re- scoring are applied to generate more fair classification scores. We conduct extensive experiments on CIFAR100 and Ima- geNet100 datasets. The results demonstrate that TCIL con- sistently achieves state-of-the-art accuracy. It mitigates both ITC and ONC, while showing advantages in battle with catas- trophic forgetting even no rehearsal memory is reserved. Source code: https://github.com/YellowPancake/TCIL.
Boosting Point Clouds Rendering via Radiance Mapping
Authors: Huang Xiaoyang , Zhang Yi , Ni Bingbing , Li Teng , Chen Kai , Zhang Wenjun
Published: June 2023

Recent years we have witnessed rapid development in NeRF-based image rendering due to its high quality. However, point clouds rendering is somehow less explored. Compared to NeRF-based rendering which suffers from dense spatial sampling, point clouds rendering is naturally less computation intensive, which enables its deployment in mobile computing device. In this work, we focus on boosting the image quality of point clouds rendering with a compact model design. We first analyze the adaption of the volume rendering formulation on point clouds. Based on the analysis, we simplify the NeRF representation to a spatial mapping function which only requires single evaluation per pixel. Further, motivated by ray marching, we rectify the the noisy raw point clouds to the estimated intersection between rays and surfaces as queried coordinates, which could avoid spatial frequency collapse and neighbor point disturbance. Composed of rasterization, spatial mapping and the refinement stages, our method achieves the state-of-the-art performance on point clouds rendering, outperforming prior works by notable margins, with a smaller model size. We obtain a PSNR of 31.74 on NeRF-Synthetic, 25.88 on ScanNet and 30.81 on DTU. Code and data are publicly available in https://github.com/seanywang0408/RadianceMapping.
Semi-attention Partition for Occluded Person Re-identification
Authors: Jia Mengxi , Sun Yifan , Zhai Yunpeng , Cheng Xinhua , Yang Yi , Li Ying
Published: June 2023

This paper proposes a Semi-Attention Partition (SAP) method to learn well-aligned part features for occluded person re-identification (re-ID). Currently, the mainstream methods employ either external semantic partition or attention-based partition, and the latter manner is usually better than the former one. Under this background, this paper explores a potential that the weak semantic partition can be a good teacher for the strong attention-based partition. In other words, the attention-based student can substantially surpass its noisy semantic-based teacher, contradicting the common sense that the student usually achieves inferior (or comparable) accuracy. A key to this effect is: the proposed SAP encourages the attention-based partition of the (transformer) student to be partially consistent with the semantic-based teacher partition through knowledge distillation, yielding the so-called semi-attention. Such partial consistency allows the student to have both consistency and reasonable conflict with the noisy teacher. More specifically, on the one hand, the attention is guided by the semantic partition from the teacher. On the other hand, the attention mechanism itself still has some degree of freedom to comply with the inherent similarity between different patches, thus gaining resistance against noisy supervision. Moreover, we integrate a battery of well-engineered designs into SAP to reinforce their cooperation (e.g., multiple forms of teacher-student consistency), as well as to promote reasonable conflict (e.g., mutual absorbing partition refinement and a supervision signal dropout strategy). Experimental results confirm that the transformer student achieves substantial improvement after this semi-attention learning scheme, and produces new state-of-the-art accuracy on several standard re-ID benchmarks.
Fourier-Net: Fast Image Registration with Band-Limited Deformation
Authors: Jia Xi , Bartlett Joseph , Chen Wei , Song Siyang , Zhang Tianyang , Cheng Xinxing , Lu Wenqi , Qiu Zhaowen , Duan Jinming
Published: June 2023

Unsupervised image registration commonly adopts U-Net style networks to predict dense displacement fields in the full-resolution spatial domain. For high-resolution volumetric image data, this process is however resource-intensive and time-consuming. To tackle this problem, we propose the Fourier-Net, replacing the expansive path in a U-Net style network with a parameter-free model-driven decoder. Specifically, instead of our Fourier-Net learning to output a full-resolution displacement field in the spatial domain, we learn its low-dimensional representation in a band-limited Fourier domain. This representation is then decoded by our devised model-driven decoder (consisting of a zero padding layer and an inverse discrete Fourier transform layer) to the dense, full-resolution displacement field in the spatial domain. These changes allow our unsupervised Fourier-Net to contain fewer parameters and computational operations, resulting in faster inference speeds. Fourier-Net is then evaluated on two public 3D brain datasets against various state-of-the-art approaches. For example, when compared to a recent transformer-based method, named TransMorph, our Fourier-Net, which only uses 2.2% of its parameters and 6.66% of the multiply-add operations, achieves a 0.5% higher Dice score and an 11.48 times faster inference speed. Code is available at https://github.com/xi-jia/Fourier-Net.
Multispectral Invisible Coating: Laminated Visible-Thermal Physical Attack against Multispectral Object Detectors Using Transparent Low-E Films
Authors: Kim Taeheon , Yu Youngjoon , Ro Yong Man
Published: June 2023

Multispectral object detection plays a vital role in safety-critical vision systems that require an around-the-clock operation and encounter dynamic real-world situations(e.g., self-driving cars and autonomous surveillance systems). Despite its crucial competence in safety-related applications, its security against physical attacks is severely understudied. We investigate the vulnerability of multispectral detectors against physical attacks by proposing a new physical method: Multispectral Invisible Coating. Utilizing transparent Low-e films, we realize a laminated visible-thermal physical attack by attaching Low-e films over a visible attack printing. Moreover, we apply our physical method to manufacture a Multispectral Invisible Suit that hides persons from the multiple view angles of Multispectral detectors. To simulate our attack under various surveillance scenes, we constructed a large-scale multispectral pedestrian dataset which we will release in public. Extensive experiments show that our proposed method effectively attacks the state-of-the-art multispectral detector both in the digital space and the physical world.
Symbolic Replay: Scene Graph as Prompt for Continual Learning on VQA Task
Authors: Lei Stan Weixian , Gao Difei , Wu Jay Zhangjie , Wang Yuxuan , Liu Wei , Zhang Mengmi , Shou Mike Zheng
Published: June 2023

VQA is an ambitious task aiming to answer any image-related question. However, in reality, it is hard to build such a system once for all since the needs of users are continuously updated, and the system has to implement new functions. Thus, Continual Learning (CL) ability is a must in developing advanced VQA systems. Recently, a pioneer work split a VQA dataset into disjoint answer sets to study this topic. However, CL on VQA involves not only the expansion of label sets (new Answer sets). It is crucial to study how to answer questions when deploying VQA systems to new environments (new Visual scenes) and how to answer questions requiring new functions (new Question types). Thus, we propose CLOVE, a benchmark for Continual Learning On Visual quEstion answering, which contains scene- and function-incremental settings for the two aforementioned CL scenarios. In terms of methodology, the main difference between CL on VQA and classification is that the former additionally involves expanding and preventing forgetting of reasoning mechanisms, while the latter focusing on class representation. Thus, we propose a real-data-free replay-based method tailored for CL on VQA, named Scene Graph as Prompt for Symbolic Replay. Using a piece of scene graph as a prompt, it replays pseudo scene graphs to represent the past images, along with correlated QA pairs. A unified VQA model is also proposed to utilize the current and replayed data to enhance its QA ability. Finally, experimental results reveal challenges in CLOVE and demonstrate the effectiveness of our method. Code and data
are available at https://github.com/showlab/CLVQA.
Frequency Domain Disentanglement for Arbitrary Neural Style Transfer
Authors: Li Dongyang , Luo Hao , Wang Pichao , Wang Zhibin , Liu Shang , Wang Fan
Published: June 2023

Arbitrary neural style transfer has been a popular research topic due to its rich application scenarios. Effective disentanglement of content and style is the critical factor for synthesizing an image with arbitrary style. The existing methods focus on disentangling feature representations of content and style in the spatial domain where the content and style components are innately entangled and difficult to be disentangled clearly. Therefore, these methods always suffer from low-quality results because of the sub-optimal disentanglement. To address such a challenge, this paper proposes the frequency mixer (FreMixer) module that disentangles and re-entangles the frequency spectrum of content and style components in the frequency domain. Since content and style components have different frequency-domain characteristics (frequency bands and frequency patterns), the FreMixer could well disentangle these two components. Based on the FreMixer module, we design a novel Frequency Domain Disentanglement (FDD) framework for arbitrary neural style transfer. Qualitative and quantitative experiments verify that the proposed method can render better stylized results compared to the state-of-the-art methods.
Real-World Deep Local Motion Deblurring
Authors: Li Haoying , Zhang Ziran , Jiang Tingting , Luo Peng , Feng Huajun , Xu Zhihai
Published: June 2023

Most existing deblurring methods focus on removing global blur caused by camera shake, while they cannot well handle local blur caused by object movements. To fill the vacancy of local deblurring in real scenes, we establish the first real local motion blur dataset (ReLoBlur), which is captured by a synchronized beam-splitting photographing system and corrected by a post-progressing pipeline. Based on ReLoBlur, we propose a Local Blur-Aware Gated network (LBAG) and several local blur-aware techniques to bridge the gap between global and local deblurring: 1) a blur detection approach based on background subtraction to localize blurred regions; 2) a gate mechanism to guide our network to focus on blurred regions; and 3) a blur-aware patch cropping strategy to address data imbalance problem. Extensive experiments prove the reliability of ReLoBlur dataset, and demonstrate that LBAG achieves better performance than state-of-the-art global deblurring methods and our proposed local blur-aware techniques are effective.
Spatial-Spectral Transformer for Hyperspectral Image Denoising
Authors: Li Miaoyu , Fu Ying , Zhang Yulun
Published: June 2023

Hyperspectral image (HSI) denoising is a crucial preprocessing procedure for the subsequent HSI applications. Unfortunately, though witnessing the development of deep learning in HSI denoising area, existing convolution-based methods face the trade-off between computational efficiency and capability to model non-local characteristics of HSI. In this paper, we propose a Spatial-Spectral Transformer (SST) to alleviate this problem. To fully explore intrinsic similarity characteristics in both spatial dimension and spectral dimension, we conduct non-local spatial self-attention and global spectral self-attention with Transformer architecture. The window-based spatial self-attention focuses on the spatial similarity beyond the neighboring region. While, the spectral self-attention exploits the long-range dependencies between highly correlative bands. Experimental results show that our proposed method outperforms the state-of-the-art HSI denoising methods in quantitative quality and visual results. The code is released at https://github.com/MyuLi/SST.
The Multi-Agent Transportation Problem
Authors: Bachor Pascal , Bergdoll Rolf-David , Nebel Bernhard
Published: June 2023

We introduce the multi-agent transportation (MAT) problem, where agents have to transport containers from their starting positions to their designated goal positions. Movement takes place in a common environment where collisions between agents and between containers must be avoided.
In contrast to other frameworks such as multi-agent pathfinding (MAPF) or multi-agent pickup and delivery (MAPD), the agents are allowed to separate from the containers at any time, which can reduce the makespan and also allows for plans in scenarios that are unsolvable otherwise.
We present a complexity analysis establishing the problem's NP-completeness and show how the problem can be reduced to a sequence of SAT problems when optimizing for makespan.
A MAT solver is empirically evaluated with regard to varying input characteristics and movement constraints and compared to a MAPD solver that utilizes conflict-based search (CBS).
Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition
Authors: Liu Shunyu , Zhou Yihe , Song Jie , Zheng Tongya , Chen Kaixuan , Zhu Tongtian , Feng Zunlei , Song Mingli
Published: June 2023

Value Decomposition (VD) aims to deduce the contributions of agents for decentralized policies in the presence of only global rewards, and has recently emerged as a powerful credit assignment paradigm for tackling cooperative Multi-Agent Reinforcement Learning (MARL) problems. One of the main challenges in VD is to promote diverse behaviors among agents, while existing methods directly encourage the diversity of learned agent networks with various strategies. However, we argue that these dedicated designs for agent networks are still limited by the indistinguishable VD network, leading to homogeneous agent behaviors and thus downgrading the cooperation capability. In this paper, we propose a novel Contrastive Identity-Aware learning (CIA) method, explicitly boosting the credit-level distinguishability of the VD network to break the bottleneck of multi-agent diversity. Specifically, our approach leverages contrastive learning to maximize the mutual information between the temporal credits and identity representations of different agents, encouraging the full expressiveness of credit assignment and further the emergence of individualities. The algorithm implementation of the proposed CIA module is simple yet effective that can be readily incorporated into various VD architectures. Experiments on the SMAC benchmarks and across different VD backbones demonstrate that the proposed method yields results superior to the state-of-the-art counterparts. Our code is available at https://github.com/liushunyu/CIA.
LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfinding
Authors: Okumura Keisuke
Published: June 2023

We propose a novel complete algorithm for multi-agent pathfinding (MAPF) called lazy constraints addition search for MAPF (LaCAM). MAPF is a problem of finding collision-free paths for multiple agents on graphs and is the foundation of multi-robot coordination. LaCAM uses a two-level search to find solutions quickly, even with hundreds of agents or more. At the low-level, it searches constraints about agents' locations. At the high-level, it searches a sequence of all agents' locations, following the constraints specified by the low-level. Our exhaustive experiments reveal that LaCAM is comparable to or outperforms state-of-the-art sub-optimal MAPF algorithms in a variety of scenarios, regarding success rate, planning time, and solution quality of sum-of-costs.
Effective Integration of Weighted Cost-to-Go and Conflict Heuristic within Suboptimal CBS
Authors: Veerapaneni Rishi , Kusnur Tushar , Likhachev Maxim
Published: June 2023

Conflict-Based Search (CBS) is a popular multi-agent path finding (MAPF) solver that employs a low-level single agent planner and a high-level constraint tree to resolve conflicts. The vast majority of modern MAPF solvers focus on improving CBS by reducing the size of this tree through various strategies with few methods modifying the low level planner. Typically low level planners in existing CBS methods use an unweighted cost-to-go heuristic, with suboptimal CBS methods also using a conflict heuristic to help the high level search. In this paper, we show that, contrary to prevailing CBS beliefs, a weighted cost-to-go heuristic can be used effectively alongside the conflict heuristic in two possible variants. In particular, one of these variants can obtain large speedups, 2-100x, across several scenarios and suboptimal CBS methods. Importantly, we discover that performance is related not to the weighted cost-to-go heuristic but rather to the relative conflict heuristic weight's ability to effectively balance low-level and high-level work. Additionally, to the best of our knowledge, we show the first theoretical relation of prioritized planning and bounded suboptimal CBS and demonstrate that our methods are their natural generalization.
Hierarchical Mean-Field Deep Reinforcement Learning for Large-Scale Multiagent Systems
Authors: Yu Chao
Published: June 2023

Learning for efficient coordination in large-scale multiagent systems suffers from the problem of the curse of dimensionality due to the exponential growth of agent interactions. Mean-Field (MF)-based methods address this issue by transforming the interactions within the whole system into a single agent played with the average effect of its neighbors. However, considering the neighbors merely by their average may ignore the varying influences of each neighbor, and learning with this kind of local average effect would likely lead to inferior system performance due to lack of an efficient coordination mechanism in the whole population level. In this work, we propose a Hierarchical Mean-Field (HMF) learning framework to further improve the performance of existing MF methods. The basic idea is to approximate the average effect for a sub-group of agents by considering their different influences within the sub-group, and realize population-level coordination through the interactions among different sub-groups. Empirical studies show that HMF significantly outperforms existing baselines on both challenging cooperative and mixed cooperative-competitive tasks with different scales of agent populations.
Robust Multi-Agent Coordination via Evolutionary Generation of Auxiliary Adversarial Attackers
Authors: Yuan Lei , Zhang Ziqian , Xue Ke , Yin Hao , Chen Feng , Guan Cong , Li Lihe , Qian Chao , Yu Yang
Published: June 2023

Cooperative Multi-agent Reinforcement Learning (CMARL) has shown to be promising for many real-world applications. Previous works mainly focus on improving coordination ability via solving MARL-specific challenges (e.g., non-stationarity, credit assignment, scalability), but ignore the policy perturbation issue when testing in a different environment. This issue hasn't been considered in problem formulation or efficient algorithm design. To address this issue, we firstly model the problem as a Limited Policy Adversary Dec-POMDP (LPA-Dec-POMDP), where some coordinators from a team might accidentally and unpredictably encounter a limited number of malicious action attacks, but the regular coordinators still strive for the intended goal. Then, we propose Robust Multi-Agent Coordination via Evolutionary Generation of Auxiliary Adversarial Attackers (ROMANCE), which enables the trained policy to encounter diversified and strong auxiliary adversarial attacks during training, thus achieving high robustness under various
policy perturbations. Concretely, to avoid the ego-system overfitting to a specific attacker, we maintain a set of attackers, which is optimized to guarantee the attackers high attacking quality and behavior diversity. The goal of quality is to minimize the ego-system coordination effect, and a novel diversity regularizer based on sparse action is applied to diversify the behaviors among attackers. The ego-system is then paired with a population of attackers selected from the maintained attacker set, and alternately trained against the constantly evolving attackers. Extensive experiments on multiple scenarios from SMAC indicate our ROMANCE provides comparable or better robustness and generalization ability than other baselines.
Learning to Play General-Sum Games against Multiple Boundedly Rational Agents
Authors: Zhao Eric , Trott Alexander R. , Xiong Caiming , Zheng Stephan
Published: June 2023

We study the problem of training a principal in a multi-agent general-sum game using reinforcement learning (RL). Learning a robust principal policy requires anticipating the worst possible strategic responses of other agents, which is generally NP-hard. However, we show that no-regret dynamics can identify these worst-case responses in poly-time in smooth games. We propose a framework that uses this policy evaluation method for efficiently learning a robust principal policy using RL. This framework can be extended to provide robustness to boundedly rational agents too. Our motivating application is automated mechanism design: we empirically demonstrate our framework learns robust mechanisms in both matrix games and complex spatiotemporal games. In particular, we learn a dynamic tax policy that improves the welfare of a simulated trade-and-barter economy by 15%, even when facing previously unseen boundedly rational RL taxpayers.
Towards Robust Metrics for Concept Representation Evaluation
Authors: Espinosa Zarlenga Mateo , Barbiero Pietro , Shams Zohreh , Kazhdan Dmitry , Bhatt Umang , Weller Adrian , Jamnik Mateja
Published: June 2023

Recent work on interpretability has focused on concept-based explanations, where deep learning models are explained in terms of high-level units of information, referred to as concepts. Concept learning models, however, have been shown to be prone to encoding impurities in their representations, failing to fully capture meaningful features of their inputs. While concept learning lacks metrics to measure such phenomena, the field of disentanglement learning has explored the related notion of underlying factors of variation in the data, with plenty of metrics to measure the purity of such factors. In this paper, we show that such metrics are not appropriate for concept learning and propose novel metrics for evaluating the purity of concept representations in both approaches. We show the advantage of these metrics over existing ones and demonstrate their utility in evaluating the robustness of concept representations and interventions performed on them. In addition, we show their utility for benchmarking state-of-the-art methods from both families and find that, contrary to common assumptions, supervision alone may not be sufficient for pure concept representations.
On the Vulnerability of Backdoor Defenses for Federated Learning
Authors: Fang Pei , Chen Jinghui
Published: June 2023

Federated learning (FL) is a popular distributed machine learning paradigm which enables jointly training a global model without sharing clients' data. However, its repetitive server-client communication gives room for possible backdoor attacks which aims to mislead the global model into a targeted misprediction when a specific trigger pattern is presented. In response to such backdoor threats on federated learning, various defense measures have been proposed. In this paper, we study whether the current defense mechanisms truly neutralize the backdoor threats from federated learning in a practical setting by proposing a new federated backdoor attack framework for possible countermeasures. Different from traditional training (on triggered data) and rescaling (the malicious client model) based backdoor injection, the proposed backdoor attack framework (1) directly modifies (a small proportion of) local model weights to inject the backdoor trigger via sign flips; (2) jointly optimize the trigger pattern with the client model, thus is more persistent and stealthy for circumventing existing defenses. In a case study, we examine the strength and weaknesses of several recent federated backdoor defenses from three major categories and provide suggestions to the practitioners when training federated models in practice.
Distributionally Robust Optimization with Probabilistic Group
Authors: Ghosal Soumya Suvra , Li Yixuan
Published: June 2023

Modern machine learning models may be susceptible to learning spurious correlations that hold on average but not for the atypical group of samples. To address the problem, previous approaches minimize the empirical worst-group risk. Despite the promise, they often assume that each sample belongs to one and only one group, which does not allow expressing the uncertainty in group labeling. In this paper, we propose a novel framework PG-DRO, which explores the idea of probabilistic group membership for distributionally robust optimization. Key to our framework, we consider soft group membership instead of hard group annotations. The group probabilities can be flexibly generated using either supervised learning or zero-shot approaches. Our framework accommodates samples with group membership ambiguity, offering stronger flexibility and generality than the prior art. We comprehensively evaluate PG-DRO on both image classification and natural language processing benchmarks, establishing superior performance.
Explaining Model Confidence Using Counterfactuals
Authors: Le Thao , Miller Tim , Singh Ronal , Sonenberg Liz
Published: June 2023

Displaying confidence scores in human-AI interaction has been shown to help build trust between humans and AI systems. However, most existing research uses only the confidence score as a form of communication. As confidence scores are just another model output, users may want to understand why the algorithm is confident to determine whether to accept the confidence score. In this paper, we show that counterfactual explanations of confidence scores help study participants to better understand and better trust a machine learning model's prediction. We present two methods for understanding model confidence using counterfactual explanation: (1) based on counterfactual examples; and (2) based on visualisation of the counterfactual space. Both increase understanding and trust for study participants over a baseline of no explanation, but qualitative results show that they are used quite differently, leading to recommendations of when to use each one and directions of designing better explanations.
XRand: Differentially Private Defense against Explanation-Guided Attacks
Authors: Nguyen Truc , Lai Phung , Phan Hai , Thai My T.
Published: June 2023

Recent development in the field of explainable artificial intelligence (XAI) has helped improve trust in Machine-Learning-as-a-Service (MLaaS) systems, in which an explanation is provided together with the model prediction in response to each query. However, XAI also opens a door for adversaries to gain insights into the black-box models in MLaaS, thereby making the models more vulnerable to several attacks. For example, feature-based explanations (e.g., SHAP) could expose the top important features that a black-box model focuses on. Such disclosure has been exploited to craft effective backdoor triggers against malware classifiers. To address this trade-off, we introduce a new concept of achieving local differential privacy (LDP) in the explanations, and from that we establish a defense, called XRand, against such attacks. We show that our mechanism restricts the information that the adversary can learn about the top important features, while maintaining the faithfulness of the explanations.
Equity Promotion in Public Transportation
Authors: Pramanik Anik , Xu Pan , Xu Yifan
Published: June 2023

There are many news articles reporting the obstacles confronting poverty-stricken households in access to public transits. These barriers create a great deal of inconveniences for these impoverished families and more importantly, they contribute a lot of social inequalities. A typical approach addressing the issue is to build more transport infrastructure to offer more opportunities to access the public transits especially for those deprived communities. Examples include adding more bus lines connecting needy residents to railways systems and extending existing bus lines to areas with low socioeconomic status. Recently, a new strategy is proposed, which is to harness the ubiquitous ride-hailing services to connect disadvantaged households with the nearest public transportations. Compared with the former infrastructure-based solution, the ride-hailing-based strategy enjoys a few exclusive benefits such as higher effectiveness and more flexibility.

In this paper, we propose an optimization model to study how to integrate the two approaches together for equity-promotion purposes. Specifically, we aim to design a strategy of allocating a given limited budget to different candidate programs such that the overall social equity is maximized, which is defined as the minimum covering ratio among all pre-specified protected groups of households (based on race, income, etc.). We have designed a linear-programming (LP) based rounding algorithm, which proves to achieve an optimal approximation ratio of 1-1/e. Additionally, we test our algorithm against a few baselines on real data assembled by outsourcing multiple public datasets collected in the city of Chicago. Experimental results confirm our theoretical predictions and demonstrate the effectiveness of our LP-based strategy in promoting social equity, especially when the budget is insufficient.
Expressive Optimal Temporal Planning via Optimization Modulo Theory
Authors: Panjkovic Stefan , Micheli Andrea
Published: June 2023

Temporal Planning is the problem of synthesizing a course of actions given a predictive model of a system subject to temporal constraints. This kind of planning finds natural applications in the automation of industrial processes and in robotics when the timing and deadlines are important. Finding any plan in temporal planning is often not enough as it is sometimes needed to optimize a certain objective function: particularly interesting are the minimization of the makespan and the optimization of the costs of actions. Despite the importance of the problem, only few works in the literature tackled the problem of optimal temporal planning because of the complicated intermix of planning and scheduling.
In this paper, we address the problem of optimal temporal planning for a very expressive class of problems using a reduction of the bounded planning problem to Optimization Modulo Theory (OMT) a powerful discrete/continuous optimization framework. We theoretically and empirically show the expressive power of this approach and we set a baseline for future research in this area.
A Simple Unified Approach to Testing High-Dimensional Conditional Independences for Categorical and Ordinal Data
Authors: Ankan Ankur , Textor Johannes
Published: June 2023

Conditional independence (CI) tests underlie many approaches to model testing and structure learning in causal inference. Most existing CI tests for categorical and ordinal data stratify the sample by the conditioning variables, perform simple independence tests in each stratum, and combine the results. Unfortunately, the statistical power of this approach degrades rapidly as the number of conditioning variables increases. Here we propose a simple unified CI test for ordinal and categorical data that maintains reasonable calibration and power in high dimensions. We show that our test outperforms existing baselines in model testing and structure learning for dense directed graphical models while being comparable for sparse models. Our approach could be attractive for causal model testing because it is easy to implement, can be used with non-parametric or parametric probability models, has the symmetry property, and has reasonable computational requirements.
Neural Diffeomorphic Non-uniform B-spline Flows
Authors: Hong Seongmin , Chun Se Young
Published: June 2023

Normalizing flows have been successfully modeling a complex probability distribution as an invertible transformation of a simple base distribution. However, there are often applications that require more than invertibility. For instance, the computation of energies and forces in physics requires the second derivatives of the transformation to be well-defined and continuous. Smooth normalizing flows employ infinitely differentiable transformation, but with the price of slow non-analytic inverse transforms. In this work, we propose diffeomorphic non-uniform B-spline flows that are at least twice continuously differentiable while bi-Lipschitz continuous, enabling efficient parametrization while retaining analytic inverse transforms based on a sufficient condition for diffeomorphism. Firstly, we investigate the sufficient condition for C(k-2)-diffeomorphic non-uniform kth-order B-spline transformations. Then, we derive an analytic inverse transformation of the non-uniform cubic B-spline transformation for neural diffeomorphic non-uniform B-spline flows. Lastly, we performed experiments on solving the force matching problem in Boltzmann generators, demonstrating that our C2-diffeomorphic non-uniform B-spline flows yielded solutions better than previous spline flows and faster than smooth normalizing flows. Our source code is publicly available at https://github.com/smhongok/Non-uniform-B-spline-Flow.
Identification and Estimation of the Probabilities of Potential Outcome Types Using Covariate Information in Studies with Non-compliance
Authors: Kawakami Yuta , Shingaki Ryusei , Kuroki Manabu
Published: June 2023

We propose novel identification conditions and a statistical estimation method for the probabilities of potential outcome types using covariate information in randomized trials in which the treatment assignment is randomized but subject compliance is not perfect. Different from existing studies, the proposed identification conditions do not require strict assumptions such as the assumption of monotonicity. When the probabilities of potential outcome types are identifiable through the proposed conditions, the problem of estimating the probabilities of potential outcome types is reduced to that of singular models. Thus, the probabilities cannot be evaluated using standard statistical likelihood-based estimation methods. Rather, the proposed identification conditions show that we can derive consistent estimators of the probabilities of potential outcome types via the method of moments, which leads to the asymptotic normality of the proposed estimators through the delta method under regular conditions. We also propose a new statistical estimation method based on the bounded constrained augmented Lagrangian method to derive more efficient estimators than can be derived through the method of moments.
Differentially Private Nonlinear Causal Discovery from Numerical Data
Authors: Zhang Hao , Xia Yewei , Ren Yixin , Guan Jihong , Zhou Shuigeng
Published: June 2023

Recently, several methods such as private ANM, EM-PC and Priv-PC have been proposed to perform differentially private causal discovery in various scenarios including bivariate, multivariate Gaussian and categorical cases. However, there is little effort on how to conduct private nonlinear causal discovery from numerical data. This work tries to challenge this problem. To this end, we propose a method to infer nonlinear causal relations from observed numerical data by using regression-based conditional independence test (RCIT) that consists of kernel ridge regression (KRR) and Hilbert-Schmidt independence criterion (HSIC) with permutation approximation. Sensitivity analysis for RCIT is given and a private constraint-based causal discovery framework with differential privacy guarantee is developed. Extensive simulations and real-world experiments for both conditional independence test and causal discovery are conducted, which show that our method is effective in handling nonlinear numerical cases and easy to implement. The source code of our method and data are available at https://github.com/Causality-Inference/PCD.
GRASMOS: Graph Signage Model Selection for Gene Regulatory Networks
Authors: Brilliantova Angelina , Miller Hannah , Bezáková Ivona
Published: June 2023

Signed networks (networks with positive and negative edges) commonly arise in various domains from molecular biology to social media.
The edge signs -- i.e., the graph signage -- represent the interaction pattern between the vertices and can provide insights into the underlying system formation process. Generative models considering signage formation are essential for testing hypotheses about the emergence of interactions and for creating synthetic datasets for algorithm benchmarking (especially in areas where obtaining real-world datasets is difficult).

In this work, we pose a novel Maximum-Likelihood-based optimization problem for modeling signages given their topology and showcase it in the context of gene regulation. Regulatory interactions of genes play a key role in the process of organism development, and when broken can lead to serious organism abnormalities and diseases. Our contributions are threefold: First, we design a new class of signage models for a given topology, and, based on the parameter setting, we discuss its biological interpretations for gene regulatory networks (GRNs). Second, we design algorithms computing the Maximum Likelihood -- depending on the parameter setting, our algorithms range from closed-form expressions to MCMC sampling. Third, we evaluated the results of our algorithms on synthetic datasets and real-world large GRNs. Our work can lead to the prediction of unknown gene regulations, novel biological hypotheses, and realistic benchmark datasets in the realm of gene regulation.
Runtime Analysis for the NSGA-II: Provable Speed-Ups from Crossover
Authors: Doerr Benjamin , Qu Zhongdi
Published: June 2023

Very recently, the first mathematical runtime analyses for the NSGA-II, the most common multi-objective evolutionary algorithm, have been conducted. Continuing this research direction, we prove that the NSGA-II optimizes the OneJumpZeroJump benchmark asymptotically faster when crossover is employed. Together with a parallel independent work by Dang, Opris, Salehi, and Sudholt, this is the first time such an advantage of crossover is proven for the NSGA-II. Our arguments can be transferred to single-objective optimization. They then prove that crossover can speed up the (mu+1) genetic algorithm in a different way and more pronounced than known before. Our experiments confirm the added value of crossover and show that the observed advantages are even larger than what our proofs can guarantee.
A Formal Metareasoning Model of Concurrent Planning and Execution
Authors: Elboher Amihay , Bensoussan Ava , Karpas Erez , Ruml Wheeler , Shperberg Shahaf S. , Shimony Eyal
Published: June 2023

Agents that plan and act in the real world must deal with the fact that time passes as they are planning. When timing is tight, there may be insufficient time to complete the search for a plan before it is time to act. By commencing execution before search concludes, one gains time to search by making planning and execution concurrent. However, this incurs the risk of making incorrect action choices, especially if actions are irreversible. This tradeoff between opportunity and risk is the problem addressed in this paper. Our main contribution is to formally define this setting as an abstract metareasoning problem. We find that the abstract problem is intractable. However, we identify special cases that are solvable in polynomial time, develop greedy solution algorithms, and, through tests on instances derived from search problems, find several methods that achieve promising practical performance. This work lays the foundation for a principled time-aware executive that concurrently plans and executes.
TransPath: Learning Heuristics for Grid-Based Pathfinding via Transformers
Authors: Kirilenko Daniil , Andreychuk Anton , Panov Aleksandr , Yakovlev Konstantin
Published: June 2023

Heuristic search algorithms, e.g. A*, are the commonly used tools for pathfinding on grids, i.e. graphs of regular structure that are widely employed to represent environments in robotics, video games, etc. Instance-independent heuristics for grid graphs, e.g. Manhattan distance, do not take the obstacles into account, and thus the search led by such heuristics performs poorly in obstacle-rich environments. To this end, we suggest learning the instance-dependent heuristic proxies that are supposed to notably increase the efficiency of the search. The first heuristic proxy we suggest to learn is the correction factor, i.e. the ratio between the instance-independent cost-to-go estimate and the perfect one (computed offline at the training phase). Unlike learning the absolute values of the cost-to-go heuristic function, which was known before, learning the correction factor utilizes the knowledge of the instance-independent heuristic. The second heuristic proxy is the path probability, which indicates how likely the grid cell is lying on the shortest path. This heuristic can be employed in the Focal Search framework as the secondary heuristic, allowing us to preserve the guarantees on the bounded sub-optimality of the solution. We learn both suggested heuristics in a supervised fashion with the state-of-the-art neural networks containing attention blocks (transformers). We conduct a thorough empirical evaluation on a comprehensive dataset of planning tasks, showing that the suggested techniques i) reduce the computational effort of the A* up to a factor of 4x while producing the solutions, whose costs exceed those of the optimal solutions by less than 0.3% on average; ii) outperform the competitors, which include the conventional techniques from the heuristic search, i.e. weighted A*, as well as the state-of-the-art learnable planners.

The project web-page is: https://airi-institute.github.io/TransPath/.
OPT-GAN: A Broad-Spectrum Global Optimizer for Black-Box Problems by Learning Distribution
Authors: Lu Minfang , Ning Shuai , Liu Shuangrong , Sun Fengyang , Zhang Bo , Yang Bo , Wang Lin
Published: June 2023

Black-box optimization (BBO) algorithms are concerned with finding the best solutions for problems with missing analytical details. Most classical methods for such problems are based on strong and fixed a priori assumptions, such as Gaussianity. However, the complex real-world problems, especially when the global optimum is desired, could be very far from the a priori assumptions because of their diversities, causing unexpected obstacles. In this study, we propose a generative adversarial net-based broad-spectrum global optimizer (OPT-GAN) which estimates the distribution of optimum gradually, with strategies to balance exploration-exploitation trade-off. It has potential to better adapt to the regularity and structure of diversified landscapes than other methods with fixed prior, e.g., Gaussian assumption or separability.
Experiments on diverse BBO benchmarks and high dimensional real world applications exhibit that OPT-GAN outperforms other traditional and neural net-based BBO algorithms. The code and Appendix are available at https://github.com/NBICLAB/OPT-GAN
Generalized Category Discovery with Decoupled Prototypical Network
Authors: An Wenbin , Tian Feng , Zheng Qinghua , Ding Wei , Wang Qianying , Chen Ping
Published: June 2023

Generalized Category Discovery (GCD) aims to recognize both known and novel categories from a set of unlabeled data, based on another dataset labeled with only known categories. Without considering differences between known and novel categories, current methods learn about them in a coupled manner, which can hurt model's generalization and discriminative ability. Furthermore, the coupled training approach prevents these models transferring category-specific knowledge explicitly from labeled data to unlabeled data, which can lose high-level semantic information and impair model performance. To mitigate above limitations, we present a novel model called Decoupled Prototypical Network (DPN). By formulating a bipartite matching problem for category prototypes, DPN can not only decouple known and novel categories to achieve different training targets effectively, but also align known categories in labeled and unlabeled data to transfer category-specific knowledge explicitly and capture high-level semantics. Furthermore, DPN can learn more discriminative features for both known and novel categories through our proposed Semantic-aware Prototypical Learning (SPL). Besides capturing meaningful semantic information, SPL can also alleviate the noise of hard pseudo labels through semantic-weighted soft assignment. Extensive experiments show that DPN outperforms state-of-the-art models by a large margin on all evaluation metrics across multiple benchmark datasets. Code and data are available at https://github.com/Lackel/DPN.