bluuebunny/crossref_metadata_2025 huggingface dataset:
Proportionality Guarantees in Elections with Interdependent Issues
Authors: Brill Markus , Markakis Evangelos , Papasotiropoulos Georgios , Peters Jannik
Published: August 2023

We consider a multi-issue election setting over a set of possibly interdependent issues with the goal of achieving proportional representation of the views of the electorate. To this end, we employ a proportionality criterion suggested recently in the literature, that guarantees fair representation for all groups of voters of sufficient size. For this criterion, there exist rules that perform well in the case where all the issues have a binary domain and are independent of each other. In particular, this has been shown for Proportional Approval Voting (PAV) and for the Method of Equal Shares (MES). In this paper, we go two steps further: we generalize these guarantees for issues with a non-binary domain, and, most importantly, we consider extensions to elections with dependencies among issues, where we identify restrictions that lead to analogous results. To achieve this, we define appropriate generalizations of PAV and MES to handle conditional ballots. In addition to proportionality considerations, we also examine the computational properties of the conditional version of MES. Our findings indicate that the conditional case poses additional challenges and differs significantly from the unconditional one, both in terms of proportionality guarantees and computational complexity.
New Fairness Concepts for Allocating Indivisible Items
Authors: Caragiannis Ioannis , Garg Jugal , Rathi Nidhi , Sharma Eklavya , Varricchio Giovanna
Published: August 2023

For the fundamental problem of fairly dividing a set of indivisible items among agents, envy-freeness up to any item (EFX) and maximin fairness (MMS) are arguably the most compelling fairness concepts proposed till now. Unfortunately, despite significant efforts over the past few years, whether EFX allocations always exist is still an enigmatic open problem, let alone their efficient computation. Furthermore, today we know that MMS allocations are not always guaranteed to exist. These facts weaken the usefulness of both EFX and MMS, albeit their appealing conceptual characteristics.

We propose two alternative fairness concepts—called epistemic EFX (EEFX) and minimum EFX value fairness (MXS)---inspired by EFX and MMS. For both, we explore their relationships to well-studied fairness notions and, more importantly, prove that EEFX and MXS allocations always exist and can be computed efficiently for additive valuations. Our results justify that the new fairness concepts are excellent alternatives to EFX and MMS.
Rainbow Cycle Number and EFX Allocations: (Almost) Closing the Gap
Authors: Chashm Jahan Shayan , Seddighin Masoud , Seyed-Javadi Seyed-Mohammad , Sharifi Mohammad
Published: August 2023

Recently, some studies on the fair allocation of indivisible goods notice a connection between a purely combinatorial problem called the Rainbow Cycle problem and a fairness notion known as EFX: assuming that the rainbow cycle number for parameter d (i.e. R(d)) is O(d^β .log(d)^γ), we can find a (1 − ϵ)-EFX allocation with O_ϵ(n^(β/β+1) .log(n)^(γ/β+1)) number of discarded goods. The best upper bound on R(d) is improved in a series of works to O(d^4), O(d^(2+o(1))), and finally to O(d^2). Also, via a simple observation, we have R(d) ∈ Ω(d).

In this paper, we introduce another problem in extremal combinatorics. For a parameter l, we define the rainbow path degree and denote it by H(l). We show that any lower bound on H(l) yields an upper bound on R(d). Next, we prove that H(l) ∈ Ω(l^2 / log(l)) which yields an almost tight upper bound of R(d) ∈ Ω(d.log(d)). This, in turn, proves the existence of (1−ϵ)-EFX allocation with O_ϵ(√n .log(n)) number of discarded goods. In addition, for the special case of the Rainbow Cycle problem that the edges in each part form a permutation, we improve the upper bound to R(d) ≤ 2d−4. We leverage H(l) to achieve this bound.

Our conjecture is that the exact value of H(l) is ⌊l^2/2⌋ − 1. We provide some experiments that support this conjecture. Assuming this conjecture is correct, we have R(d) ∈ θ(d).
On Lower Bounds for Maximin Share Guarantees
Authors: Hummel Halvard
Published: August 2023

We study the problem of fairly allocating a set of indivisible items to a set of agents with additive valuations. Recently, Feige et al. (WINE'21) proved that a maximin share (MMS) allocation exists for all instances with n agents and no more than n + 5 items. Moreover, they proved that an MMS allocation is not guaranteed to exist for instances with 3 agents and at least 9 items, or n ≥ 4 agents and at least 3n + 3 items. In this work, we shrink the gap between these upper and lower bounds for guaranteed existence of MMS allocations. We prove that for any integer c > 0, there exists a number of agents n_c such that an MMS allocation exists for any instance with n ≥ n_c agents and at most n + c items, where n_c ≤ ⌊0.6597^c · c!⌋ for allocation of goods and n_c ≤ ⌊0.7838^c · c!⌋ for chores. Furthermore, we show that for n ≠ 3 agents, all instances with n + 6 goods have an MMS allocation.
Game Theory with Simulation of Other Players
Authors: Kovařík Vojtěch , Oesterheld Caspar , Conitzer Vincent
Published: August 2023

Game-theoretic interactions with AI agents could differ from traditional human-human interactions in various ways. One such difference is that it may be possible to simulate an AI agent (for example because its source code is known), which allows others to accurately predict the agent's actions. This could lower the bar for trust and cooperation. In this paper, we first formally define games in which one player can simulate another at a cost, and derive some basic properties of such games. Then, we prove a number of results for such games, including: (1) introducing simulation into generic-payoff normal-form games makes them easier to solve; (2) if the only obstacle to cooperation is a lack of trust in the possibly-simulated agent, simulation enables equilibria that improve the outcome for both agents; and (3) however, there are settings where introducing simulation results in strictly worse outcomes for both players.
Truthful Fair Mechanisms for Allocating Mixed Divisible and Indivisible Goods
Authors: Li Zihao , Liu Shengxin , Lu Xinhang , Tao Biaoshuai
Published: August 2023

We study the problem of designing truthful and fair mechanisms when allocating a mixture of divisible and indivisible goods. We first show that there does not exist an EFM (envy-free for mixed goods) and truthful mechanism in general. This impossibility result holds even if there is only one indivisible good and one divisible good and there are only two agents. Thus, we focus on some more restricted settings. Under the setting where agents have binary valuations on indivisible goods and identical valuations on a single divisible good (e.g., money), we design an EFM and truthful mechanism. When agents have binary valuations over both divisible and indivisible goods, we first show there exist EFM and truthful mechanisms when there are only two agents or when there is a single divisible good. On the other hand, we show that the mechanism maximizing Nash welfare cannot ensure EFM and truthfulness simultaneously.
Auto-bidding with Budget and ROI Constrained Buyers
Authors: Liu Xiaodong , Shen Weiran
Published: August 2023

In online advertising markets, an increasing number of advertisers are adopting auto-bidders to buy advertising slots. This tool simplifies the process of optimizing bids based on various financial constraints.

In our study, we focus on second-price auctions where bidders have both private budget and private ROI (return on investment) constraints. We formulate the auto-bidding system design problem as a mathematical program and analyze the auto-bidders' bidding strategy under such constraints. We demonstrate that our design ensures truthfulness, i.e., among all pure and mixed strategies, always reporting the truthful budget and ROI is an optimal strategy for the bidders. Although the program is non-convex, we provide a fast algorithm to compute the optimal bidding strategy for the bidders based on our analysis. We also study the welfare and provide a lower bound for the PoA (price of anarchy). Moreover, we prove that if all bidders utilize our auto-bidding system, a Bayesian Nash equilibrium exists. We provide a sufficient condition under which the iterated best response process converges to such an equilibrium. Finally, we conduct extensive experiments to empirically evaluate the effectiveness of our design.
Finding Mixed-Strategy Equilibria of Continuous-Action Games without Gradients Using Randomized Policy Networks
Authors: Martin Carlos , Sandholm Tuomas
Published: August 2023

We study the problem of computing an approximate Nash equilibrium of continuous-action game without access to gradients. Such game access is common in reinforcement learning settings, where the environment is typically treated as a black box. To tackle this problem, we apply zeroth-order optimization techniques that combine smoothed gradient estimators with equilibrium-finding dynamics.

We model players' strategies using artificial neural networks. In particular, we use randomized policy networks to model mixed strategies. These take noise in addition to an observation as input and can flexibly represent arbitrary observation-dependent, continuous-action distributions. Being able to model such mixed strategies is crucial for tackling continuous-action games that lack pure-strategy equilibria.

We evaluate the performance of our method using an approximation of the Nash convergence metric from game theory, which measures how much players can benefit from unilaterally changing their strategy.

We apply our method to continuous Colonel Blotto games, single-item and multi-item auctions, and a visibility game.

The experiments show that our method can quickly find a high-quality approximate equilibrium.

Furthermore, they show that the dimensionality of the input noise is crucial for performance.

To our knowledge, this paper is the first to solve general continuous-action games with unrestricted mixed strategies and without any gradient information.
Truthful Auctions for Automated Bidding in Online Advertising
Authors: Xing Yidan , Zhang Zhilin , Zheng Zhenzhe , Yu Chuan , Xu Jian , Wu Fan , Chen Guihai
Published: August 2023

Automated bidding, an emerging intelligent decision-making paradigm powered by machine learning, has become popular in online advertising. Advertisers in automated bidding evaluate the cumulative utilities and have private financial constraints over multiple ad auctions in a long-term period. Based on these distinct features, we consider a new ad auction model for automated bidding: the values of advertisers are public while the financial constraints, such as budget and return on investment (ROI) rate, are private types. We derive the truthfulness conditions with respect to private constraints for this multi-dimensional setting, and demonstrate any feasible allocation rule could be equivalently reduced to a series of non-decreasing functions on budget. However, the resulted allocation mapped from these non-decreasing functions generally follows an irregular shape, making it difficult to obtain a closed-form expression for the auction objective. To overcome this design difficulty, we propose a family of truthful automated bidding auction with personalized rank scores, similar to the Generalized Second-Price (GSP) auction. The intuition behind our design is to leverage personalized rank scores as the criteria to allocate items, and compute a critical ROI to transforms the constraints on budget to the same dimension as ROI. The experimental results demonstrate that the proposed auction mechanism outperforms the widely used ad auctions, such as first-price auction and second-price auction, in various automated bidding environments.
Incentive-Compatible Selection for One or Two Influentials
Authors: Zhao Yuxin , Zhang Yao , Zhao Dengji
Published: August 2023

Selecting influentials in networks against strategic manipulations has attracted many researchers' attention and it also has many practical applications. Here, we aim to select one or two influentials in terms of progeny (the influential power) and prevent agents from manipulating their edges (incentive compatibility). The existing studies mostly focused on selecting a single influential for this setting. Zhang et al. [2021] studied the problem of selecting one agent and proved an upper bound of 1/(1+ln2) to approximate the optimal selection. In this paper, we first design a mechanism to actually reach the bound. Then, we move this forward to choosing two agents and propose a mechanism to achieve an approximation ratio of (3+ln2)/(4(1+ln2)) (approx. 0.54).
Sequence Learning Using Equilibrium Propagation
Authors: Bal Malyaban , Sengupta Abhronil
Published: August 2023

Equilibrium Propagation (EP) is a powerful and more bio-plausible alternative to conventional learning frameworks such as backpropagation. The effectiveness of EP stems from the fact that it relies only on local computations and requires solely one kind of computational unit during both of its training phases, thereby enabling greater applicability in domains such as bio-inspired neuromorphic computing. The dynamics of the model in EP is governed by an energy function and the internal states of the model consequently converge to a steady state following the state transition rules defined by the same. However, by definition, EP requires the input to the model (a convergent RNN) to be static in both the phases of training. Thus it is not possible to design a model for sequence classification using EP with an LSTM or GRU like architecture. In this paper, we leverage recent developments in modern hopfield networks to further understand energy based models and develop solutions for complex sequence classification tasks using EP while satisfying its convergence criteria and maintaining its theoretical similarities with recurrent backpropagation. We explore the possibility of integrating modern hopfield networks as an attention mechanism with convergent RNN models used in EP, thereby extending its applicability for the first time on two different sequence classification tasks in natural language processing viz. sentiment analysis (IMDB dataset) and natural language inference (SNLI dataset). Our implementation source code is available at https://github.com/NeuroCompLab-psu/EqProp-SeqLearning.
Learnable Surrogate Gradient for Direct Training Spiking Neural Networks
Authors: Lian Shuang , Shen Jiangrong , Liu Qianhui , Wang Ziming , Yan Rui , Tang Huajin
Published: August 2023

Spiking neural networks (SNNs) have increasingly drawn massive research attention due to biological interpretability and efficient computation. Recent achievements are devoted to utilizing the surrogate gradient (SG) method to avoid the dilemma of non-differentiability of spiking activity to directly train SNNs by backpropagation. However, the fixed width of the SG leads to gradient vanishing and mismatch problems, thus limiting the performance of directly trained SNNs. In this work, we propose a novel perspective to unlock the width limitation of SG, called the learnable surrogate gradient (LSG) method. The LSG method modulates the width of SG according to the change of the distribution of the membrane potentials, which is identified to be related to the decay factors based on our theoretical analysis. Then we introduce the trainable decay factors to implement the LSG method, which can optimize the width of SG automatically during training to avoid the gradient vanishing and mismatch problems caused by the limited width of SG. We evaluate the proposed LSG method on both image and neuromorphic datasets. Experimental results show that the LSG method can effectively alleviate the blocking of gradient propagation caused by the limited width of SG when training deep SNNs directly. Meanwhile, the LSG method can help SNNs achieve competitive performance on both latency and accuracy.
A Hierarchical Approach to Population Training for Human-AI Collaboration
Authors: Loo Yi , Gong Chen , Meghjani Malika
Published: August 2023

A major challenge for deep reinforcement learning (DRL) agents is to collaborate with novel partners that were not encountered by them during the training phase. This is specifically worsened by an increased variance in action responses when the DRL agents collaborate with human partners due to the lack of consistency in human behaviors. Recent work have shown that training a single agent as the best response to a diverse population of training partners significantly increases an agent's robustness to novel partners. We further enhance the population-based training approach by introducing a Hierarchical Reinforcement Learning (HRL) based method for Human-AI Collaboration. Our agent is able to learn multiple best-response policies as its low-level policy while at the same time, it learns a high-level policy that acts as a manager which allows the agent to dynamically switch between the low-level best-response policies based on its current partner. We demonstrate that our method is able to dynamically adapt to novel partners of different play styles and skill levels in the 2-player collaborative Overcooked game environment. We also conducted a human study in the same environment to test the effectiveness of our method when partnering with real human subjects. Code is available at https://gitlab.com/marvl-hipt/hipt.
CVTP3D: Cross-view Trajectory Prediction Using Shared 3D Queries for Autonomous Driving
Authors: Song Zijian , Bi Huikun , Zhang Ruisi , Mao Tianlu , Wang Zhaoqi
Published: August 2023

Trajectory prediction with uncertainty is a critical and challenging task for autonomous driving. Nowadays, we can easily access sensor data represented in multiple views. However, cross-view consistency has not been evaluated by the existing models, which might lead to divergences between the multimodal predictions from different views. It is not practical and effective when the network does not comprehend the 3D scene, which could cause the downstream module in a dilemma. Instead, we predicts multimodal trajectories while maintaining cross-view consistency. We presented a cross-view trajectory prediction method using shared 3D Queries (XVTP3D). We employ a set of 3D queries shared across views to generate multi-goals that are cross-view consistent. We also proposed a random mask method and coarse-to-fine cross-attention to capture robust cross-view features. As far as we know, this is the first work that introduces the outstanding top-down paradigm in BEV detection field to a trajectory prediction problem. The results of experiments on two publicly available datasets show that XVTP3D achieved state-of-the-art performance with consistent cross-view predictions.
Cognitively Inspired Learning of Incremental Drifting Concepts
Authors: Rostami Mohammad , Galstyan Aram
Published: August 2023

Humans continually expand their learned knowledge to new domains and learn new concepts without any interference with past learned experiences. In contrast, machine learning models perform poorly in a continual learning setting, where input data distribution changes over time. Inspired by the nervous system learning mechanisms, we develop a computational model that enables a deep neural network to learn new concepts and expand its learned knowledge to new domains incrementally in a continual learning setting. We rely on the Parallel Distributed Processing theory to encode abstract concepts in an embedding space in terms of a multimodal distribution. This embedding space is modeled by internal data representations in a hidden network layer. We also leverage the Complementary Learning Systems theory to equip the model with a memory mechanism to overcome catastrophic forgetting through implementing pseudo-rehearsal. Our model can generate pseudo-data points for experience replay and accumulate new experiences to past learned experiences without causing cross-task interference.
The Effects of AI Biases and Explanations on Human Decision Fairness: A Case Study of Bidding in Rental Housing Markets
Authors: Wang Xinru , Liang Chen , Yin Ming
Published: August 2023

The use of AI-based decision aids in diverse domains has inspired many empirical investigations into how AI models’ decision recommendations impact humans’ decision accuracy in AI-assisted decision making, while explorations on the impacts on humans’ decision fairness are largely lacking despite their clear importance. In this paper, using a real-world business decision making scenario—bidding in rental housing markets—as our testbed, we present an experimental study on understanding how the bias level of the AI-based decision aid as well as the provision of AI explanations affect the fairness level of humans’ decisions, both during and after their usage of the decision aid. Our results suggest that when people are assisted by an AI-based decision aid, both the higher level of racial biases the decision aid exhibits and surprisingly, the presence of AI explanations, result in more unfair human decisions across racial groups. Moreover, these impacts are partly made through triggering humans’ “disparate interactions” with AI. However, regardless of the AI bias level and the presence of AI explanations, when people return to make independent decisions after their usage of the AI-based decision aid, their decisions no longer exhibit significant unfairness across racial groups.
Spatial-Temporal Self-Attention for Asynchronous Spiking Neural Networks
Authors: Wang Yuchen , Shi Kexin , Lu Chengzhuo , Liu Yuguo , Zhang Malu , Qu Hong
Published: August 2023

The brain-inspired spiking neural networks (SNNs) are receiving increasing attention due to their asynchronous event-driven characteristics and low power consumption. As attention mechanisms recently become an indispensable part of sequence dependence modeling, the combination of SNNs and attention mechanisms holds great potential for energy-efficient and high-performance computing paradigms. However, the existing works cannot benefit from both temporal-wise attention and the asynchronous characteristic of SNNs. To fully leverage the advantages of both SNNs and attention mechanisms, we propose an SNNs-based spatial-temporal self-attention (STSA) mechanism, which calculates the feature dependence across the time and space domains without destroying the asynchronous transmission properties of SNNs. To further improve the performance, we also propose a spatial-temporal relative position bias (STRPB) for STSA to consider the spatiotemporal position of spikes. Based on the STSA and STRPB, we construct a spatial-temporal spiking Transformer framework, named STS-Transformer, which is powerful and enables SNNs to work in an asynchronous event-driven manner. Extensive experiments are conducted on popular neuromorphic datasets and speech datasets, including DVS128 Gesture, CIFAR10-DVS, and Google Speech Commands, and our experimental results can outperform other state-of-the-art models.
REPLACE: A Logical Framework for Combining Collective Entity Resolution and Repairing
Authors: Bienvenu Meghyn , Cima Gianluca , Gutiérrez-Basulto Víctor
Published: August 2023

This paper considers the problem of querying dirty databases, which may contain both erroneous facts and multiple names for the same entity. While both of these data quality issues have been widely studied in isolation, our contribution is a holistic framework for jointly deduplicating and repairing data. Our REPLACE framework follows a declarative approach, utilizing logical rules to specify under which conditions a pair of entity references can or must be merged and logical constraints to specify consistency requirements. The semantics defines a space of solutions, each consisting of a set of merges to perform and a set of facts to delete, which can be further refined by applying optimality criteria. As there may be multiple optimal solutions, we use classical notions of possible and certain query answers to reason over the alternative solutions, and introduce a novel notion of most informative answer to obtain a more compact presentation of query results. We perform a detailed analysis of the data complexity of the central reasoning tasks of recognizing optimal solutions and (most informative) possible and certain answers, for each of the three notions of optimal solution and for both general and restricted specifications.
Optimal Anytime Coalition Structure Generation Utilizing Compact Solution Space Representation
Authors: Taguelmimt Redha , Aknine Samir , Boukredera Djamila , Changder Narayan , Sandholm Tuomas
Published: August 2023

Coalition formation is a central approach for multiagent coordination. A crucial part of coalition formation that is extensively studied in AI is coalition structure generation: partitioning agents into coalitions to maximize overall value.

In this paper, we propose a novel method for coalition structure generation by introducing a compact and efficient representation of coalition structures. Our representation partitions the solution space into smaller, more manageable subspaces that gather structures containing coalitions of specific sizes. Our proposed method combines two new algorithms, one which leverages our compact representation and a branch-and-bound technique to generate optimal coalition structures, and another that utilizes a preprocessing phase to identify the most promising sets of coalitions to evaluate. Additionally, we show how parts of the solution space can be gathered into groups to avoid their redundant evaluation and we investigate the computational gain that is achieved by avoiding that redundant processing. Through this approach, our algorithm is able to prune the solution space more efficiently. Our results show that the proposed algorithm is superior to prior state-of-the-art methods in generating optimal coalition structures under several value distributions.
Automatic Verification for Soundness of Bounded QNP Abstractions for Generalized Planning
Authors: Cui Zhenhe , Kuang Weidu , Liu Yongmei
Published: August 2023

Generalized planning (GP) studies the computation of general solutions for a set of planning problems. Computing general solutions with correctness guarantee has long been a key issue in GP. Abstractions are widely used to solve GP problems. For example, a popular abstraction model for GP is qualitative numeric planning (QNP), which extends classical planning with non-negative real variables that can be increased or decreased by some arbitrary amount. The refinement of correct solutions of sound abstractions are solutions with correctness guarantees for GP problems. More recent literature proposed a uniform abstraction framework for GP and gave model-theoretic definitions of sound and complete abstractions for GP problems. In this paper, based on the previous work, we explore automatic verification of sound abstractions for GP. Firstly, we present a proof-theoretic characterization for sound abstractions. Secondly, based on the characterization, we give a sufficient condition for sound abstractions with deterministic actions. Then we study how to verify the sufficient condition when the abstraction models are bounded QNPs where integer variables can be incremented or decremented by one. To this end, we develop methods to handle counting and transitive closure, which are often used to define numerical variables. Finally, we implement a sound bounded QNP abstraction verification system and report experimental results on several domains.
Disentanglement of Latent Representations via Causal Interventions
Authors: Gendron Gaël , Witbrock Michael , Dobbie Gillian
Published: August 2023

The process of generating data such as images is controlled by independent and unknown factors of variation. The retrieval of these variables has been studied extensively in the disentanglement, causal representation learning, and independent component analysis fields. Recently, approaches merging these domains together have shown great success. Instead of directly representing the factors of variation, the problem of disentanglement can be seen as finding the interventions on one image that yield a change to a single factor. Following this assumption, we introduce a new method for disentanglement inspired by causal dynamics that combines causality theory with vector-quantized variational autoencoders. Our model considers the quantized vectors as causal variables and links them in a causal graph. It performs causal interventions on the graph and generates atomic transitions affecting a unique factor of variation in the image. We also introduce a new task of action retrieval that consists of finding the action responsible for the transition between two images. We test our method on standard synthetic and real-world disentanglement datasets. We show that it can effectively disentangle the factors of variation and perform precise interventions on high-level semantic attributes of an image without affecting its quality, even with imbalanced data distributions.
Ranking-based Argumentation Semantics Applied to Logical Argumentation
Authors: Heyninck Jesse , Raddaoui Badran , Straßer Christian
Published: August 2023

In formal argumentation, a distinction can be made between extension-based semantics, where sets of

arguments are either (jointly) accepted or not, and ranking-based semantics, where grades of accept-

ability are assigned to arguments. Another important distinction is that between abstract approaches,

that abstract away from the content of arguments, and structured approaches, that specify a method

of constructing argument graphs on the basis of a knowledge base. While ranking-based semantics

have been extensively applied to abstract argumentation, few work has been done on ranking-based

semantics for structured argumentation. In this paper, we make a systematic investigation into the be-

haviour of ranking-based semantics applied to existing formalisms for structured argumentation. We

show that a wide class of ranking-based semantics gives rise to so-called culpability measures, and

are relatively robust to specific choices in argument construction methods.
Relative Inconsistency Measures for Indefinite Databases with Denial Constraints
Authors: Parisi Francesco , Grant John
Published: August 2023

Handling conflicting information is an important challenge in AI. Measuring inconsistency is an approach that provides ways to quantify the severity of inconsistency and helps understanding the primary sources of conflicts. In particular, a relative inconsistency measure computes, by some criteria, the proportion of the knowledge base that is inconsistent. In this paper we investigate relative inconsistency measures for indefinite databases, which allow for indefinite or partial information which is formally expressed by means of disjunctive tuples. We introduce a postulate-based definition of relative inconsistency measure for indefinite databases with denial constraints, and investigate the compliance of some relative inconsistency measures with rationality postulates for indefinite databases as well as for the special case of definite databases. Finally, we investigate the complexity of the problem of computing the value of the proposed relative inconsistency measures as well as of the problems of deciding whether the inconsistency value is lower than, greater than, or equal to a given threshold for indefinite and definite databases.
Measuring Acoustics with Collaborative Multiple Agents
Authors: Yu Yinfeng , Chen Changan , Cao Lele , Yang Fangkai , Sun Fuchun
Published: August 2023

As humans, we hear sound every second of our life. The sound we hear is often affected by the acoustics of the environment surrounding us. For example, a spacious hall leads to more reverberation. Room Impulse Responses (RIR) are commonly used to characterize environment acoustics as a function of the scene geometry, materials, and source/receiver locations. Traditionally, RIRs are measured by setting up a loudspeaker and microphone in the environment for all source/receiver locations, which is time-consuming and inefficient. We propose to let two robots measure the environment's acoustics by actively moving and emitting/receiving sweep signals. We also devise a collaborative multi-agent policy where these two robots are trained to explore the environment's acoustics while being rewarded for wide exploration and accurate prediction. We show that the robots learn to collaborate and move to explore environment acoustics while minimizing the prediction error. To the best of our knowledge, we present the very first problem formulation and solution to the task of collaborative environment acoustics measurements with multiple agents.
CROP: Towards Distributional-Shift Robust Reinforcement Learning Using Compact Reshaped Observation Processing
Authors: Altmann Philipp , Ritz Fabian , Feuchtinger Leonard , Nüßlein Jonas , Linnhoff-Popien Claudia , Phan Thomy
Published: August 2023

The safe application of reinforcement learning (RL) requires generalization from limited training data to unseen scenarios. Yet, fulfilling tasks under changing circumstances is a key challenge in RL. Current state-of-the-art approaches for generalization apply data augmentation techniques to increase the diversity of training data. Even though this prevents overfitting to the training environment(s), it hinders policy optimization. Crafting a suitable observation, only containing crucial information, has been shown to be a challenging task itself. To improve data efficiency and generalization capabilities, we propose Compact Reshaped Observation Processing (CROP) to reduce the state information used for policy optimization. By providing only relevant information, overfitting to a specific training layout is precluded and generalization to unseen environments is improved. We formulate three CROPs that can be applied to fully observable observation- and action-spaces and provide methodical foundation. We empirically show the improvements of CROP in a distributionally shifted safety gridworld. We furthermore provide benchmark comparisons to full observability and data-augmentation in two different-sized procedurally generated mazes.
On Approximating Total Variation Distance
Authors: Bhattacharyya Arnab , Gayen Sutanu , Meel Kuldeep S. , Myrisiotis Dimitrios , Pavan A. , Vinodchandran N. V.
Published: August 2023

Total variation distance (TV distance) is a fundamental notion of distance between probability distributions. In this work, we introduce and study the problem of computing the TV distance of two product distributions over the domain {0,1}^n. In particular, we establish the following results.

1. The problem of exactly computing the TV distance of two product distributions is #P-complete. This is in stark contrast with other distance measures such as KL, Chi-square, and Hellinger which tensorize over the marginals leading to efficient algorithms.

2. There is a fully polynomial-time deterministic approximation scheme (FPTAS) for computing the TV distance of two product distributions P and Q where Q is the uniform distribution. This result is extended to the case where Q has a constant number of distinct marginals. In contrast, we show that when P and Q are Bayes net distributions the relative approximation of their TV distance is NP-hard.
Inducing Stackelberg Equilibrium through Spatio-Temporal Sequential Decision-Making in Multi-Agent Reinforcement Learning
Authors: Zhang Bin , Li Lijuan , Xu Zhiwei , Li Dapeng , Fan Guoliang
Published: August 2023

In multi-agent reinforcement learning (MARL), self-interested agents attempt to establish equilibrium and achieve coordination depending on game structure. However, existing MARL approaches are mostly bound by the simultaneous actions of all agents in the Markov game (MG) framework, and few works consider the formation of equilibrium strategies via asynchronous action coordination. In view of the advantages of Stackelberg equilibrium (SE) over Nash equilibrium, we construct a spatio-temporal sequential decision-making structure derived from the MG and propose an N-level policy model based on a conditional hypernetwork shared by all agents. This approach allows for asymmetric training with symmetric execution, with each agent responding optimally conditioned on the decisions made by superior agents. Agents can learn heterogeneous SE policies while still maintaining parameter sharing, which leads to reduced cost for learning and storage and enhanced scalability as the number of agents increases. Experiments demonstrate that our method effectively converges to the SE policies in repeated matrix game scenarios, and performs admirably in immensely complex settings including cooperative tasks and mixed tasks.
DeepPSL: End-to-End Perception and Reasoning
Authors: Dasaratha Sridhar , Puranam Sai Akhil , Phogat Karmvir Singh , Tiyyagura Sunil Reddy , Duffy Nigel P.
Published: August 2023

We introduce DeepPSL a variant of probabilistic soft logic (PSL) to produce an end-to-end trainable system that integrates reasoning and perception. PSL represents first-order logic in terms of a convex graphical model – hinge-loss Markov random fields (HL-MRFs). PSL stands out among probabilistic logic frameworks due to its tractability having been applied to systems of more than 1 billion ground rules. The key to our approach is to represent predicates in first-order logic using deep neural networks and then to approximately back-propagate through the HL-MRF and thus train every aspect of the first-order system being represented. We believe that this approach represents an interesting direction for the integration of deep learning and reasoning techniques with applications to knowledge base learning, multi-task learning, and explainability. Evaluation on three different tasks demonstrates that DeepPSL significantly outperforms state-of-the-art neuro-symbolic methods on scalability while achieving comparable or better accuracy.
Federated Graph Semantic and Structural Learning
Authors: Huang Wenke , Wan Guancheng , Ye Mang , Du Bo
Published: August 2023

Federated graph learning collaboratively learns a global graph neural network with distributed graphs, where the non-independent and identically distributed property is one of the major challenge. Most relative arts focus on traditional distributed tasks like images and voices, incapable of the graph structures. This paper firstly reveals that local client distortion is brought by both node-level semantics and graph-level structure. First, for node-level semantic, we find that contrasting nodes from distinct classes is beneficial to provide a well-performing discrimination. We pull the local node towards the global node of the same class and push them away from the global node of different classes. Second, we postulate that a well-structural graph neural network possesses similarity for neighbors due to the inherent adjacency relationships. However, aligning each node with adjacent nodes hinders discrimination due to the potential class inconsistency. We transform the adjacency relationships into the similarity distribution and leverage the global model to distill the relation knowledge into the local model, which preserves the structural information and discriminability of the local model. Empirical results on three graph datasets manifest the superiority of the proposed method over counterparts.
Multi-Modality Deep Network for JPEG Artifacts Reduction
Authors: Jiang Xuhao , Tan Weimin , Lin Qing , Ma Chenxi , Yan Bo , Shen Liquan
Published: August 2023

In recent years, many convolutional neural network-based models are designed for JPEG artifacts reduction, and have achieved notable progress. However, few methods are suitable for extreme low-bitrate image compression artifacts reduction. The main challenge is that the highly compressed image loses too much information, resulting in reconstructing high-quality image difficultly. To address this issue, we propose a multimodal fusion learning method for text-guided JPEG artifacts reduction, in which the corresponding text description not only provides the potential prior information of the highly compressed image, but also serves as supplementary information to assist in image deblocking. We fuse image features and text semantic features from the global and local perspectives respectively, and design a contrastive loss built upon contrastive learning to produce visually pleasing results. Extensive experiments, including a user study, prove that our method can obtain better deblocking results compared to the state-of-the-art methods.
IID-GAN: an IID Sampling Perspective for Regularizing Mode Collapse
Authors: Li Yang , Shi Liangliang , Yan Junchi
Published: August 2023

Despite its success, generative adversarial networks (GANs) still suffer from mode collapse, i.e., the generator can only map latent variables to a partial set of modes in the target distribution. In this paper, we analyze and seek to regularize this issue with an independent and identically distributed (IID) sampling perspective and emphasize that holding the IID property referring to the target distribution for generation can naturally avoid mode collapse. This is based on the basic IID assumption for real data in machine learning. However, though the source samples {z} obey IID, the generations {G(z)} may not necessarily be IID sampling from the target distribution. Based on this observation, considering a necessary condition of IID generation, we propose a new loss to encourage the closeness between the inverse samples of real data and the Gaussian source in the latent space to regularize the generation to be IID from the target distribution. The logic is that the inverse samples from target data should also be IID in the source distribution. Experiments on both synthetic and real-world data show the effectiveness of our model.
FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment
Authors: Liu Jiahao , Wu Jiang , Chen Jinyu , Hu Miao , Zhou Yipeng , Wu Di
Published: August 2023

Different from conventional federated learning, personalized federated learning (PFL) is able to train a customized model for each individual client according to its unique requirement. The mainstream approach is to adopt a kind of weighted aggregation method to generate personalized models, in which weights are determined by the loss value or model parameters among different clients. However, such kinds of methods require clients to download others' models. It not only sheer increases communication traffic but also potentially infringes data privacy. In this paper, we propose a new PFL algorithm called FedDWA (Federated Learning with Dynamic Weight Adjustment) to address the above problem, which leverages the parameter server (PS) to compute personalized aggregation weights based on collected models from clients. In this way, FedDWA can capture similarities between clients with much less communication overhead. More specifically, we formulate the PFL problem as an optimization problem by minimizing the distance between personalized models and guidance models, so as to customize aggregation weights for each client. Guidance models are obtained by the local one-step ahead adaptation on individual clients. Finally, we conduct extensive experiments using five real datasets and the results demonstrate that FedDWA can significantly reduce the communication traffic and achieve much higher model accuracy than the state-of-the-art approaches.
Recognizable Information Bottleneck
Authors: Lyu Yilin , Liu Xin , Song Mingyang , Wang Xinyue , Peng Yaxin , Zeng Tieyong , Jing Liping
Published: August 2023

Information Bottlenecks (IBs) learn representations that generalize to unseen data by information compression. However, existing IBs are practically unable to guarantee generalization in real-world scenarios due to the vacuous generalization bound. The recent PAC-Bayes IB uses information complexity instead of information compression to establish a connection with the mutual information generalization bound. However, it requires the computation of expensive second-order curvature, which hinders its practical application. In this paper, we establish the connection between the recognizability of representations and the recent functional conditional mutual information (f-CMI) generalization bound, which is significantly easier to estimate. On this basis we propose a Recognizable Information Bottleneck (RIB) which regularizes the recognizability of representations through a recognizability critic optimized by density ratio matching under the Bregman divergence. Extensive experiments on several commonly used datasets demonstrate the effectiveness of the proposed method in regularizing the model and estimating the generalization gap.
Efficient NLP Model Finetuning via Multistage Data Filtering
Authors: Ouyang Xu , Ansari Shahina Mohd Azam , Lin Felix Xiaozhu , Ji Yangfeng
Published: August 2023

As model finetuning is central to the modern NLP, we set to maximize its efficiency. Motivated by redundancy in training examples and the sheer sizes of pretrained models, we exploit a key opportunity: training only on important data. To this end, we set to filter training examples in a streaming fashion, in tandem with training the target model. Our key techniques are two: (1) automatically determine a training loss threshold for skipping backward training passes; (2) run a meta predictor for further skipping forward training passes. We integrate the above techniques in a holistic, three-stage training pro- cess. On a diverse set of benchmarks, our method reduces the required training examples by up to 5.3× and training time by up to 6.8×, while only seeing minor accuracy degradation. Our method is effective even for training one epoch, where each training example is encountered only once. It is simple to implement and is compatible with the existing finetuning techniques. Code is available at: https://github.com/xo28/efficient-NLP-multistage-training
Guide to Control: Offline Hierarchical Reinforcement Learning Using Subgoal Generation for Long-Horizon and Sparse-Reward Tasks
Authors: Shin Wonchul , Kim Yusung
Published: August 2023

Reinforcement learning (RL) has achieved considerable success in many fields, but applying it to real-world problems can be costly and risky because it requires a lot of online interaction. Recently, offline RL has shown the possibility of extracting a solution through existing logged data without online interaction. In this work, we propose an offline hierarchical RL method, Guider (Guide to Control), that can efficiently solve long-horizon and sparse-reward tasks from offline data. The high-level policy sequentially generates a subgoal that can guide the agent to arrive at the final goal, and the lower-level policy learns how to reach each given guided subgoal. In the process of learning from offline data, the key is to make the low-level policy reachable to the generated subgoals. We show that high-quality subgoal generation is possible through pre-training a latent subgoal prior model. The well-regulated subgoal generation improves performance while avoiding distributional shifts in offline RL by breaking down long, complex tasks into shorter, easier ones. For evaluations, Guider outperforms prior offline RL methods in long-horizon robot navigation and complex manipulation benchmarks. Our code is available at https://github.com/gckor/Guider.