Canada

The maximum vertex-weighted clique problem (MVWCP) and the maximum edge-weighted clique problem (MEWCP) are two natural extensions of the fundamental maximum clique problem. 
In this paper, we systematically study MEWCP and make the following major contributions:
(1) We show that MEWCP is NP-hard even when the minimum degree of the graph is n-2, in contrast to MVWCP which is polynomial-time solvable when the minimum degree of the graph is at least n-3. This result distinguishes the complexity of the two problems for the first time.
(2) To address MEWCP, we develop an efficient branch-and-bound algorithm called MEWCat with both practical and theoretical performance guarantees. In practice, MEWCat utilizes a new upper bound tighter than existing ones, which allows for more efficient pruning of branches. In theory, we prove a running-time bound of O*(1.4423^n) for MEWCat, which breaks the trivial bound of O*(2^n) in the research line of practical exact MEWCP solvers for the first time.
(3) Empirically, we evaluate the performance of MEWCat on various benchmark instances. The experiments demonstrate that MEWCat outperforms state-of-the-art exact solvers significantly. For instance, on 16 DIMACS graphs that the state-of-the-art solver BBEWC fails to solve within 7200 seconds, MEWCat solves all of them with an average time of less than 1000 seconds. On real-world graphs, MEWCat achieves an average speedup of over 36x.

AAAI 2024

A Fast Exact Solver with Theoretical Analysis for the Maximum Edge-Weighted Clique Problem

maximum edge-weighted clique problem

search and optimization

combinotorial optimization

branch and bound

other foundations of search & optimization

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2021

technical paper

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-24 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.<br><br><br><br>

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

One of the most crucial capabilities in the commercial sector is a personalized prediction of a customer's next purchase. We present a novel method of creating a commerce intelligence engine that caters to multiple merchants intended for the UB Platform, managed by e-payment company Harex InfoTech. To cultivate this intelligence, we utilized payment receipt data and created a Natural Language Processing (NLP)-based commerce model using a Transformer to accommodate multinational and merchant trade. Our model, called General Commerce Intelligence (GCI), provides a range of services for merchants, including product recommendations, product brainstorming, product bundling, event promotions, collaborative marketing, target marketing, and demand fore-casting etc. To bolster user privacy and foster sustainable business collaboration, especially among micro-, small-, and medium-sized enterprises (MSMEs), the GCI model was trained through federated learning, especially with glocalization. This study delves into the structure, development, and assessment of GCI, showcasing its transformative capacity to implement User Centric AI and re-shape the global commerce landscape to benefit MSMEs.

General Commerce Intelligence: Glocally Federated NLP-Based Engine for Privacy-Preserving and Sustainable Personalized Services of Multi-Merchants

Over the past decade, significant advances have been made in the field of image search for e-commerce applications. Traditional image-to-image retrieval models, which focus solely on image details such as texture, tend to overlook useful semantic information contained within the images. As a result, the retrieved products might possess similar image details, but fail to fulfil the user's search goals. Moreover, the use of image-to-image retrieval models for products containing multiple images results in significant online product feature storage overhead and complex mapping implementations. In this paper, we report the design and deployment of the proposed Multi-modal Item Embedding Model (MIEM) to address these limitations. It is capable of utilizing both textual information and multiple images about a product to construct meaningful product features. By leveraging semantic information from images, MIEM effectively supplements the image search process, improving the overall accuracy of retrieval results. MIEM has become an integral part of the Shopee image search platform. Since its deployment in March 2023, it has achieved a remarkable 9.90% increase in terms of clicks per user and a 4.23% boost in terms of orders per user for the image search feature on the Shopee e-commerce platform.

Transformer-Empowered Multi-Modal Item Embedding for Enhanced Image Search in E-commerce

Deep learning, the most important subfield of machine learning and artificial intelligence (AI) over the last decade, is considered one of the fundamental technologies underpinning the Fourth Industrial Revolution. But despite its record-breaking history, deep learning’s enormous appetite for compute and data means that sometimes it can be too costly to practically use. In this paper, we connect technical insights from deep learning scaling laws and transfer learning with the economics of IT to propose a framework for estimating the cost of deep learning computer vision systems to achieve a desired level of accuracy. Our tool can be of practical use to AI practitioners in industry or academia to guide investment decisions.

A Model for Estimating the Economic Costs of Computer Vision Systems That Use Deep Learning

Discrete optimization belongs to the set of N P-hard
problems, spanning fields such as mixed-integer
programming and combinatorial optimization. A current
standard approach to solving convex discrete optimization
problems is the use of cutting-plane algorithms, which
reach optimal solutions by iteratively adding inequalities
known as cuts to refine a feasible set. Despite the existence
of a number of general-purpose cut-generating algorithms,
large-scale discrete optimization problems continue to suffer
from intractability. In this work, we propose a method for
accelerating cutting-plane algorithms via reinforcement
learning. Our approach uses learned policies as surrogates
for N P-hard elements of the cut generating procedure
in a way that (i) accelerates convergence, and (ii) retains
guarantees of optimality. We apply our method on two types
of problems where cutting-plane algorithms are commonly
used: stochastic optimization, and mixed-integer quadratic
programming. We observe the benefits of our method when
applied to Benders decomposition (stochastic optimization)
and iterative loss approximation (quadratic programming),
achieving up to 45% faster average convergence when
compared to modern alternative algorithms.

Accelerating Cutting-Plane Algorithms via Reinforcement Learning Surrogates

Answer Set Programming (ASP) has emerged as a promising
paradigm in knowledge representation and automated reason-
ing owing to its ability to model hard combinatorial problems
from diverse domains in a natural way. Building on advances
in propositional SAT solving, the past two decades have wit-
nessed the emergence of well-engineered systems for solv-
ing the answer set satisfiability problem, i.e., finding mod-
els or answer sets for a given answer set program. In re-
cent years, there has been growing interest in problems be-
yond satisfiability, such as model counting, in the context of
ASP. Akin to the early days of propositional model count-
ing, state-of-the-art exact answer set counters do not scale
well beyond small instances. Exact ASP counters struggle
with handling larger input formulas. The primary contribu-
tion of this paper is a new ASP counting framework, called
sharpASP, which counts answer sets avoiding larger input
formulas. This relies on an alternative way of defining answer
sets that allows lifting of key techniques developed in the con-
text of propositional model counting. Our extensive empirical
analysis over 1470 benchmarks demonstrates significant per-
formance gain over current state-of-the-art exact answer set
counters. Specifically, by using sharpASP, we were able to
solve 1062 benchmarks with PAR2 score of 3082 whereas
using prior state-of-the-art, we could only solve 895 bench-
marks with PAR2 score of 4205, all other experimental con-
ditions being the same.

Exact ASP Counting with Compact Encodings

This paper is a contribution to neural network semantics, a foundational framework for neuro-symbolic AI. The key insight of this theory is that logical operators can be mapped to operators on neural network states. In this paper, we do this for a neural network \emph{learning} operator. We map a dynamic operator $\Hebbop{\varphi}$ to iterated Hebbian learning, a simple learning policy that updates a neural network by repeatedly applying Hebb's learning rule until the net reaches a fixed-point. Our main result is that we can "translate away" $\Hebbop{\varphi}$-formulas via reduction axioms. This means that completeness for the logic of iterated Hebbian learning follows from completeness of the base logic. These reduction axioms also provide (1) a human-interpretable description of iterated Hebbian learning as a kind of plausibility upgrade, and (2) an approach to building neural networks with guarantees on what they can learn.

What Do Hebbian Learners Learn? Reduction Axioms for Unstable Hebbian Learning

Graph neural networks (GNNs) have been demonstrated to perform well in graph representation learning, but always lacking of generalization capability when tackling out-of-distribution (OOD) data. Graph invariant learning methods, backed by the invariance principle among defined multiple environments, have shown effectiveness in dealing this issue. However, existing methods heavily rely on well predefined or accurately generated environment partitions, which are hard to be obtained in practice, leading to sub-optimal OOD generalization performances.
In this paper, we propose a novel graph invariant learning method based on invariant and variant patterns co-mixup strategy, which is capable of jointly generating mixed multiple environments and capturing invariant patterns from the mixed graph data. Specifically, we first adopt a subgraph extractor to identify invariant subgraphs. Subsequently, we design one novel co-mixup strategy, i.e., jointly conducting environment mixup and invariant mixup. For the environment mixup, we mix the variant environment-related subgraphs so as to generate sufficiently diverse multiple environments, which is important to guarantee the quality of the graph invariant learning. For the invariant mixup, we mix the invariant subgraphs, further encouraging to capture invariant patterns behind graphs while getting rid of spurious correlations for OOD generalization. We demonstrate that the proposed environment mixup and invariant mixup can mutually promote each other. Extensive experiments on both synthetic and real-world datasets demonstrate that our method significantly outperforms state-of-the-art under various distribution shifts.

Graph Invariant Learning with Subgraph Co-mixup for Out-of-Distribution Generalization

Learning personalized treatment policies is a formative challenge in many real-world applications, including in healthcare, econometrics, artificial intelligence. However, the effectiveness of candidate policies is not always identifiable, i.e., it is not uniquely computable from the combination of the available data and assumptions about the generating mechanisms. This paper studies policy learning from data collected in various non-identifiable settings, i.e., (1) observational studies with unobserved confounding; (2) randomized experiments with partial observability; and (3) their combinations. We derive sharp, closed-formed bounds from observational and experimental data over the conditional treatment effects. Based on these novel bounds, we further characterize the problem of safe policy learning and develop an algorithm that trains a policy from data guaranteed to achieve, at least, the performance of the baseline policy currently deployed. Finally, we validate our proposed algorithm on synthetic data and a large clinical trial, demonstrating that it guarantees safe behaviors and robust performance.

Towards Safe Policy Learning under Partial Identifiability: A Causal Approach

Explaining an answer to a Datalog query is an essential task towards Explainable AI, especially nowadays where Datalog plays a critical role in the development of ontology-based applications. A well-established approach for explaining a query answer is the so-called why-provenance, which essentially collects all the subsets of the input database that can be used to obtain that answer via some derivation process, typically represented as a proof tree. It is well known, however, that computing the why-provenance for Datalog queries is computationally expensive, and thus, very few attempts can be found in the literature. The goal of this work is to demonstrate how off-the-shelf SAT solvers can be exploited towards an efficient computation of the why-provenance for Datalog queries. Interestingly, our SAT-based approach allows us to build the why-provenance in an incremental fashion, that is, one explanation at a time, which is much more useful in a practical context than the one-shot computation of the whole set of explanations as done by existing approaches.

Computing the Why-Provenance for Datalog Queries via SAT Solvers

Science discovery requires exploration and analysis of complex data and AI4Science has gained considerable importance due to the ability of AI in effectively handling data. Our work aims to develop and apply structured learning techniques to enable researchers to unveil hidden patterns and make advan-cements in their fields.

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