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Personalized learning is a promising educational approach that aims to provide high-quality personalized services for each student with minimum demands for practice data. The key to achieving that lies in the cognitive diagnosis task, which estimates the cognitive state of the student through his/her logged data of doing practice quizzes. Nevertheless, in the personalized learning scenario, existing cognitive diagnosis models suffer from the inability to (1) quickly adapt to new students using a small amount of data, and (2) measure the reliability of the diagnosis result to avoid improper services that mismatch the student&#39;s actual state. In this paper, we propose a general Bayesian mETA-learned Cognitive Diagnosis framework (BETA-CD), which addresses the two challenges by prior knowledge exploitation and model uncertainty quantification, respectively. Specifically, we firstly introduce Bayesian hierarchical modeling to associate each student&#39;s cognitive state with a shared prior distribution encoding prior knowledge and a personal posterior distribution indicating model uncertainty. Furthermore, we formulate a meta-learning objective to automatically exploit prior knowledge from historical students, and efficiently solve it with a gradient-based variational inference method. Extensive experiments upon various real-world datasets and models show the effectiveness and generality of the proposed BETA-CD.

AAAI 2023

BETA-CD: A Bayesian Meta-learned Cognitive Diagnosis Framework for Personalized Learning

bayesian learning

educational data mining

meta-learning

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-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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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.

Personalized learning is a promising educational approach that aims to provide high-quality personalized services for each student with minimum demands for practice data. The key to achieving that lies in the cognitive diagnosis task, which estimates the cognitive state of the student through his/her logged data of doing practice quizzes. Nevertheless, in the personalized learning scenario, existing cognitive diagnosis models suffer from the inability to (1) quickly adapt to new students using a small amount of data, and (2) measure the reliability of the diagnosis result to avoid improper services that mismatch the student's actual state. In this paper, we propose a general Bayesian mETA-learned Cognitive Diagnosis framework (BETA-CD), which addresses the two challenges by prior knowledge exploitation and model uncertainty quantification, respectively. Specifically, we firstly introduce Bayesian hierarchical modeling to associate each student's cognitive state with a shared prior distribution encoding prior knowledge and a personal posterior distribution indicating model uncertainty. Furthermore, we formulate a meta-learning objective to automatically exploit prior knowledge from historical students, and efficiently solve it with a gradient-based variational inference method. Extensive experiments upon various real-world datasets and models show the effectiveness and generality of the proposed BETA-CD.

poster

Human has the remarkable ability of learning novel objects by browsing extremely few examples, which may be attributed to the generic and robust feature extracted in the ventral stream of our brain for representing visual objects. In this sense, the tuning characteristics of ventral stream's neurons can be useful prior knowledge to improve few-shot classification. Specifically, we computationally model two groups of neurons found in ventral stream which are respectively sensitive to shape cues and color cues. Then we propose the hierarchical feature regularization method with these neuron models to regularize the backbone of a few-shot model, thus making it producing more generic and robust features for few-shot classification. In addition, to simulate the tuning characteristic that neuron firing at a higher rate in response to foreground stimulus elements compared to background elements, which we call belongingness, we design a foreground segmentation algorithm based on the observation that the foreground object usually does not appear at the edge of the picture, then multiply the foreground mask with the backbone of few-shot model. Our method is model-agnostic and can be applied to few-shot models with different backbones, training paradigms and classifiers.

Exploring Tuning Characteristics of Ventral Stream's Neurons for Few-Shot Image Classification

Molecular dynamics (MD) has long been the de facto choice for simulating complex atomistic systems from first principles. Recently deep learning models become a popular way to accelerate MD. Notwithstanding, existing models depend on intermediate variables such as the potential energy or force fields to update atomic positions, which requires additional computations to perform back-propagation. To waive this requirement, we propose a novel model called DiffMD by directly estimating the gradient of the log density of molecular conformations. DiffMD relies on a score-based denoising diffusion generative model that perturbs the molecular structure with a conditional noise depending on atomic accelerations and treats conformations at previous timeframes as the prior distribution for sampling}. Another challenge of modeling such a conformation generation process is that a molecule is kinetic instead of static, which no prior works have strictly studied. To solve this challenge, we propose an equivariant geometric Transformer as the score function in the diffusion process to calculate corresponding gradients. It incorporates the directions and velocities of atomic motions via 3D spherical Fourier-Bessel representations. With multiple architectural improvements, we outperform state-of-the-art baselines on MD17 and isomers of C7O2H10 datasets. This work contributes to accelerating material and drug discovery.

DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations

One major limitation of CNNs is that they are vulnerable to adversarial attacks. Currently, adversarial robustness in neural networks is commonly optimized with respect to a small pre-selected adversarial noise strength, causing them to have potentially limited performance when under attack by larger adversarial noises in real-world scenarios. In this research, we aim to find Neural Architectures that have improved robustness on a wide range of adversarial noise strengths through Neural Architecture Search. In detail, we propose a lightweight Adversarial Noise Estimator to reduce the high cost of generating adversarial noise with respect to different strengths. Besides, we construct an Efficient Wide Spectrum Searcher to reduce the cost of adjusting network architecture with the large adversarial validation set during the search. With the two components proposed, the number of adversarial noise strengths searched can be increased significantly while having a limited increase in search time. Extensive experiments on benchmark datasets such as CIFAR and ImageNet demonstrate that with a significantly richer search signal in robustness, our method can find architectures with improved overall robustness while having a limited impact on natural accuracy and around 40% reduction in search time compared with the naive approach of searching. Codes available at: https://github.com/zhicheng2T0/Wsr-NAS.git

Neural Architecture Search for Wide Spectrum Adversarial Robustness

The Datalog query language can express several powerful recursive properties, often crucial in real-world scenarios. While answering such queries is feasible over relational databases, the picture changes dramatically when data is enriched with intensional knowledge. It is indeed well-known that answering Datalog queries is undecidable already over lightweight knowledge bases (KBs) of the DL-Lite family. To overcome this issue, we propose a new query language based on Disjunctive Datalog rules combined with a modal epistemic operator. Rules in this language interact with the queried KB exclusively via the epistemic operator, thus extracting only the information true in every model of the KB. This form of interaction is crucial for not falling into undecidability. The contribution provided by this paper is threefold. First, we illustrate the syntax and the semantics of the novel query language. Second, we study the expressive power of different fragments of our new language and compare it with Disjunctive Datalog and its variants. Third, we outline the precise data complexity of answering queries in our new language over KBs expressed in various well-known formalisms.

Epistemic Disjunctive Datalog for Querying Knowledge Bases

Submodular maximization arises in many applications, and has attracted a lot of research attentions from various areas such as artificial intelligence, finance and operations research. Previous studies mainly consider only one kind of constraint, while many real-world problems often involve several constraints. In this paper, we consider the problem of submodular maximization under the intersection of two commonly used constraints, i.e., $k$-matroid constraint and $m$-knapsack constraint, and propose a new algorithm SPROUT by incorporating partial enumeration into the simultaneous greedy framework. We prove that SPROUT can achieve a polynomial-time approximation guarantee better than the state-of-the-art algorithms. Then, we introduce the random enumeration and smooth techniques into SPROUT to improve its efficiency, resulting in the SPROUT++ algorithm, which can keep a similar approximation guarantee. Experiments on the applications of movie recommendation and weighted max-cut demonstrate the superiority of SPROUT++ in practice.

Submodular Maximization Under the Intersection of Matroid and Knapsack Constraints

The Condorcet criterion (CC) is a classical and well-accepted criterion for voting. Unfortunately, it is incompatible with many other desiderata including participation (PAR), half- way monotonicity (HM), Maskin monotonicity (MM), and strategy-proofness (SP). Such incompatibilities are often known as impossibility theorems, and are proved by worst-case analysis. Previous work has investigated the likelihood for these impossibilities to occur under certain models, which are often criticized of being unrealistic.

We strengthen previous work by proving the first set of semi-
random impossibilities for voting rules to satisfy CC and the
more general, group versions of the four desiderata: for any sufficiently large number of voters $n$, any size of the group $1\le B\le \sqrt n$, any voting rule $r$, and under a large class of {\em semi-random} models that include Impartial Culture, the likelihood for $r$ to satisfy CC and PAR, CC and HM, CC
and MM, or CC and SP  is $1-\Omega(\frac{B}{\sqrt n})$. This matches existing lower bounds for CC&PAR ($B=1$) and CC&SP and CC&HM ($B\le \sqrt n$), showing that many commonly-studied voting rules  are already asymptotically optimal in such cases.

Semi-Random Impossibilities of Condorcet Criterion

Two-player zero-sum {\em graph games} are central in logic, verification, and multi-agent systems. The game proceeds by placing a token on a vertex of a graph, and allowing the players to move it to produce an infinite path, which determines the winner or payoff of the game. Traditionally, the players alternate turns in moving the token. In {\em bidding games}, however, the players have budgets and in each turn, an auction (bidding) determines which player moves the token. So far, bidding games have only been studied as full-information games. 
In this work we initiate the study of partial-information bidding games: we study bidding games in which a player's initial budget is drawn from a known probability distribution. 
We show that while for some bidding mechanisms and objectives, it is straightforward to adapt the results from the full-information setting to the partial-information setting, for others, the analysis is significantly more challenging, requires new techniques, and gives rise to interesting results. 
Specifically, we study games with {\em mean-payoff} objectives in combination with {\em poorman} bidding. We construct optimal strategies for a partially-informed player who plays against a fully-informed adversary. We show that, somewhat surprisingly, the {\em value} under pure strategies does not necessarily exist in such games. 

Bidding Graph Games with Partially-Observable Budgets

Traffic forecasting as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the spatio-temporal heterogeneity and non-stationarity implied in the traffic stream, in this study, we propose Spatio-Temporal Meta-Graph Learning as a novel Graph Structure Learning mechanism on spatio-temporal data. Specifically, we implement this idea into Meta-Graph Convolutional Recurrent Network (MegaCRN) by plugging the Meta-Graph Learner powered by a Meta-Node Bank into GCRN encoder-decoder. We conduct a comprehensive evaluation on two benchmark datasets (METR-LA and PEMS-BAY) and a new large-scale traffic speed dataset in which traffic incident information is contained. Our model outperformed the state-of-the-arts to a large degree on all three datasets (over 27% MAE and 34% RMSE). Besides, through a series of qualitative evaluations, we demonstrate that our model can explicitly disentangle the road links and time slots with different patterns and be robustly adaptive to any anomalous traffic situations. Codes and datasets are available at https://github.com/deepkashiwa20/MegaCRN.

Spatio-Temporal Meta-Graph Learning for Traffic Forecasting

The popularity of on-demand ride pooling is owing to the benefits offered to customers (lower prices), taxi drivers (higher revenue), environment (lower carbon footprint due to fewer vehicles) and aggregation companies like Uber (higher revenue). To achieve these benefits, two key interlinked challenges have to be solved effectively: (a) pricing -- setting prices to customer requests for taxis; and (b) matching -- assignment of customers (that accepted the prices) to taxis/cars. Traditionally, both these challenges have been studied individually and using myopic approaches (considering only current requests), without considering the impact of current matching on addressing future requests. In this paper, we develop a novel framework that handles the pricing and matching problems together, while also considering the future impact of the pricing and matching decisions. In our experimental results on a real-world taxi dataset, we demonstrate that our framework can significantly improve revenue (up to 17\% and on average 6.4\%) in a sustainable manner by reducing the number of vehicles  (up to 14\% and on average 10.6\%) required to obtain a given fixed revenue and the overall distance travelled by vehicles (up to 11.1\% and on average 3.7\%). That is to say, we are able to provide an ideal win-win scenario for all stakeholders (customers, drivers, aggregator, environment) involved by obtaining higher revenue for customers, drivers, aggregator (ride pooling company) while being good for the environment (due to fewer number of vehicles on the road and lesser fuel consumed).

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