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To efficiently solve online problems with complicated constraints, projection-free algorithms including online frank-wolfe (OFW) and its variants have received significant interest recently. However, in the general case, existing efficient projection-free algorithms only achieved the regret bound of O(T^{3/4}), which is worse than the regret of projection-based algorithms, where T is the number of decision rounds. In this paper, we study the special case of online learning over strongly convex sets, for which we first prove that OFW can enjoy a better regret bound of O(T^{2/3}) for general convex losses. The key idea is to refine the decaying step-size in the original OFW by a simple line search rule. Furthermore, for strongly convex losses, we propose a strongly convex variant of OFW by redefining the surrogate loss function in OFW. We show that it achieves a regret bound of O(T^{2/3}) over general convex sets and a better regret bound of O(T^{1/2}) over strongly convex sets.

AAAI 2021

Projection-Free Online Learning over Strongly Convex Sets

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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AAAI 2023

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.

technical paper

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. 

Stackelberg planning is a recent framework where a leader and a follower each choose a plan in the same planning task, the leader's objective being to maximize plan cost for the follower. This formulation naturally captures security-related (leader=defender, follower=attacker) as well as robustness-related (leader=adversarial event, follower=agent) scenarios. Solving Stackelberg planning tasks requires solving many related planning tasks at the follower level (in the worst case, one for every possible leader plan). Here we introduce new methods to tackle this source of complexity, through sharing information across follower tasks. Our evaluation shows that these methods can significantly reduce both the time needed to solve follower tasks and the number of follower tasks that need to be solved in the first place.

Faster Stackelberg Planning via Symbolic Search and Information Sharing

Heterogeneous information networks (HINs) are ideal for describing real-world data with different types of entities and relationships. To carry out machine learning on HINs, meta-paths are widely utilized to extract semantics with pre-defined patterns, and models such as graph convolutional networks (GCNs) are thus enabled. However, previous works generally assume a fixed set of meta-paths, which is unrealistic as real-world data are overwhelmingly diverse. Therefore, it is appealing if meta-paths can be automatically selected given an HIN, yet existing works aiming at such problem possess drawbacks, such as poor efficiency and ignoring feature heterogeneity. To address these drawbacks, we propose GraphMSE, an efficient heterogeneous GCN combined with automatic meta-path selection. Specifically, we design highly efficient meta-path sampling techniques, and then injectively project sampled meta-path instances to vectors. We then design a novel semantic feature space alignment, aiming to align the meta-path instance vectors and hence facilitate meta-path selection. Extensive experiments on real-world datasets demonstrate that GraphMSE outperforms state-of-the-art counterparts, figures out important meta-paths, and is dramatically (e.g. 200 times) more efficient.

GraphMSE: Efficient Meta-Path Selection in Semantically Aligned Feature Space for Graph Neural Networks

Understanding the dynamics of complex biological and physiological systems has been explored  for many years in the form of physically-based mathematical simulators. The behavior of a physical system is often described via ordinary differential equations (ODE), referred to as the dynamics. In the standard case, the dynamics are derived from purely physical considerations. By contrast, in this work we study how the dynamics can be learned by a generative adversarial network which combines both physical and data considerations. As a use case, we focus on the dynamics of the heart signal electrocardiogram (ECG). We begin by introducing a new GAN framework, dubbed ODE-GAN, in which the generator learns the dynamics of a physical system in the form of an ordinary differential equation. Specifically, the generator network receives as input a value at a specific time step, and produces the derivative of the system at that time step. Thus, the ODE-GAN learns purely data-driven dynamics. We then show how to incorporate physical considerations into ODE-GAN. We achieve this through the introduction of an additional input to the ODE-GAN generator: physical parameters, which partially characterize the signal of interest. As we focus on ECG signals, we refer to this new framework as ECG-ODE-GAN. We perform an empirical evaluation and show that generating ECG heartbeats from our learned dynamics improves ECG heartbeat classification.

ECG ODE-GAN: Learning Ordinary Differential Equations of ECG Dynamics via Generative Adversarial Learning

This paper considers a linear Fisher market with n buyers and a continuum of items. In order to compute market equilibria, we introduce (infinite-dimensional) convex programs over Banach spaces, thereby generalizing the Eisenberg-Gale convex program and its dual. Regarding the new convex programs, we establish existence of optimal solutions, KKT conditions, as well as strong duality. All these properties are established via non-standard arguments, which circumvent the limitations of duality theory in optimization over infinite-dimensional vector spaces. Furthermore, we show that there exists a pure equilibrium allocation, i.e., a division of the item space. Similar to the finite-dimensional case, a market equilibrium under the infinite-dimensional Fisher market is Pareto optimal, envy-free and proportional. We also show how to obtain the (a.e. unique) equilibrium prices and a pure equilibrium allocation from the (unique) equilibrium utility prices. When the item space is the unit interval [0,1] and buyers have piecewise linear utilities, we show that approximate equilibrium prices can be computed in polynomial time. This is achieved by solving a finite-dimensional convex program using the ellipsoid method. To this end, we give nontrivial and efficient subgradient and separation oracles. For general buyer valuations, we propose computing market equilibrium using stochastic dual averaging, which finds approximate equilibrium prices with high probability.

Infinite-Dimensional Fisher Markets: Equilibrium, Duality and Optimization

Sequential recommender systems aim to model usersâ€™ evolving interests from their historical behaviors, and hence make customized time-relevant recommendations. Compared with traditional models, deep learning approaches such as CNN and RNN have achieved remarkable advancements in recommendation tasks. Recently, the BERT framework also emerges as a promising method, benefited from its self-attention mechanism in processing sequential data. However, one limitation of the original BERT framework is that it only considers one input source of the natural language tokens. It is still an open question to leverage various types of information under the BERT framework. Nonetheless, it is intuitively appealing to utilize other side information, such as item category or tag, for more comprehensive depictions and better recommendations. In our pilot experiments, we found naive approaches, which directly fuse types of side information into the item embeddings, usually bring very little or even negative effects. Therefore, in this paper, we propose the NOn-inVasive self-Attention mechanism (NOVA) to leverage side information effectively under the BERT framework. NOVA makes use of side information to generate better attention distribution, rather than directly altering the item embeddings, which may cause information overwhelming. We validate the NOVA-BERT model on both public and commercial datasets, and our method can stably outperform the state-of-the-art models with negligible computational overheads.

Noninvasive Self-Attention for Side Information Fusion in Sequential Recommendation

The synthesis of materials using the principle of thermogravimetric analysis to discover new anticorrosive paints requires several costly experiments. This paper presents an approach combining empirical data and domain analytical models to reduce the number of real experiments required to obtain the desired synthesis. The main idea is to predict the behavior of the synthesis of two materials with well-defined mass proportions as a function of temperature. As no exact equational model exists to predict the new material, we integrate a machine learning approach circumscribed by existing domain analytical models such as heating and kinetics equations in order to derive a generative model of augmented experiments. Extensive empirical evaluation shows that using machine learning approach guided by analytic models, it is possible to substantially reduce the number of needed physical experiments without losing the approximation quality.

Augmented Experiment in Material Engineering Using Machine Learning

Recently, deep neural networks (DNNs) have achieved excellent performance on time series classification. However, DNNs require large amounts of labeled data for supervised training. Although data augmentation can alleviate this problem, the standard approach assigns the same label to all augmented samples from the same source. This leads to the expansion of the data distribution such that the classification boundaries may be even harder to determine. In this paper, we propose Joint-label learning by Dual Augmentation (JobDA), which can enrich the training samples without expanding the distribution of the original data. Instead, we apply simple transformations to the time series and give these modified time series new labels, so that the model has to distinguish between these and the original data, as well as separating the original classes. This approach sharpens the boundaries around the original time series, and results in superior classification performance. We use Time Series Warping for our transformations: We shrink and stretch different regions of the original time series, like a fun-house mirror. Experiments conducted on extensive time-series datasets show that JobDA can improve the model performance on small datasets. Moreover, we verify that JobDA has better generalization ability compared with conventional data augmentation, and the visualization analysis further demonstrates that JobDA can learn more compact clusters.

Joint-Label Learning by Dual Augmentation for Time Series Classification

In a graph, a k-plex is a vertex set in which every vertex is not adjacent to at most k vertices of this set. The maximum k-plex problem, which asks for the largest k-plex from the given graph, is a key primitive in a variety of real-world applications like community detection and so on. In the paper, we develop an exact algorithm, Maplex, for solving this problem in real world graphs practically. Based on the existing first-order and the novel second-order reduction rules, we design a powerful preprocessing method which efficiently removes redundant vertices and edges for Maplex. Also, the graph color heuristic is widely used for overestimating the maximum clique of a graph. For the first time, we generalize this technique for bounding the size of maximum k-plex in Maplex. Experiments are carried out to compare our algorithm with other state-of-the-art solvers on a wide range of publicly available graphs. Maplex outperforms all other algorithms on large real world graphs and is competitive with existing solvers on artificial dense graphs. Finally, we shed light on the effectiveness of each key component of Maplex.

Improving Maximum k-plex Solver via Second-Order Reduction and Graph Color Bounding

This paper proposes human-interpretable learning of aspect-based sentiment analysis (ABSA), employing the recently introduced Tsetlin Machines (TMs). We attain interpretability by converting the intricate position-dependent textual semantics into binary form, mapping all the features into bag-of-words (BOWs). The binary form BOWs are encoded so that the information on the aspect and context words are nearly lossless for sentiment classification. We further adapt the BOWs as input to the TM, enabling learning of aspect-based sentiment patterns in propositional logic. To evaluate interpretability and accuracy, we conducted experiments on two widely used ABSA datasets of SemEval 2014: Restaurant 14 and Laptop 14. The experiments show how each relevant feature takes part in conjunctive clauses that contain the context information for the corresponding aspect word, demonstrating human-level interpretability. At the same time, the obtained accuracy is competitive with existing neural network models, reaching 78.02% on Restaurant 14 and 73.51% on Laptop 14.

Human-Level Interpretable Learning for Aspect-Based Sentiment Analysis

We address the problem of efficient exploration for transition model learning in the relational model-based reinforcement learning setting without extrinsic goals or rewards. Inspired by human curiosity, we propose goal-literal babbling (GLIB), a simple and general method for exploration in such problems. GLIB samples relational conjunctive goals that can be understood as specific, targeted effects that the agent would like to achieve in the world, and plans to achieve these goals using the transition model being learned. We provide theoretical guarantees showing that exploration with GLIB will converge almost surely to the ground truth model. Experimentally, we find GLIB to strongly outperform existing methods in both prediction and planning on a range of tasks, encompassing standard PDDL and PPDDL planning benchmarks and a robotic manipulation task implemented in the PyBullet physics simulator. Video: https://youtu.be/F6lmrPT6TOY Code: https://git.io/JIsTB

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Faster Stackelberg Planning via Symbolic Search and Information Sharing

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Distributed Projection-free Online Learning for Smooth and Convex Losses

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Faster Stackelberg Planning via Symbolic Search and Information Sharing

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Distributed Projection-free Online Learning for Smooth and Convex Losses

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