Canada

Multistream classification poses significant challenges due to the necessity for rapid adaptation in dynamic streaming processes with concept drift. Despite the growing research outcomes in this area, there has been a notable oversight regarding the temporal dynamic relationships between these streams, leading to the issue of negative transfer arising from irrelevant data. In this paper, we propose a novel Online Boosting Adaptive Learning (OBAL) method that effectively addresses this limitation by adaptively learning the dynamic correlation among different streams. Specifically, OBAL operates in a dual-phase mechanism, in the first of which we design an Adaptive COvariate Shift Adaptation (AdaCOSA) algorithm to construct an initialized ensemble model using archived data from various source streams, thus mitigating the covariate shift while learning the dynamic correlations via an adaptive re-weighting strategy. During the online process, we employ a Gaussian Mixture Model-based weighting mechanism, which is seamlessly integrated with the acquired correlations via AdaCOSA to effectively handle asynchronous drift. This approach significantly improves the predictive performance and stability of the target stream. We conduct comprehensive experiments on several synthetic and real-world data streams, encompassing various drifting scenarios and types. The results clearly demonstrate that OBAL achieves remarkable advancements in addressing multistream classification problems by effectively leveraging positive knowledge derived from multiple sources.

AAAI 2024

Online Boosting Adaptive Learning under Concept Drift for Multistream Classification

dmkm: data stream mining

ml: online learning & bandits

ml: ensemble methods

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. 

The ability to accurately predict the trajectory of surrounding vehicles is a critical hurdle to overcome on the journey to fully autonomous vehicles. To address this challenge, we pioneer a novel behavior-aware trajectory prediction model (BAT) that incorporates insights and findings from traffic psychology, human behavior, and decision-making. Our model consists of behavior-aware, interaction-aware, priority-aware, and position-aware modules that perceive and understand the underlying interactions and account for uncertainty and variability in prediction, enabling higher-level learning and flexibility without rigid categorization of driving behavior. Importantly, this approach eliminates the need for manual labeling in the training process and addresses the challenges of non-continuous behavior labeling and the selection of appropriate time windows.
We evaluate BAT's performance across the Next Generation Simulation (NGSIM), Highway Drone (HighD), Roundabout Drone (RounD), and Macao Connected Autonomous Driving (MoCAD) datasets, showcasing its superiority over prevailing state-of-the-art (SOTA) benchmarks in terms of prediction accuracy and efficiency. Remarkably, even when trained on reduced portions of the training data (25%), our model outperforms most of the baselines, demonstrating its robustness and efficiency in predicting vehicle trajectories, and the potential to reduce the amount of data required to train autonomous vehicles, especially in corner cases. In conclusion, the behavior-aware model represents a significant advancement in the development of autonomous vehicles capable of predicting trajectories with the same level of proficiency as human drivers.

BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving

Our work aims at efficiently leveraging ambiguous demonstrations for the training of a reinforcement learning (RL) agent. An ambiguous demonstration can usually be interpreted in multiple ways, which severely hinders the RL agent from learning stably and efficiently. Since an optimal demonstration may also suffer from being ambiguous, previous works that combine RL and learning from demonstration (RLfD works) may not work well. Inspired by how humans handle such situations, we propose to use self-explanation (an agent generates explanations for itself) to recognize valuable high-level relational features as an interpretation of why a successful trajectory is successful. This way, the agent can leverage the explained important relations as guidance for its RL learning. Our main contribution is to propose the Self-Explanation for RL from Demonstrations (SERLfD) framework, which can overcome the limitations of existing RLfD works. Our experimental results show that an RLfD model can be improved by using our SERLfD framework in terms of training stability and performance. To foster further research in self-explanation-guided robot learning, we have made our demonstrations and code publicly accessible at https://github.com/YantianZha/SERLfD. For a deeper understanding of our work, interested readers can refer to our arXiv version: https://arxiv.org/pdf/2110.05286.pdf, including an accompanying appendix.

Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement Learning

Fair allocation of indivisible goods presents intriguing challenges from both a social choice perspective and an algorithmic standpoint. Due to the indivisibility of goods, it is common for one agent to envy the bundle of goods assigned to another agent and, indeed, envy-free solutions do not exist in general. In line with the classical game-theoretic concept of Nucleolus in coalitional games, we propose that a fair allocation should minimize the agents’ dissatisfaction profile in a lexicographic manner, where the dissatisfaction of an agent is defined as her maximum envy towards other agents. Therefore, we seek allocations that minimize the maximum envy. In cases where multiple solutions have an equal maximum value, we minimize the second-worst value, and so on. Additionally, as is customary in fair division problems, we also consider an efficiency requirement: among the allocations with the best agents’ dissatisfaction profile, we prioritize those that maximize the sum of agents’ utilities, known as maximum social welfare. Such allocations, referred to as maxileximin allocations, always exist.
In this study, we analyze the computational properties of maxileximin allocations in the context of fair allocation problems with constraints. Specifically, we focus on the Connected Fair Division problem, where goods correspond to the nodes of a graph, and a bundle of goods is allowed if the subgraph formed by those goods is connected. We demonstrate that the problem is F∆P2 -complete, even for instances with simple graphical structures such as path and star graphs.
However, we identify islands of tractability for instances with more intricate graphs, such as those having bounded treewidth, provided that the number of agents is bounded by a fixed number and utility functions use small values.

Maxileximin Envy Allocations and Connected Goods

In this work, we focus on high-dimensional single index models with non-Gaussian sensing vectors and generative priors. More specifically, our goal is to estimate the underlying signal from $m$ i.i.d. realizations of the semi-parameterized single index model $y=f(\mathbf{a}^T\mathbf{x}^*, w)$, where the signal $\mathbf{x}^* \in \mathbb{R}^n$ is contained in (up to a constant scaling) the range of an $L$-Lipschitz continuous generative model with bounded $k$-dimensional inputs, $\mathbf{a} \in \mathbb{R}^n$ follows a non-Gaussian distribution, $w$ is a random noise variable that is independent of $\mathbf{a}$, $f\,:\, \mathbb{R}^2 \to  \mathbb{R}$ is an unknown non-linear link function that is differentiable with respect to the first argument, and $y$ is the observed random variable. Using the first- and second-order Stein's identity, we introduce efficient algorithms to obtain estimated vectors that achieve the near-optimal statistical rate. In particular, we demonstrate that roughly $O\big(\frac{k}{\varepsilon^2}\log L\big)$ samples are sufficient to ensure that the estimated vectors of the algorithms attain $\varepsilon$-accurate recovery. Experimental results on image datasets are provided to support our theory.

Efficient Algorithms for Non-gaussian Single Index Models with Generative Priors

With the rapid development of virtual reality, omnidirectional images (ODIs) have attracted much attention from both the industrial community and academia. However, due to storage and transmission limitations, the resolution of current ODIs is often insufficient to provide an immersive virtual reality experience. Previous approaches address this issue using conventional 2D super-resolution techniques on equirectangular projection without exploiting the unique geometric properties of ODIs. In particular, the equirectangular projection (ERP) provides a complete field-of-view but introduces significant distortion, while the cubemap projection (CMP) can reduce distortion yet has a limited field-of-view. In this paper, we present a novel Bi-Projection Omnidirectional Image Super-Resolution (BPOSR) network to take advantage of the geometric properties of the above two projections. Then, we design two tailored attention methods for these projections: Horizontal Striped Transformer Block (HSTB) for ERP and Perspective Shift Transformer Block (PSTB) for CMP. Furthermore, we propose a fusion module to make these projections complement each other. Extensive experiments demonstrate that BPOSR achieves state-of-the-art performance on omnidirectional image super-resolution.

Omnidirectional Image Super-resolution via Bi-projection Fusion

Video event extraction (VEE) aims to extract key events and generate the event arguments for their semantic roles from the video. Despite promising results have been achieved by existing methods, they still lack an elaborate learning strategy to adequately consider: (1) inter-object interaction, which reflects the relation between objects; (2) inter-modality interaction, which aligns the features from text and video modality. In this paper, we propose a Multi-view Interaction with knowledge Distillation (MID) framework to solve the above problems with the Knowledge Distillation (KD) mechanism. Specifically, we propose the self-Relational KD (self-RKD) to enhance the inter-object interaction, where the relation between objects is measured by distance metric, and the high-level relational knowledge from the deeper layer is taken as the guidance for boosting the shallow layer in the video encoder. Meanwhile, to improve the inter-modality interaction, the Layer-to-layer KD (LKD) is proposed, which integrates additional cross-modal supervisions (i.e., the results of cross-attention) with the textual supervising signal for training each transformer decoder layer. Extensive experiments show that without any additional parameters, MID achieves the state-of-the-art performance compared to other strong methods in VEE.

Video Event Extraction with Multi-View Interaction Knowledge Distillation

Vector image representation is a popular choice when editability and flexibility in resolution are desired. However, most images are only available in raster form, making raster-to-vector image conversion (vectorization) an important task. 
Classical methods for vectorization are either domain-specific or yield an abundance of shapes which limits editability and interpretability.
Learning-based methods, that use differentiable rendering, has revolutionized vectorization, at the cost of poor generalization to out-of-training distribution domains Optimization-based counterparts are either slow or produce non-editable and redundant shapes. 
In this work, we propose Optimize & Reduce (O&R), a top-down approach to vectorization that is both fast and domain-agnostic. O&R aims to attain a compact representation of input images by iteratively optimizing Bezier curve parameters and significantly reducing the number of shapes, using a devised importance measure. 
We contribute a benchmark of five datasets comprising images from a broad spectrum of image complexities - from emojis to natural-like images. Through extensive experiments on hundreds of images, we demonstrate that our method is domain agnostic and outperforms existing works in both reconstruction and perceptual quality for a fixed number of shapes. Moreover, we show that our algorithm is x10 faster than the state-of-the-art optimization-based method.

Optimize & Reduce: A Top-Down Approach for Image Vectorization | VIDEO

We propose a combinatorial optimisation model called Limited Query Graph Connectivity Test.  We consider a graph whose edges have two possible states (On/Off).  The edges' states are hidden initially. We could query an edge to reveal its state.  Given a source s and a destination t, we aim to test s-t connectivity by identifying either a path (consisting of only On edges) or a cut (consisting of only Off edges). We are limited to B queries, after which we stop regardless of whether graph connectivity is established.  We aim to design a query policy that minimizes the expected number of queries.

Our model is mainly motivated by a cyber security use case where we need to establish whether an attack path exists in a network, between a source (i.e., a compromised user node) and a destination (i.e., a high-privilege admin node).  Edge query is resolved by manual effort from the IT admin, which is the motivation behind query minimization.

Our model is highly related to monotone Stochastic Boolean Function Evaluation (SBFE).  There are two existing exact algorithms for SBFE that are prohibitively expensive.  We propose a significantly more scalable exact algorithm.  While previous exact algorithms only scale for trivial graphs (i.e., past works experimented on at most 20 edges), we empirically demonstrate that our algorithm is scalable for a wide range of much larger practical graphs (i.e., Windows domain network graphs with tens of thousands of edges).

We propose three heuristics. Our best-performing heuristic is via reducing the search horizon of the exact algorithm. The other two are via reinforcement learning (RL) and Monte Carlo tree search (MCTS).  We also derive an anytime algorithm for computing the performance lower bound. Experimentally, we show that all our heuristics are near optimal.  The exact algorithm based heuristic outperforms all, surpassing RL, MCTS and 8 existing heuristics ported from SBFE and related literature.

Limited Query Graph Connectivity Test | VIDEO

In today's online advertising markets, a crucial requirement for an advertiser is to control her total expenditure within a time horizon under some budget. 
Among various budget control methods, throttling has emerged as a popular choice, managing an advertiser's total expenditure by selecting only a subset of auctions to participate in.
This paper provides a theoretical panorama of a single advertiser's dynamic budget throttling process in repeated second-price auctions.
We first establish a lower bound on the regret and an upper bound on the asymptotic competitive ratio for any throttling algorithm, respectively, when the advertiser's values are stochastic and adversarial. 
Regarding the algorithmic side, we propose the OGD-CB algorithm, which guarantees a near-optimal expected regret with stochastic values. 
On the other hand, when values are adversarial, we prove that this algorithm also reaches the upper bound on the asymptotic competitive ratio. 
We further compare throttling with pacing, another widely adopted budget control method, in repeated second-price auctions. 
In the stochastic case, we demonstrate that pacing is generally superior to throttling for the advertiser, supporting the well-known result that pacing is asymptotically optimal in this scenario. 
However, in the adversarial case, we give an exciting result indicating that throttling is also an asymptotically optimal dynamic bidding strategy. 
Our results bridge the gaps in theoretical research of throttling in repeated auctions and comprehensively reveal the ability of this popular budget-smoothing strategy.

Dynamic Budget Throttling in Repeated Second-Price Auctions

The task of Composed Image Retrieval (CoIR) involves queries that combine image and text modalities, allowing users to express their intent more effectively. However, current CoIR datasets are orders of magnitude smaller compared to other vision and language (V&L) datasets. Additionally, some of these datasets have noticeable issues, such as queries containing redundant modalities. To address these shortcomings, we introduce the Large Scale Composed Image Retrieval (LaSCo) dataset, a new CoIR dataset which is ten times larger than existing ones. Pre-training on our LaSCo, shows a noteworthy improvement in performance, even in zero-shot. Furthermore, we propose a new approach for analyzing CoIR datasets and methods, which detects modality redundancy or necessity, in queries.
We also introduce a new CoIR baseline, the Cross-Attention driven Shift Encoder (CASE). This baseline allows for early fusion of modalities using a cross-attention module and employs an additional auxiliary task during training. Our experiments demonstrate that this new baseline outperforms the current state-of-the-art methods on established benchmarks like FashionIQ and CIRR.

Online Boosting Adaptive Learning under Concept Drift for Multistream Classification

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BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving

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Online Boosting Adaptive Learning under Concept Drift for Multistream Classification

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Next from AAAI 2024

BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving

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PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads