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Offline black-box optimization aims to identify the optimal solution of a black-box objective function under the guidance of a surrogate model constructed solely from a pre-collected dataset. It is commonly used in industrial scenarios, which often involve constraints, i.e., constrained offline optimization (COO). Offline optimization has progressed in addressing the out-of-distribution (OOD) issue caused by its inherent inability to interact with the objective function. However, there is not enough research in addressing more difficult scenarios, which must simultaneously address OOD issues and constrained issues to find stable, high-quality (i.e., high-scoring and feasible) solutions. To bridge this gap, this paper proposes a method called constrained offline optimization via risk evaluation and management (COOREM), which is capable of consistently surpassing the offline dataset under the condition of satisfying constraints. Specifically, COOREM employs a dual-energy model to separately evaluate OOD risk and constrained risk. This separation strategy aims to distinguish and address two difficulty cases: the infeasible but not OOD solutions and the feasible but OOD solutions. Moreover, COOREM effectively manages OOD risk and constrained risk, ensuring the identification of high-quality solutions. Extensive experiments on real-world tasks, e.g., space missions, process synthesis and design problems, showcase COOREM&#39;s effectiveness in managing both OOD risk and constrained risk. Furthermore, our findings indicate that COOREM could outperform online methods that need to access the objective function in certain space missions.

AAAI 2025

Constrained Offline Black-Box Optimization via Risk Evaluation and Management

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.

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

AAAI 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. 

Offline black-box optimization aims to identify the optimal solution of a black-box objective function under the guidance of a surrogate model constructed solely from a pre-collected dataset. It is commonly used in industrial scenarios, which often involve constraints, i.e., constrained offline optimization (COO). Offline optimization has progressed in addressing the out-of-distribution (OOD) issue caused by its inherent inability to interact with the objective function. However, there is not enough research in addressing more difficult scenarios, which must simultaneously address OOD issues and constrained issues to find stable, high-quality (i.e., high-scoring and feasible) solutions. To bridge this gap, this paper proposes a method called constrained offline optimization via risk evaluation and management (COOREM), which is capable of consistently surpassing the offline dataset under the condition of satisfying constraints. Specifically, COOREM employs a dual-energy model to separately evaluate OOD risk and constrained risk. This separation strategy aims to distinguish and address two difficulty cases: the infeasible but not OOD solutions and the feasible but OOD solutions. Moreover, COOREM effectively manages OOD risk and constrained risk, ensuring the identification of high-quality solutions. Extensive experiments on real-world tasks, e.g., space missions, process synthesis and design problems, showcase COOREM's effectiveness in managing both OOD risk and constrained risk. Furthermore, our findings indicate that COOREM could outperform online methods that need to access the objective function in certain space missions.

poster

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

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-25 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.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/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. AAAI-25 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 investigate the sample-memory-pass trade-offs for pure exploration in multi-pass streaming multi-armed bandits (MABs) with the \emph{a priori} knowledge of the optimality gap $\Delta_{[2]}$. 
Here, and throughout, the optimality gap $\Delta_{[i]}$ is defined as the mean reward gap between the best and the $i$-th best arms. A recent line of results by Jin, Huang, Tang, and Xiao [ICML'21] and Assadi and Wang [COLT'24] have shown that if there is no known $\Delta_{[2]}$, a pass complexity of $\tilde{\Theta}(\log(1/\Delta_{[2]}))$ is necessary and sufficient to obtain the \emph{worst-case optimal} $O(n/\Delta^2_{[2]})$ sample complexity with a single-arm memory. However, our understanding of multi-pass algorithms with known $\Delta_{[2]}$ is still limited. Here, the key open problem is how many passes are required to achieve the complexity, i.e., $O( \sum_{i=2}^{n}1/\Delta^2_{[i]} \cdot \log{n})$ arm pulls, with a sublinear memory size.
	
In this work, we show that the ``right answer'' for the question is $\tilde{\Theta}(\log{n})$ passes.  We first present a lower bound, showing that any algorithm that finds the best arm with slightly sublinear memory -- a memory of $o({n}/{\text{polylog}({n})})$ arms -- and $O(\sum_{i=2}^{n}{1}/{\Delta^2_{[i]}}\cdot \log{n})$ arm pulls has to make $\Omega(\frac{\log{n}}{\log\log{n}})$ passes over the stream. We then show a nearly-matching algorithm that assuming the knowledge of $\Delta_{[2]}$, finds the best arm with $O( \sum_{i=2}^{n}1/\Delta^2_{[i]} \cdot \log{n})$ arm pulls and a \emph{single arm} memory.

Nearly Tight Bounds for Exploration in Streaming Multi-Armed Bandits with Known Optimality Gap

Solving tabular math word problems (TMWPs) has become one critical role to evaluate the mathematical reasoning ability of large language models (LLMs), where large-scale TMWP samples are commonly required for LLM fine-tuning. Since the collection of high-quality TMWP dataset is costly and time-consuming, recent research has concentrated on automatic TMWP generation. However, current generated samples usually suffer from issues of either correctness or diversity. In this paper, we propose a TEmplate-driven LLM-paraphrased (TeLL) framework for generating high-quality TMWP samples with diverse descriptions and correct question-answers. To this end, we first extract templates from existing real samples to generate question-answer pairs, ensuring the correctness. Then, we adopt an LLM to extend templates and paraphrase problems, obtaining diverse TMWP samples. Furthermore, we find the reasoning annotation is important for solving TMWPs. Therefore, we propose to enrich each solution with illustrative reasoning steps. Through the proposed framework, we construct a high-quality dataset TabMWP-TeLL by adhering to the question types in the TabMWP dataset, and we conduct extensive experiments on a variety of LLMs to demonstrate the effectiveness of TabMWP-TeLL in improving TMWP solving performance.

Template-Driven LLM-Paraphrased Framework for Tabular Math Word Problem Generation

Visual language models like Contrastive Language-Image Pretraining (CLIP) have shown impressive performance in analyzing natural images with language information. However, these models often encounter challenges when applied to specialized domains such as remote sensing due to the limited availability of image-text pairs for training. To tackle this issue, we introduce DiffCLIP, a novel framework that extends CLIP to effectively convey comprehensive language-driven semantic information for accurate classification of high-dimensional multimodal remote sensing images. DiffCLIP is a few-shot learning method that leverages unlabeled images for pretraining. It employs unsupervised mask diffusion learning to capture the distribution of diverse modalities without requiring labels. The modality-shared image encoder maps multimodal data into a unified subspace, extracting shared features with consistent parameters across modalities. A well-trained image encoder further enhances learning by aligning visual representations with class-label text information from CLIP. By integrating these approaches, DiffCLIP significantly boosts CLIP performance using a minimal number of image-text pairs. We evaluate DiffCLIP on widely used high-dimensional multimodal datasets, demonstrating its effectiveness in addressing few-shot annotated classification tasks. DiffCLIP achieves an overall accuracy improvement of 10.65\% across three remote sensing datasets compared with CLIP, while utilizing only 2-shot image-text pairs.

DiffCLIP: Few-shot Language-driven Multimodal Classifier

Feature matching between image pairs is a fundamental problem in computer vision that drives many applications, such as SLAM. 
Recently, semi-dense matching approaches have achieved substantial performance enhancements and established a widely-accepted coarse-to-fine paradigm. 
However, the majority of existing methods focus on improving coarse feature representation rather than the fine-matching module.
Prior fine-matching techniques, which rely on point-to-patch matching probability expectation or direct regression, often lack precision and do not guarantee the continuity of feature points across sequential images.
To address this limitation, this paper concentrates on enhancing the fine-matching module in the semi-dense matching framework.
We employ a lightweight and efficient homography estimation network to generate the perspective mapping between patches obtained from coarse matching. 
This patch-to-patch approach achieves the overall alignment of two patches, resulting in a higher sub-pixel accuracy by incorporating additional constraints.
By leveraging the homography estimation between patches, we can achieve a dense matching result with low computational cost. 
Extensive experiments demonstrate that our method achieves higher accuracy compared to previous semi-dense matchers.
Meanwhile, our dense matching results exhibit similar end-point-error accuracy compared to previous dense matchers while maintaining semi-dense efficiency.

HomoMatcher: Achieving Dense Feature Matching with Semi-Dense Efficiency by Homography Estimation

Recommender systems used in online platforms can drive users to
consume content continuously in an attempt to maximize satisfaction. Such engagement is invariably broken due to more pressing
work, alternate pursuits, distractions or fatigue. Recommender systems need to ensure the continuity of experience when the user
joins back. Session-based recommender systems typically create
different sessions based on a fixed time interval (𝜃 ), often resulting
in creation of a separate session when the user gets off the platform
temporarily. When the user joins back, session-based recommender
systems are likely to recommend content different than what they
would have in case the earlier session had continued. This may
cause dissatisfaction given that there is a difference in the predicted
world model of the user, i.e. the expectation from the last session,
and the observed one, i.e. the recommendations.
To handle this problem, we propose the creation of content-
driven sessions instead of time-driven sessions. In this, a session
continues until a single item category dominates in the user-item
interactions. A new session is created when a different item cate-
gory begins to dominate. The proposed content-driven method also
solves the ever-standing problem of deciding the optimal value of
time threshold for defining the time-based session. We report
that the proposed method outperforms existing SOTA method-
ologies set by time-based sessions by a large margin in terms of
recommendation performance on multiple datasets.

Ready for You When You Are Back: Content-Driven Session-Based Recommendation for Continuity of Experience

Temporal knowledge graph (TKG) reasoning that infers future missing facts is an essential and challenging task. Predicting future events typically relies on closely related historical facts, yielding more accurate results for repetitive or periodic events. However, for future events with sparse historical interactions, the effectiveness of this method, which focuses on leveraging high-frequency historical information, diminishes. Recently, the capabilities of diffusion models in image generation have opened new opportunities for TKG reasoning. Therefore, we propose a graph node diffusion model with dual-domain periodic contrastive learning (DPCL-Diff). Graph node diffusion model (GNDiff) introduces noise into sparsely related events to simulate new events, generating high-quality data that better conforms to the actual distribution. This generative mechanism significantly enhances the model's ability to reason about new events. Additionally, the dual-domain periodic contrastive learning (DPCL) maps periodic and non-periodic event entities to Poincaré and Euclidean spaces, leveraging their characteristics to distinguish similar periodic events effectively. Experimental results on four public datasets demonstrate that DPCL-Diff significantly outperforms state-of-the-art TKG models in event prediction, demonstrating our approach's effectiveness. This study also investigates the combined effectiveness of GNDiff and DPCL in TKG tasks.

DPCL-Diff：Temporal Knowledge Graph Reasoning Based on Graph Node Diffusion Model with Dual-Domain Periodic Contrastive Learning

We address unsupervised dependency parsing by building ensembles of diverse, previously proposed models through post-hoc aggregation of their output dependency parse structures. We observe that these ensembles often suffer from low robustness against weak ensemble components due to error accumulation. To tackle this problem, we propose an efficient, error-accumulation-resistant ensemble-selection approach, applied prior to building the ensembles. This method significantly enhances the performance and robustness of our ensembles. Results demonstrate that our approach outperforms each individual model and previous ensemble techniques. Additionally, our experiments show that the proposed ensemble-selection method surpasses previously proposed strategies, none of which account for error diversity.

Error Diversity Matters: An Error-Resistant Ensemble Method for Unsupervised Dependency Parsing

In this paper, we present generalization bounds for the unsupervised risk in the Deep Contrastive Representation Learning framework, which employs deep neural networks as representation functions. We approach this problem from two angles. On the one hand, we derive a parameter-counting bound that scales with the overall size of the neural networks. On the other hand, we provide a norm-based bound that scales with the norms of neural networks' weight matrices. Ignoring logarithmic factors, the bounds are independent of $k$, the size of the tuples provided for contrastive learning. To the best of our knowledge, this property is only shared by one other work, which employed a different proof strategy and suffers from very strong exponential dependence on the depth of the network which is due to a use of the peeling technique. Our results circumvent this by leveraging powerful results on covering numbers with respect to uniform norms over samples. In addition, we utilize loss augmentation techniques to further reduce the dependency on matrix norms and the implicit dependence on network depth. In fact, our techniques allow us to produce many bounds for the contrastive learning setting with similar architectural dependencies as in the study of the sample complexity of ordinary loss functions, thereby bridging the gap between the learning theories of contrastive learning and DNNs.

Generalization Analysis for Deep Contrastive Representation Learning

As machine learning models evolve, maintaining transparency demands more human-centric explainable AI techniques. Counterfactual explanations, with roots in human reasoning, identify the minimal input changes needed to obtain a given output and, hence, are crucial for supporting decision-making. Despite their importance, the evaluation of these explanations often lacks grounding in user studies and remains fragmented, with existing metrics not fully capturing human perspectives. To address this challenge, we developed a diverse set of 30 counterfactual scenarios and collected ratings across 8 evaluation metrics from 206 respondents. Subsequently, we fine-tuned different Large Language Models (LLMs) to predict average or individual human judgment across these metrics. Our methodology allowed LLMs to achieve an accuracy of up to 63% in zero-shot evaluations and 85% (over a 3-classes prediction) with fine-tuning across all metrics. The fine-tuned models predicting human ratings offer better comparability and scalability in evaluating different counterfactual explanation frameworks.

Towards Unifying Evaluation of Counterfactual Explanations: Leveraging Large Language Models for Human-Centric Assessments

The emergence of long-context text applications utilizing large language models (LLMs) has presented significant scalability challenges, particularly in memory footprint. The linear growth of the Key-Value (KV) cache—responsible for storing attention keys and values to minimize redundant computations—can lead to substantial increases in memory consumption, potentially causing models to fail to serve with limited memory resources. To address this issue, we propose a novel approach called Cache Sparse Representation (CSR), which converts the KV cache by transforming the dense Key-Value cache tensor into sparse indexes and weights, offering a more memory-efficient representation during LLM inference. Furthermore, we introduce NeuralDict, a novel neural network-based method for automatically generating the dictionary used in our sparse representation. Our extensive experiments demonstrate that CSR achieves performance comparable to state-of-the-art KV cache quantization algorithms while maintaining robust functionality in memory-constrained environments.

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Nearly Tight Bounds for Exploration in Streaming Multi-Armed Bandits with Known Optimality Gap

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