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Column Generation (CG) is an effective and iterative algorithm to solve large-scale linear programs (LP). During each CG iteration, new columns are added to improve the solution of the LP. Typically, CG greedily selects one column with the most negative reduced cost, which can be improved by adding more columns at once. However, selecting all columns with negative reduced costs would lead to the addition of redundant columns that do not improve the objective value. Therefore, selecting the appropriate columns to add is still an open problem and previous machine-learning-based approaches for CG only add a constant quantity of columns per iteration due to the state-space explosion problem. To address this, we propose Fast Family Column Generation (FFCG) -- a novel reinforcement-learning-based CG that selects a variable number of columns as needed in an iteration. Specifically, we formulate the column selection problem in CG as an MDP and design a reward metric that balances both the convergence speed and the number of redundant columns. In our experiments, FFCG converges faster on the common benchmarks and reduces the number of CG iterations by 77.1% for Cutting Stock Problem (CSP) and 84.8% for Vehicle Routing Problem with Time Windows (VRPTW), and a 71.4% reduction in computing time for CSP and 84.0% for VRPTW on average compared to several state-of-the-art baselines.

AAAI 2025

FFCG: Effective and Fast Family Column Generation for Solving Large-Scale Linear Program

solvers and tools

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. 

technical paper

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.



Model counting is task of counting the number of satisfying assignments of a Boolean formula. Since counting is intractable in general, most applications use $(\varepsilon, \delta)$-approximations, where the output is within a $(1+\varepsilon)$-factor of the count with  probability at least $1-\delta$. 
Many demanding applications make thousands of counting queries, and the state-of-the-art approximate counter, ApproxMC, makes hundreds of calls to SAT solvers to answer a single approximate counting query.
The sheer number of SAT calls, poses a significant challenge to the existing approaches. 

In this work, we propose an approximation scheme, RealMC, that is tailored to such demanding applications with low time limits. Compared to ApproxMC, RealMC makes 14$\times$ fewer SAT calls while providing the same guarantees as ApproxMC in the constant-factor regime. In an evaluation over 2,247 instances, RealMC solved 271 more and achieved a $2\times$ speedup against ApproxMC.

Towards Real-Time Approximate Counting

Image-guided object assembly represents a burgeoning research topic in computer vision. This paper introduces a novel task: translating multi-view images of a structural 3D model (for example, one constructed with building blocks drawn from a 3D-object library) into a detailed sequence of assembly instructions executable by a robotic arm. Fed with multi-view images of the target 3D model for replication, the model designed for this task must address several sub-tasks, including recognizing individual components used in constructing the 3D model, estimating the geometric pose of each component, and deducing a feasible assembly order adhering to physical rules. Establishing accurate 2D-3D correspondence between multi-view images and 3D objects is technically challenging. To tackle this, we propose an end-to-end model known as the Neural Assembler. This model learns an object graph where each vertex represents recognized components from the images, and the edges specify the topology of the 3D model, enabling the derivation of an assembly plan. We establish benchmarks for this task and conduct comprehensive empirical evaluations of Neural Assembler and alternative solutions. Our experiments clearly demonstrate the superiority of Neural Assembler.

Neural Assembler: Learning to Generate Fine-Grained Robotic Assembly Instructions from Multi-View Images

Current collaborative perception methods often rely on fully annotated datasets, which can be expensive to obtain in practical situations. 
To reduce annotation costs, some works adopt sparsely supervised learning techniques and generate pseudo labels for the missing instances. However, these methods fail to achieve an optimal confidence threshold that harmonizes the quality and quantity of pseudo labels. To address this issue, we propose an end-to-end Collaborative perception Dual Teacher-Student framework (CoDTS), which employs adaptive complementary learning to produce both high-quality and high-quantity pseudo labels. Specifically, the Main Foreground Mining (MFM) module generates high-quality pseudo labels based on the prediction of the static teacher. Subsequently, the Supplement Foreground Mining (SFM) module ensures a balance between the quality and quantity of pseudo labels by adaptively identifying missing instances based on the prediction of the dynamic teacher. Additionally, the Neighbor Anchor Sampling (NAS) module is incorporated to enhance the representation of pseudo labels. To promote the adaptive complementary learning,  we implement a staged training strategy that trains the student and dynamic teacher in a mutually beneficial manner, enabling their continuous optimization. Extensive experiments conducted on V2X-Sim, OPV2V, DAIR-V2X and V2V4Real datasets demonstrate that the proposed CoDTS effectively ensures an optimal balance of pseudo labels in both quality and quantity, establishing a new state-of-the-art in sparsely supervised collaborative perception.

CoDTS: Enhancing Sparsely Supervised Collaborative Perception with a Dual Teacher-Student Framework

Symbolic Regression of Integer Sequences (SRIS) aims to discover precise mathematical formulas from integer sequences. The neural machine translation-based method of SRIS trains the model using randomly generated data, and directly utilizes the trained model for inference on target sequences. However, the method often fails to effectively generalize to the target sequence, since the randomly generated data can not adequately cover the distributions of target data, i.e., there are distribution differences between them. In this work, we propose a progressive self-learning (PSL) method to explicitly capture sequence-formula distributions of the target domain. Specifically, a source domain dataset is generated by incorporating initial terms of the target domain to reduce the sequence distribution gap between the source domain and the target domain. Meanwhile, a self-learning loop strategy is adopted to improve the ability of the model to capture the sequence-formula distribution of the target domain. In this strategy, a neural machine translation model is used to learn the mappings from sequences to formulas in an end-to-end fashion. Then, this model is employed to explore candidate formulas of the target sequence using beam search. After verifying these candidate formula correctness, some of them are retained as training data for the next learning. Experimental results on OEIS datasets demonstrate that the proposed method surpasses current state-of-the-art methods in accuracy, and also discovers new formulas.

Progressive Self-Learning for Domain Adaptation on Symbolic Regression of Integer Sequences

Enabling humanoid robots to perform long-horizon mobile manipulation planning in real-world environments based on embodied perception and comprehension abilities has been a longstanding challenge. 
With the recent rise of large language models (LLMs), there has been a notable increase in the development of LLM-based planners. These approaches either utilize human-provided textual representations of the real world or heavily depend on prompt engineering to extract such representations, lacking the capability to quantitatively understand the environment, such as determining the feasibility of manipulating objects.
To address these limitations, we present the Instruction-Augmented Long-Horizon Planning (IALP) system, a novel framework that employs LLMs to generate feasible and optimal actions based on real-time sensor feedback, including grounded knowledge of the environment, in a closed-loop interaction. Distinct from prior works, our approach augments user instructions into PDDL problems by leveraging both the abstract reasoning capabilities of LLMs and grounding mechanisms.
By conducting various real-world long-horizon tasks, each consisting of seven distinct manipulatory skills, our results demonstrate that the IALP system can efficiently solve these tasks with an average success rate exceeding 80\%.
Our proposed method can operate as a high-level planner for open-world long-horizon tasks, equipping robots with substantial autonomy in unstructured environments through the utilization of multi-modal sensor inputs.

Instruction-Augmented Long-Horizon Planning: Embedding Grounding Mechanisms in Embodied Mobile Manipulation

This paper presents an experiential learning pedagogy that teaches undergraduate business management infor-mation systems students hands-on AI skills through the lens of sustainability. The learning modules aim to em-power undergraduate business students to gain interest and confidence in AI knowledge, skills, and careers, to sharpen their higher order thinking abilities, and to help them gain a deeper understanding of sustainability is-sues. Students learn AI through developing chatbots that address pressing sustainability issues within their own communities. Results of the pilot study indicate that students have increased self-efficacy in AI, more positive attitudes towards AI learning and AI-related careers, en-hanced sustainability awareness, and more confidence in their ability to innovate.

Developing Chatbots for Sustainability: Experiential Learning in an Undergraduate Business Course

Developing competency in artificial intelligence is becoming increasingly crucial for computer science (CS) students at all levels of the CS curriculum. However, most previous research focuses on advanced CS courses, as traditional introductory courses provide limited opportunities to develop AI skills and knowledge. This paper introduces an introductory CS course where students learn computational thinking through computer vision, a sub-field of AI, as an application context. The course aims to achieve computational thinking outcomes alongside critical thinking outcomes that expose students to AI approaches and their societal implications. Through experiential activities such as individual projects and reading discussions, our course seeks to balance technical learning and critical thinking goals. Our evaluation, based on pre-and post-course surveys, shows an improved sense of belonging, self-efficacy, and AI ethics awareness among students. The results suggest that an AI-focused context can enhance participation and employability, student-selected projects support self-efficacy, and ethically grounded AI instruction can be effective for interdisciplinary audiences. Students' discussions on reading assignments demonstrated deep engagement with the complex challenges in today's AI landscape. Finally, we share insights on scaling such courses for larger cohorts and improving the learning experience for introductory CS students.

Computational Thinking with Computer Vision: Developing AI Competency in an Introductory Computer Science Course

Responsible AI (RAI) is the science and practice of ensuring the design, development, use, and oversight of AI are socially sustainable---benefiting diverse stakeholders while controlling the risks. Achieving this goal requires active engagement and participation from the broader public. This paper introduces "We are AI: Taking Control of Technology," a public education course that brings the topics of AI and RAI to the general audience in a peer-learning setting. 

We outline the goals behind the course's development, discuss the multi-year iterative process that shaped its creation, and summarize its content. We also discuss two offerings of "We are AI" to an active and engaged group of librarians and professional staff at New York University, highlighting successes and areas for improvement. The course materials, including a multilingual comic book series by the same name, are publicly available and can be used independently. By sharing our experience in creating and teaching "We are AI", we aim to introduce these resources to the community of AI educators, researchers, and practitioners, supporting their public education efforts.

We Are AI: Taking Control of Technology

This talk explores the challenge of customizing large-scale AI models, particularly generative AI, on cost-effective devices with limited memory and energy resources. Modern AI models demand substantial computational power, often relying on specialized hardware such as GPUs. To address this, the talk introduces compression-aware computing, a framework enabling AI models to recognize and adapt to their compressed states while preserving performance. Compression-aware computing integrates compression techniques like sparsification, quantization, and low-rank decomposition to enhance the efficiency and accuracy of AI models, broadening these models' accessibility across diverse devices. Additionally, this talk highlights one rationale of scalable and sustainable AI in advancing Alzheimer’s research by facilitating the analysis of large single-cell transcriptomics datasets for gene-gene interaction discovery.

Compression-Aware Computing for Scalable and Sustainable AI

Probabilistic inference is a fundamental challenge in machine learning, spanning tasks from approximate Bayesian inference to generative AI. In this talk, I will present theoretically-guaranteed scalable and efficient probabilistic inference with applications in Bayesian deep learning and generative modeling. First, I will introduce a general framework that leverages modern accelerators—specifically low-precision computation—to significantly accelerate inference while preserving accuracy. Next, I will present a new framework for efficient inference in discrete domains, using gradient information to achieve much more informed and directional exploration. Finally, I will showcase experimental results demonstrating the effectiveness of these methods across various ML tasks, including Bayesian neural networks, energy-based models, and large language models.

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