Canada

Ensuring a machine learning model’s trustworthiness is crucial to prevent potential harm. One way to foster trust is through the formal verification of the model’s adherence to essential design requirements. However, this approach relies on well-defined, application-domain-centric criteria with which to test the model, and such specifications may be cumbersome to collect in practice. We propose a data-driven approach for creating specifications to evaluate a trained model effectively. Implementing this framework allows us to prove that the model will exhibit safe behavior while minimizing the false-positive prediction rate. This strategy enhances predictive accuracy and safety, providing deeper insight into the model’s strengths and weaknesses, and promotes trust through a systematic approach.

AAAI 2024

Data-Driven Discovery of Design Specifications (Student Abstract)

design specification

model certification

data-driven

trustworthiness

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. 

Recent research has shown a growing interest in per-instance differential privacy (pDP), highlighting the fact that each data instance within a dataset may incur distinct levels of privacy loss.
However, conventional additive noise mechanisms apply identical noise to all query outputs, thereby deteriorating data statistics.
In this study, we propose an instance-wise Laplace mechanism, which adds non-identical Laplace noises to the query output for each data instance.
A challenge arises from the complex interaction of additive noise, where the noise introduced to individual instances impacts the pDP of other instances, adding complexity and resilience to straightforward solutions.
To tackle this problem, we introduce an instance-wise Laplace mechanism algorithm via deep reinforcement learning and validate its ability to better preserve data statistics on a real dataset, compared to the original Laplace mechanism.

Instance-Wise Laplace Mechanism via Deep Reinforcement Learning (Student Abstract)

We consider a platform in a two-sided market with unit-supply sellers and unit-demand buyers. Each buyer can transact with a subset of sellers it knows off platform and another seller that the platform recommends. Given the choice of sellers, transactions and prices form a competitive equilibrium. The platform selects one seller for each buyer, and charges a fixed percentage of prices to all transactions that it recommends. The platform seeks to maximize total revenue.

We show that the platform's problem is NP-hard, even when each buyer knows at most two buyers off platform. Finally, when each buyer values all sellers equally and knows only one buyer off platform, we provide a polynomial time algorithm that optimally solves the problem.

Strategic Recommendation: Revenue Optimal Matching for Online Platforms (Student Abstract) | VIDEO

Rendering of complex scenes from software such as Blender is time consuming,  but corresponding auxiliary data such as depth or object segmentation maps are relatively fast to generate. The auxiliary data also provides a wealth of information for tasks such as optical flow prediction.

In this paper we present the QuickRender dataset, a collection of procedurally generated scenes rendered into over 5,000 sequential image triplets along with accompanying auxiliary data. The goal of this dataset is to provide a diversity of scenes and motion while maintaining realistic behaviours. A sample application using this dataset to perform single image super resolution is also presented.

The dataset and related source code can be found at https://github.com/MP-mtroyal/MetaSRGAN.

QuickRender: A Photorealistic Procedurally Generated Dataset with Applications to Super Resolution (Student Abstract)

Graph spectral clustering is a fundamental technique in data analysis, which utilizes eigenpairs of the Laplacian matrix to partition graph vertices into clusters. However, classical spectral clustering algorithms require eigendecomposition of the Laplacian matrix, which has cubic time complexity. In
this work, we describe pass-efficient spectral clustering algorithms that leverage recent advances in randomized eigendecomposition and the structure of the graph vertex-edge matrix. Furthermore, we derive formulas for their efficient implementation. The resulting algorithms have a linear time complexity with respect to the number of vertices and edges and pass over the graph constant times, making them suitable for processing large graphs stored on slow memory. Experiments validate the accuracy and efficiency of the algorithms.

Pass-Efficient Algorithms for Graph Spectral Clustering (Student Abstract)

We propose an automated negotiation for a reinforcement learning agent to adapt the agent to unexpected situations such as demand changes in supply chain management (SCM).
Existing studies that consider reinforcement learning and SCM assume a centralized environment where the coordination of chain components is hierarchical rather than through negotiations between agents.
This study focused on a negotiation agent that considered the value function of reinforcement learning for SCM as its utility function in automated negotiation.
We demonstrated that the proposed approach could avoid inventory shortages under increased demand requests from the terminal customer.

Coordination of Emergent Demand Changes via Value-Based Negotiation for Supply Chain Management (Student Abstract)

Recently there has been a proliferation of intrinsic motivation (IM) reward shaping methods to learn in complex and sparse-reward environments. These methods can often inadvertently change the set of optimal policies in an environment, leading to suboptimal behavior. Previous work on mitigating the risks of reward shaping, particularly through potential-based reward shaping (PBRS), has not been applicable to many IM methods, as they are often complex, trainable functions themselves, and therefore dependent on a wider set of variables than the traditional reward functions that PBRS was developed for. We present an extension to PBRS that we show preserves the set of optimal policies under a more general set of functions than has been previously demonstrated. We also present Potential-Based Intrinsic Motivation (PBIM), a method for converting IM rewards into a potential-based form that are useable without altering the set of optimal policies. Testing in the MiniGrid DoorKey environment, we demonstrate that PBIM successfully prevents the agent from converging to a suboptimal policy and can speed up training.

Potential-Based Reward Shaping for Intrinsic Motivation (Student Abstract) | VIDEO

Recent advances in image-to-image translation involve the integration of non-visual imagery in deep models. Non-visual sensors, although more costly, often produce low-resolution images. To combat this, methods using RGB images to enhance the resolution of these modalities have been introduced. Fusing these modalities to achieve high-resolution results demands models with millions of parameters and extended inference times. We present LaMAR, a lightweight model. It employs Laplacian image pyramids combined with a low-resolution thermal image for Guided Thermal Super Resolution. By decomposing the RGB image into a Laplacian pyramid, LaMAR preserves image details and avoids high-resolution feature map computations, ensuring efficiency. With faster inference times and fewer parameters, our model demonstrates state-of-the-art results.

LaMAR: Laplacian Pyramid for Multimodal Adaptive Super Resolution (Student Abstract)

Despite Siamese trackers’ substantial potential, they offer sub-optimal tracking performance in low-resolution (LR) contexts. We introduce a Random Noise Salient Feature Fusion Learning Network to address this issue. This method integrates random noise-infused feature maps into a similaritylearning matching model. This integration acts as an effective regularization technique, enhancing the network’s generalization capabilities in LR environments. Additionally, by integrating attention mechanisms, we enhance the discriminative ability of the network, assigning more weights to important features. This directs the network’s focus toward the most salient regions of the feature map, ensuring improved accuracy without a significant increase in parameter overhead, and maintaining a high operating speed. To validate the effectiveness of our method, we performed qualitative and quantitative comparisons with state-of-the-art (SOTA) trackers.

Learning Random Noise Salient Feature Fusion Siamese Network for Low-Resolution Object Tracking (Student Abstract)

Evolutionary Algorithms (EA) have been leveraged to tackle the challenges faced while using GANs such as mode collapse, vanishing gradient, latent space search, etc. However, the existing techniques of using EA with GANs operate backpropagation and EA in isolation from each other, leaving ample room for further exploration. This paper creates a collaborative bridge between EA and GANs by exploring a neuroevolution method for utilising both EA and backpropagation-based optimisation, simultaneously, for a multi-generator GAN architecture. Experiments conducted using a standard dataset with variants of the proposed method highlight the towering impact of each of the components involved in the proposed method.

Neuroevolution of a Multi-Generator GAN (Student Abstract)

The COVID-19 pandemic has exacerbated the challenges faced by healthcare delivery in developing nations, placing additional strain on already fragile infrastructure and healthcare systems. This has prompted an increased reliance on lay healthcare workers (LHWs) to meet the surging demand for services. Due to limited formal training, many LHWs have resorted to using unreliable sources, such as internet searches, to access medical information.

Large language models (LLMs) offer a promising opportunity to support LHWs by providing accurate, context-sensitive information for improving healthcare delivery, provided they are appropriately fine-tuned on domain-specific multilingual data. This paper delves into critical issues and presents potential solutions for developing LLM-powered virtual assistants tailored to LHWs serving Telugu and Hindi-speaking populations. Key focal points include the customization of language and content to suit local contexts, the integration of feedback mechanisms to continuously enhance assistance quality, and the delicate balance between automation and human oversight.

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Instance-Wise Laplace Mechanism via Deep Reinforcement Learning (Student Abstract)

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Instance-Wise Laplace Mechanism via Deep Reinforcement Learning (Student Abstract)

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PRESENTATIONS

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