45 accepted papers
Although Large Language Models (LLMs) have demonstrated impressive formal reasoning abilities, they often break down when problems require complex proof planning. One promising approach for improving LLM reasoning abilities involves translating problems into formal logic and using a logic solver. Al…
Recent efforts to improve the reasoning abilities of Large Language Models (LLMs) have focused on integrating formal logic solvers within neurosymbolic frameworks. A key challenge is that formal solvers lack commonsense world knowledge, preventing them from making reasoning steps that humans find ob…
Pre-trained large language models (LLMs) show promise for robotic task planning but often struggle to guarantee correctness in long-horizon problems. Task and motion planning (TAMP) addresses this by grounding symbolic plans in low-level execution, yet it relies heavily on manually engineered planni…
Large Language Models (LLMs) have recently shown strong promise for robotic task planning, particularly through automatic planning domain generation. Planning domains are brittle under imperfect logical states and perception noise; prior approaches largely treat generated planning domains as plan ut…
Large language models (LLMs) have achieved impressive results on complex reasoning tasks, but their high inference cost remains a major barrier to real-world deployment. A promising solution is to use cascaded inference, where small, cheap models handle easy queries, and only the hardest examples ar…
Large language models (LLMs) contain substantial factual knowledge which is commonly elicited by multiple-choice question-answering prompts. Internally, such models process the prompt through multiple transformer layers, building varying representations of the problem within its hidden states. Ultim…
Recent years have witnessed the emergence of a spectrum of foundation models, covering a broad range of capabilities and costs. Often, we effectively use foundation models as feature generators and train classifiers that use the outputs of these models to make decisions. In this paper, we consider a…
Online deep learning tackles the challenge of learning from data streams by balancing two competing goals: fast learning and deep learning. However, existing research primarily emphasizes deep learning solutions, which are more adept at handling the ''deep'' aspect than the ''fast'' aspect of online…
Koopman operator theory provides a framework for nonlinear dynamical system analysis and time-series forecasting by mapping dynamics to a space of real-valued measurement functions, enabling a linear operator representation. Despite the advantage of linearity, the operator is generally infinite-dime…
A significant challenge in maintaining real-world machine learning models is responding to the continuous and unpredictable evolution of data. Most practitioners are faced with the difficult question: when should I retrain or update my machine learning model? This seemingly straightforward problem i…
The existing definitions of graph convolution, either from spatial or spectral perspectives, are inflexible and not unified. Defining a general convolution operator in the graph domain is challenging due to the lack of canonical coordinates, the presence of irregular structures, and the properties o…
As we expect to see a rapid integration of generative models in our day to day lives, the development of rigorous methods of evaluation and analysis for generative models has never been more pressing. Multiple works have highlighted the shortcomings of widely used metrics and exposed how they fail t…
Efficiently determining the satisfiability of a boolean equation --- known as the SAT problem for brevity --- is crucial in various industrial problems. Recently, the advent of deep learning methods has introduced significant potential for enhancing SAT solving. However, a major barrier to the adva…
Neural Temporal Point Processes (TPPs) have emerged as the primary framework for predicting sequences of events that occur at irregular time intervals, but their sequential nature can hamper performance for long-horizon forecasts. To address this, we introduce a novel approach that incorporates a di…
Large pretrained models, coupled with fine-tuning, are slowly becoming established as the dominant architecture in machine learning. Even though these models offer impressive performance, their practical application is often limited by the prohibitive amount of resources required for $\textit{every}…
The performance of transformers for time-series forecasting has improved significantly. Recent architectures learn complex temporal patterns by segmenting a time-series into patches and using the patches as tokens. The patch size controls the ability of transformers to learn the temporal patterns at…
Offline model-based optimization aims to maximize a black-box objective function with a static dataset of designs and their scores. In this paper, we focus on biological sequence design to maximize some sequence score. A recent approach employs bidirectional learning, combining a forward mapping for…
Learning a categorical distribution comes with its own set of challenges. A successful approach taken by state-of-the-art works is to cast the problem in a continuous domain to take advantage of the impressive performance of the generative models for continuous data. Amongst them are the recently em…
The machine learning community has mainly relied on real data to benchmark algorithms as it provides compelling evidence of model applicability. Evaluation on synthetic datasets can be a powerful tool to provide a better understanding of a model’s strengths, weaknesses and overall capabilities. Gain…
Transformers for graph data are increasingly widely studied and successful in numerous learning tasks. Graph inductive biases are crucial for Graph Transformers, and previous works incorporate them using message-passing modules and/or positional encodings. However, Graph Transformers that use messag…
The mobile communication enabled by cellular networks is the one of the main foundations of our modern society. Optimizing the performance of cellular networks and providing massive connectivity with improved coverage and user experience has a considerable social and economic impact on our daily lif…
We describe a new setting for learning a deep graph generative model (GGM) from aggregate graph statistics, rather than from the graph adjacency matrix. Matching the statistics of observed training graphs is the main approach for learning traditional GGMs (e.g, BTER, Chung-Lu, and Erdos-Renyi model…
One of the most crucial concerns in Neural Architecture Search (NAS) is the amount of resources and time required for the search. In this paper we address the problem of training-free (or zero-shot) search. We propose a generalized architecture search framework called enhanced training-free neural a…
Contrastive learning has emerged as a premier method for learning representations with or without supervision. Recent studies have shown its utility in graph representation learning for pre-training. Despite successes, the understanding of how to design effective graph augmentations that can capture…
Recommender systems now consume large-scale data and play a significant role in improving user experience. Graph Neural Networks (GNNs) have emerged as one of the most effective recommender system models because they model the rich relational information. The ever-growing volume of data can make tra…
Multiple Instance Learning (MIL) is a weakly supervised learning problem where the aim is to assign labels to sets or bags of instances, as opposed to traditional supervised learning where each instance is assumed to be independent and identically distributed (IID) and is to be labeled individually.…
In offline model-based optimization, we strive to maximize a black-box objective function by only leveraging a static dataset of designs and their scores. This problem setting arises in numerous fields including the design of materials, robots, DNAs, proteins, etc. Recent approaches train a deep neu…
A well-known challenge associated with the multi-label classification problem is modelling dependencies between labels. Most attempts at modelling label dependencies focus on co-occurrences, ignoring the valuable information that can be extracted by detecting label subsets that rarely occur together…
Graph neural network (GNN) models achieve superior performance when classifying nodes in graph-structured data. Given that state-of-the-art GNNs share many similarities with their CNN cousins and that CNNs suffer adversarial vulnerabilities, there has also been interest in exploring analogous vulner…
Forecasting of multivariate time-series is an important problem that has applications in traffic management, cellular network configuration, and quantitative finance. A special case of the problem arises when there is a graph available that captures the relationships between the time-series. In this…
Personalized recommender systems are increasingly important as more content and services become available and users struggle to identify what might interest them. Thanks to the ability for providing rich information, knowledge graphs (KGs) are being incorporated to enhance the recommendation perform…
Spatio-temporal forecasting has numerous applications in analyzing wireless, traffic, and financial networks. Many classical statistical models often fall short in handling the complexity and high non-linearity present in time-series data. Recent advances in deep learning allow for better modelling…
Node classification in attributed graphs is an important task in multiple practical settings, but it can often be difficult or expensive to obtain labels. Active learning can improve the achieved classification performance for a given budget on the number of queried labels. The best existing methods…
Graphs are ubiquitous in modelling relationalstructures. Recent endeavours in machine learningfor graph structured data have led to manyarchitectures and learning algorithms. However,the graph used by these algorithms is oftenconstructed based on inaccurate modellingassumptions and/or noisy data. As…
Improving disease outcome prediction can greatly aid in the strategic deployment of secondary prevention approaches. We develop a method to predict the evolution of diseases by taking into account personal attributes of the subjects and their relationships with medical examination results. Our appro…
Community detection from graphs has many applications in machine learning, biological and social sciences. While there is a broad spectrum of literature based on various approaches, recently there has been a significant focus on inference algorithms for statistical models of community structure. The…
Decentralized receding horizon control (D-RHC) provides a mechanism for coordination in multiagent settings without a centralized command center. However, combining a set of different goals, costs, and constraints to form an efficient optimization objective for D-RHC can be difficult. To allay this…
Particle filters has become a standard tool for state estimation in nonlinear systems. However, their performance usually deteriorates if the dimension of state space is high or the measurements are highly informative. A major challenge is to construct a proposal density that is well matched to the…
In this paper we derive a particle flow particle filter implementation of the δ-Generalized Labeled Multi-Bernoulli (δ-GLMB) filter. The bootstrap particle filter δ-GLMB suffers from weight degeneracy for high-dimensional state systems or low measurement noise. In order to avoid weight degeneracy, w…
Particle filters are among the most effective filtering algorithms for nonlinear and non-Gaussian models. When the state dimension is high, they are known to suffer from weight degeneracy. Sequential Markov chain Monte Carlo (SMCMC) methods have been proposed as an alternative sequential inference t…
We present a distributed cardinalized probability hypothesis density (CPHD) filter for multi-sensor multi-target tracking. Each sensor runs a single-sensor CPHD filter to compute the probability hypothesis density (PHD) function and cardinality distribution using only its own measurements and then f…
Particle flow algorithms have been developed as an alternative to particle filtering. In these algorithms, there is no importance sampling, and particles are migrated from the prior to the posterior via a flow, described by differential equations. Aside from a few special cases, implementations invo…
Microwave breast cancer detection involves analysing the scattered waveforms of microwave signals that are propagated into the breast. We have developed a microwave-radar time-domain system and performed clinical trials using a prototype. This paper presents a classification architecture based on co…
Random finite set (RFS) based filters such as the cardinalized probability hypothesis density (CPHD) filter have been successfully applied to the problem of single sensor multitarget tracking. Various multisensor extensions of these filters have been proposed in the literature, but exact update equa…
We introduce an online, time-dependent clustering algorithm that employs a dynamic probabilistic topic model. The proposed algorithm can handle data that evolves over time and strives to capture the evolution of clusters in the dataset. It addresses the case where the entire dataset is not available…