12,794 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
EHR-KnowGen: Knowledge-enhanced multimodal learning for disease diagnosis generation
Electronic health records (EHRs) contain diverse patient information, including medical notes, clinical events, and laboratory test results. Integrating this multimodal data can improve disease diagnoses using deep learning models. However, effectively combining different modalities for diagnosis remains challenging. Previous approaches, such as attention mechanisms and contrastive learning, have attempted to address this but do not fully integrate the modalities into a unified feature space. This paper presents EHR-KnowGen, a multimodal learning model enhanced with external domain knowledge, for improved disease diagnosis generation from diverse patient information in EHRs. Unlike previous approaches, our model integrates different modalities into a unified feature space with soft prompts learning and leverages large language models (LLMs) to generate disease diagnoses. By incorporating external domain knowledge from different levels of granularity, we enhance the extraction and fusion of multimodal information, resulting in more accurate diagnosis generation. Experimental results on real-world EHR datasets demonstrate the superiority of our generative model over comparative methods, providing explainable evidence to enhance the understanding of diagnosis results
Policy Space Diversity for Non-Transitive Games
Policy-Space Response Oracles (PSRO) is an influential algorithm framework
for approximating a Nash Equilibrium (NE) in multi-agent non-transitive games.
Many previous studies have been trying to promote policy diversity in PSRO. A
major weakness in existing diversity metrics is that a more diverse (according
to their diversity metrics) population does not necessarily mean (as we proved
in the paper) a better approximation to a NE. To alleviate this problem, we
propose a new diversity metric, the improvement of which guarantees a better
approximation to a NE. Meanwhile, we develop a practical and well-justified
method to optimize our diversity metric using only state-action samples. By
incorporating our diversity regularization into the best response solving in
PSRO, we obtain a new PSRO variant, Policy Space Diversity PSRO (PSD-PSRO). We
present the convergence property of PSD-PSRO. Empirically, extensive
experiments on various games demonstrate that PSD-PSRO is more effective in
producing significantly less exploitable policies than state-of-the-art PSRO
variants
Modular lifelong machine learning
Deep learning has drastically improved the state-of-the-art in many important fields, including computer vision and natural language processing (LeCun et al., 2015). However, it is expensive to train a deep neural network on a machine learning problem. The overall training cost further increases when one wants to solve additional problems. Lifelong machine learning (LML) develops algorithms that aim to efficiently learn to solve a sequence of problems, which become available one at a time. New problems are solved with less resources by transferring previously learned knowledge. At the same time, an LML algorithm needs to retain good performance on all encountered problems, thus avoiding catastrophic forgetting. Current approaches do not possess all the desired properties of an LML algorithm. First, they primarily focus on preventing catastrophic forgetting (Diaz-Rodriguez et al., 2018; Delange et al., 2021). As a result, they neglect some knowledge transfer properties. Furthermore, they assume that all problems in a sequence share the same input space. Finally, scaling these methods to a large sequence of problems remains a challenge.
Modular approaches to deep learning decompose a deep neural network into sub-networks, referred to as modules. Each module can then be trained to perform an atomic transformation, specialised in processing a distinct subset of inputs. This modular approach to storing knowledge makes it easy to only reuse the subset of modules which are useful for the task at hand.
This thesis introduces a line of research which demonstrates the merits of a modular approach to lifelong machine learning, and its ability to address the aforementioned shortcomings of other methods. Compared to previous work, we show that a modular approach can be used to achieve more LML properties than previously demonstrated. Furthermore, we develop tools which allow modular LML algorithms to scale in order to retain said properties on longer sequences of problems.
First, we introduce HOUDINI, a neurosymbolic framework for modular LML. HOUDINI represents modular deep neural networks as functional programs and accumulates a library of pre-trained modules over a sequence of problems. Given a new problem, we use program synthesis to select a suitable neural architecture, as well as a high-performing combination of pre-trained and new modules. We show that our approach has most of the properties desired from an LML algorithm. Notably, it can perform forward transfer, avoid negative transfer and prevent catastrophic forgetting, even across problems with disparate input domains and problems which require different neural architectures.
Second, we produce a modular LML algorithm which retains the properties of HOUDINI but can also scale to longer sequences of problems. To this end, we fix the choice of a neural architecture and introduce a probabilistic search framework, PICLE, for searching through different module combinations. To apply PICLE, we introduce two probabilistic models over neural modules which allows us to efficiently identify promising module combinations.
Third, we phrase the search over module combinations in modular LML as black-box optimisation, which allows one to make use of methods from the setting of hyperparameter optimisation (HPO). We then develop a new HPO method which marries a multi-fidelity approach with model-based optimisation. We demonstrate that this leads to improvement in anytime performance in the HPO setting and discuss how this can in turn be used to augment modular LML methods.
Overall, this thesis identifies a number of important LML properties, which have not all been attained in past methods, and presents an LML algorithm which can achieve all of them, apart from backward transfer
Online Network Source Optimization with Graph-Kernel MAB
We propose Grab-UCB, a graph-kernel multi-arms bandit algorithm to learn
online the optimal source placement in large scale networks, such that the
reward obtained from a priori unknown network processes is maximized. The
uncertainty calls for online learning, which suffers however from the curse of
dimensionality. To achieve sample efficiency, we describe the network processes
with an adaptive graph dictionary model, which typically leads to sparse
spectral representations. This enables a data-efficient learning framework,
whose learning rate scales with the dimension of the spectral representation
model instead of the one of the network. We then propose Grab-UCB, an online
sequential decision strategy that learns the parameters of the spectral
representation while optimizing the action strategy. We derive the performance
guarantees that depend on network parameters, which further influence the
learning curve of the sequential decision strategy We introduce a
computationally simplified solving method, Grab-arm-Light, an algorithm that
walks along the edges of the polytope representing the objective function.
Simulations results show that the proposed online learning algorithm
outperforms baseline offline methods that typically separate the learning phase
from the testing one. The results confirm the theoretical findings, and further
highlight the gain of the proposed online learning strategy in terms of
cumulative regret, sample efficiency and computational complexity
Evaluation Methodologies in Software Protection Research
Man-at-the-end (MATE) attackers have full control over the system on which
the attacked software runs, and try to break the confidentiality or integrity
of assets embedded in the software. Both companies and malware authors want to
prevent such attacks. This has driven an arms race between attackers and
defenders, resulting in a plethora of different protection and analysis
methods. However, it remains difficult to measure the strength of protections
because MATE attackers can reach their goals in many different ways and a
universally accepted evaluation methodology does not exist. This survey
systematically reviews the evaluation methodologies of papers on obfuscation, a
major class of protections against MATE attacks. For 572 papers, we collected
113 aspects of their evaluation methodologies, ranging from sample set types
and sizes, over sample treatment, to performed measurements. We provide
detailed insights into how the academic state of the art evaluates both the
protections and analyses thereon. In summary, there is a clear need for better
evaluation methodologies. We identify nine challenges for software protection
evaluations, which represent threats to the validity, reproducibility, and
interpretation of research results in the context of MATE attacks
Initial Value Problem Enhanced Sampling for Closed-Loop Optimal Control Design with Deep Neural Networks
Closed-loop optimal control design for high-dimensional nonlinear systems has
been a long-standing challenge. Traditional methods, such as solving the
associated Hamilton-Jacobi-Bellman equation, suffer from the curse of
dimensionality. Recent literature proposed a new promising approach based on
supervised learning, by leveraging powerful open-loop optimal control solvers
to generate training data and neural networks as efficient high-dimensional
function approximators to fit the closed-loop optimal control. This approach
successfully handles certain high-dimensional optimal control problems but
still performs poorly on more challenging problems. One of the crucial reasons
for the failure is the so-called distribution mismatch phenomenon brought by
the controlled dynamics. In this paper, we investigate this phenomenon and
propose the initial value problem enhanced sampling method to mitigate this
problem. We theoretically prove that this sampling strategy improves over the
vanilla strategy on the classical linear-quadratic regulator by a factor
proportional to the total time duration. We further numerically demonstrate
that the proposed sampling strategy significantly improves the performance on
tested control problems, including the optimal landing problem of a quadrotor
and the optimal reaching problem of a 7 DoF manipulator
TabR: Unlocking the Power of Retrieval-Augmented Tabular Deep Learning
Deep learning (DL) models for tabular data problems are receiving
increasingly more attention, while the algorithms based on gradient-boosted
decision trees (GBDT) remain a strong go-to solution. Following the recent
trends in other domains, such as natural language processing and computer
vision, several retrieval-augmented tabular DL models have been recently
proposed. For a given target object, a retrieval-based model retrieves other
relevant objects, such as the nearest neighbors, from the available (training)
data and uses their features or even labels to make a better prediction.
However, we show that the existing retrieval-based tabular DL solutions provide
only minor, if any, benefits over the properly tuned simple retrieval-free
baselines. Thus, it remains unclear whether the retrieval-based approach is a
worthy direction for tabular DL.
In this work, we give a strong positive answer to this question. We start by
incrementally augmenting a simple feed-forward architecture with an
attention-like retrieval component similar to those of many (tabular)
retrieval-based models. Then, we highlight several details of the attention
mechanism that turn out to have a massive impact on the performance on tabular
data problems, but that were not explored in prior work. As a result, we design
TabR -- a simple retrieval-based tabular DL model which, on a set of public
benchmarks, demonstrates the best average performance among tabular DL models,
becomes the new state-of-the-art on several datasets, and even outperforms GBDT
models on the recently proposed ``GBDT-friendly'' benchmark (see the first
figure).Comment: Code: https://github.com/yandex-research/tabular-dl-tab
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