pwc-archive/papers-with-abstracts huggingface dataset:
Mediffusion: Joint Diffusion for Self-Explainable Semi-Supervised Classification and Medical Image Generation
Authors: Joanna Kaleta, Paweł Skierś, Jan Dubiński, Przemysław Korzeniowski, Kamil Deja
Date: 2024-11-12 00:00:00
Tasks: counterfactual, Image Generation, Medical Image Generation

We introduce Mediffusion -- a new method for semi-supervised learning with explainable classification based on a joint diffusion model. The medical imaging domain faces unique challenges due to scarce data labelling -- insufficient for standard training, and critical nature of the applications that require high performance, confidence, and explainability of the models. In this work, we propose to tackle those challenges with a single model that combines standard classification with a diffusion-based generative task in a single shared parametrisation. By sharing representations, our model effectively learns from both labeled and unlabeled data while at the same time providing accurate explanations through counterfactual examples. In our experiments, we show that our Mediffusion achieves results comparable to recent semi-supervised methods while providing more reliable and precise explanations.
Structured Pattern Expansion with Diffusion Models
Authors: Marzia Riso, Giuseppe Vecchio, Fabio Pellacini
Date: 2024-11-12 00:00:00

Recent advances in diffusion models have significantly improved the synthesis of materials, textures, and 3D shapes. By conditioning these models via text or images, users can guide the generation, reducing the time required to create digital assets. In this paper, we address the synthesis of structured, stationary patterns, where diffusion models are generally less reliable and, more importantly, less controllable. Our approach leverages the generative capabilities of diffusion models specifically adapted for the pattern domain. It enables users to exercise direct control over the synthesis by expanding a partially hand-drawn pattern into a larger design while preserving the structure and details of the input. To enhance pattern quality, we fine-tune an image-pretrained diffusion model on structured patterns using Low-Rank Adaptation (LoRA), apply a noise rolling technique to ensure tileability, and utilize a patch-based approach to facilitate the generation of large-scale assets. We demonstrate the effectiveness of our method through a comprehensive set of experiments, showing that it outperforms existing models in generating diverse, consistent patterns that respond directly to user input.
DG-PPU: Dynamical Graphs based Post-processing of Point Clouds extracted from Knee Ultrasounds
Authors: Injune Hwang, Karthik Saravanan, Caterina V Coralli, S Jack Tu, Stephen J Mellon
Date: 2024-11-12 00:00:00
Tasks: Anatomy

Patients undergoing total knee arthroplasty (TKA) often experience non-specific anterior knee pain, arising from abnormal patellofemoral joint (PFJ) instability. Tracking PFJ motion is challenging since static imaging modalities like CT and MRI are limited by field of view and metal artefact interference. Ultrasounds offer an alternative modality for dynamic musculoskeletal imaging. We aim to achieve accurate visualisation of patellar tracking and PFJ motion, using 3D registration of point clouds extracted from ultrasound scans across different angles of joint flexion. Ultrasound images containing soft tissue are often mislabeled as bone during segmentation, resulting in noisy 3D point clouds that hinder accurate registration of the bony joint anatomy. Machine learning the intrinsic geometry of the knee bone may help us eliminate these false positives. As the intrinsic geometry of the knee does not change during PFJ motion, one may expect this to be robust across multiple angles of joint flexion. Our dynamical graphs-based post-processing algorithm (DG-PPU) is able to achieve this, creating smoother point clouds that accurately represent bony knee anatomy across different angles. After inverting these point clouds back to their original ultrasound images, we evaluated that DG-PPU outperformed manual data cleaning done by our lab technician, deleting false positives and noise with 98.2% precision across three different angles of joint flexion. DG-PPU is the first algorithm to automate the post-processing of 3D point clouds extracted from ultrasound scans. With DG-PPU, we contribute towards the development of a novel patellar mal-tracking assessment system with ultrasound, which currently does not exist.
Aligning Visual Contrastive learning models via Preference Optimization
Authors: Amirabbas Afzali, Borna Khodabandeh, Ali Rasekh, Mahyar JafariNodeh, Sepehr kazemi, Simon Gottschalk
Date: 2024-11-12 00:00:00
Tasks: Contrastive Learning, Fairness

Contrastive learning models have demonstrated impressive abilities to capture semantic similarities by aligning representations in the embedding space. However, their performance can be limited by the quality of the training data and its inherent biases. While Preference Optimization (PO) methods such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO) have been applied to align generative models with human preferences, their use in contrastive learning has yet to be explored. This paper introduces a novel method for training contrastive learning models using different PO methods to break down complex concepts. Our method systematically aligns model behavior with desired preferences, enhancing performance on the targeted task. In particular, we focus on enhancing model robustness against typographic attacks and inductive biases, commonly seen in contrastive vision-language models like CLIP. Our experiments demonstrate that models trained using PO outperform standard contrastive learning techniques while retaining their ability to handle adversarial challenges and maintain accuracy on other downstream tasks. This makes our method well-suited for tasks requiring fairness, robustness, and alignment with specific preferences. We evaluate our method for tackling typographic attacks on images and explore its ability to disentangle gender concepts and mitigate gender bias, showcasing the versatility of our approach.
Conditional regression for the Nonlinear Single-Variable Model
Authors: Yantao Wu, Mauro Maggioni
Date: 2024-11-14 00:00:00
Tasks: model, regression

Several statistical models for regression of a function $F$ on $\mathbb{R}^d$ without the statistical and computational curse of dimensionality exist, for example by imposing and exploiting geometric assumptions on the distribution of the data (e.g. that its support is low-dimensional), or strong smoothness assumptions on $F$, or a special structure $F$. Among the latter, compositional models assume $F=f\circ g$ with $g$ mapping to $\mathbb{R}^r$ with $r\ll d$, have been studied, and include classical single- and multi-index models and recent works on neural networks. While the case where $g$ is linear is rather well-understood, much less is known when $g$ is nonlinear, and in particular for which $g$'s the curse of dimensionality in estimating $F$, or both $f$ and $g$, may be circumvented. In this paper, we consider a model $F(X):=f(\Pi_\gamma X) $ where $\Pi_\gamma:\mathbb{R}^d\to[0,\rm{len}_\gamma]$ is the closest-point projection onto the parameter of a regular curve $\gamma: [0,\rm{len}_\gamma]\to\mathbb{R}^d$ and $f:[0,\rm{len}_\gamma]\to\mathbb{R}^1$. The input data $X$ is not low-dimensional, far from $\gamma$, conditioned on $\Pi_\gamma(X)$ being well-defined. The distribution of the data, $\gamma$ and $f$ are unknown. This model is a natural nonlinear generalization of the single-index model, which corresponds to $\gamma$ being a line. We propose a nonparametric estimator, based on conditional regression, and show that under suitable assumptions, the strongest of which being that $f$ is coarsely monotone, it can achieve the $one$-$dimensional$ optimal min-max rate for non-parametric regression, up to the level of noise in the observations, and be constructed in time $\mathcal{O}(d^2n\log n)$. All the constants in the learning bounds, in the minimal number of samples required for our bounds to hold, and in the computational complexity are at most low-order polynomials in $d$.
How do Machine Learning Models Change?
Authors: Joel Castaño, Rafael Cabañas, Antonio Salmerón, David Lo, Silverio Martínez-Fernández
Date: 2024-11-14 00:00:00

The proliferation of Machine Learning (ML) models and their open-source implementations has transformed Artificial Intelligence research and applications. Platforms like Hugging Face (HF) enable the development, sharing, and deployment of these models, fostering an evolving ecosystem. While previous studies have examined aspects of models hosted on platforms like HF, a comprehensive longitudinal study of how these models change remains underexplored. This study addresses this gap by utilizing both repository mining and longitudinal analysis methods to examine over 200,000 commits and 1,200 releases from over 50,000 models on HF. We replicate and extend an ML change taxonomy for classifying commits and utilize Bayesian networks to uncover patterns in commit and release activities over time. Our findings indicate that commit activities align with established data science methodologies, such as CRISP-DM, emphasizing iterative refinement and continuous improvement. Additionally, release patterns tend to consolidate significant updates, particularly in documentation, distinguishing between granular changes and milestone-based releases. Furthermore, projects with higher popularity prioritize infrastructure enhancements early in their lifecycle, and those with intensive collaboration practices exhibit improved documentation standards. These and other insights enhance the understanding of model changes on community platforms and provide valuable guidance for best practices in model maintenance.
Neural Operators Can Play Dynamic Stackelberg Games
Authors: Guillermo Alvarez, Ibrahim Ekren, Anastasis Kratsios, Xuwei Yang
Date: 2024-11-14 00:00:00

Dynamic Stackelberg games are a broad class of two-player games in which the leader acts first, and the follower chooses a response strategy to the leader's strategy. Unfortunately, only stylized Stackelberg games are explicitly solvable since the follower's best-response operator (as a function of the control of the leader) is typically analytically intractable. This paper addresses this issue by showing that the \textit{follower's best-response operator} can be approximately implemented by an \textit{attention-based neural operator}, uniformly on compact subsets of adapted open-loop controls for the leader. We further show that the value of the Stackelberg game where the follower uses the approximate best-response operator approximates the value of the original Stackelberg game. Our main result is obtained using our universal approximation theorem for attention-based neural operators between spaces of square-integrable adapted stochastic processes, as well as stability results for a general class of Stackelberg games.
MCCE: Missingness-aware Causal Concept Explainer
Authors: Jifan Gao, Guanhua Chen
Date: 2024-11-14 00:00:00
Tasks: Interpretable Machine Learning

Causal concept effect estimation is gaining increasing interest in the field of interpretable machine learning. This general approach explains the behaviors of machine learning models by estimating the causal effect of human-understandable concepts, which represent high-level knowledge more comprehensibly than raw inputs like tokens. However, existing causal concept effect explanation methods assume complete observation of all concepts involved within the dataset, which can fail in practice due to incomplete annotations or missing concept data. We theoretically demonstrate that unobserved concepts can bias the estimation of the causal effects of observed concepts. To address this limitation, we introduce the Missingness-aware Causal Concept Explainer (MCCE), a novel framework specifically designed to estimate causal concept effects when not all concepts are observable. Our framework learns to account for residual bias resulting from missing concepts and utilizes a linear predictor to model the relationships between these concepts and the outputs of black-box machine learning models. It can offer explanations on both local and global levels. We conduct validations using a real-world dataset, demonstrating that MCCE achieves promising performance compared to state-of-the-art explanation methods in causal concept effect estimation.
Counterfactual Uncertainty Quantification of Factual Estimand of Efficacy from Before-and-After Treatment Repeated Measures Randomized Controlled Trials
Authors: Xingya Wang, Yang Han, Yushi Liu, Szu-Yu Tang, Jason C. Hsu
Date: 2024-11-14 00:00:00
Tasks: counterfactual, Uncertainty Quantification

The ideal estimand for comparing treatment $Rx$ with a control $C$ is the $\textit{counterfactual}$ efficacy $Rx:C$, the expected differential outcome between $Rx$ and $C$ if each patient were given $\textit{both}$. One hundred years ago, Neyman (1923a) proved unbiased $\textit{point estimation}$ of counterfactual efficacy from designed $\textit{factual}$ experiments is achievable. But he left the determination of how much might the counterfactual variance of this estimate be smaller than the factual variance as an open challenge. This article shows $\textit{counterfactual}$ uncertainty quantification (CUQ), quantifying uncertainty for factual point estimates but in a counterfactual setting, is achievable for Randomized Controlled Trials (RCTs) with Before-and-After treatment Repeated Measures which are common in many therapeutic areas. We achieve CUQ whose variability is typically smaller than factual UQ by creating a new statistical modeling principle called ETZ. We urge caution in using predictors with measurement error which violates standard regression assumption and can cause $\textit{attenuation}$ in estimating treatment effects. Fortunately, we prove that, for traditional medicine in general, and for targeted therapy with efficacy defined as averaged over the population, counterfactual point estimation is unbiased. However, for both Real Human and Digital Twins approaches, predicting treatment effect in $\textit{subgroups}$ may have attenuation bias.
Local deployment of large-scale music AI models on commodity hardware
Authors: Xun Zhou, Charlie Ruan, Zihe Zhao, Tianqi Chen, Chris Donahue
Date: 2024-11-14 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

We present the MIDInfinite, a web application capable of generating symbolic music using a large-scale generative AI model locally on commodity hardware. Creating this demo involved porting the Anticipatory Music Transformer, a large language model (LLM) pre-trained on the Lakh MIDI dataset, to the Machine Learning Compilation (MLC) framework. Once the model is ported, MLC facilitates inference on a variety of runtimes including C++, mobile, and the browser. We envision that MLC has the potential to bridge the gap between the landscape of increasingly capable music AI models and technology more familiar to music software developers. As a proof of concept, we build a web application that allows users to generate endless streams of multi-instrumental MIDI in the browser, either from scratch or conditioned on a prompt. On commodity hardware (an M3 Macbook Pro), our demo can generate 51 notes per second, which is faster than real-time playback for 72.9% of generations, and increases to 86.3% with 2 seconds of upfront buffering.
MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust Quantitative Connectivity through Harmonized Preprocessing of Diffusion MRI
Authors: Nancy R. Newlin, Kurt Schilling, Serge Koudoro, Bramsh Qamar Chandio, Praitayini Kanakaraj, Daniel Moyer, Claire E. Kelly, Sila Genc, Jian Chen, Joseph Yuan-Mou Yang, Ye Wu, Yifei He, Jiawei Zhang, Qingrun Zeng, Fan Zhang, Nagesh Adluru, Vishwesh Nath, Sudhir Pathak, Walter Schneider, Anurag Gade, Yogesh Rathi, Tom Hendriks, Anna Vilanova, Maxime Chamberland, Tomasz Pieciak, Dominika Ciupek, Antonio Tristán Vega, Santiago Aja-Fernández, Maciej Malawski, Gani Ouedraogo, Julia Machnio, Christian Ewert, Paul M. Thompson, Neda Jahanshad, Eleftherios Garyfallidis, Bennett A. Landman
Date: 2024-11-14 00:00:00
Tasks: Diffusion  MRI

White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (DW-MRI) to qualitatively identify changes in white matter microstructure and connectivity. Yet, quantitative analysis of DW-MRI data is hindered by inconsistencies stemming from varying acquisition protocols. There is a pressing need to harmonize the preprocessing of DW-MRI datasets to ensure the derivation of robust quantitative diffusion metrics across acquisitions. In the MICCAI-CDMRI 2023 QuantConn challenge, participants were provided raw data from the same individuals collected on the same scanner but with two different acquisitions and tasked with preprocessing the DW-MRI to minimize acquisition differences while retaining biological variation. Submissions are evaluated on the reproducibility and comparability of cross-acquisition bundle-wise microstructure measures, bundle shape features, and connectomics. The key innovations of the QuantConn challenge are that (1) we assess bundles and tractography in the context of harmonization for the first time, (2) we assess connectomics in the context of harmonization for the first time, and (3) we have 10x additional subjects over prior harmonization challenge, MUSHAC and 100x over SuperMUDI. We find that bundle surface area, fractional anisotropy, connectome assortativity, betweenness centrality, edge count, modularity, nodal strength, and participation coefficient measures are most biased by acquisition and that machine learning voxel-wise correction, RISH mapping, and NeSH methods effectively reduce these biases. In addition, microstructure measures AD, MD, RD, bundle length, connectome density, efficiency, and path length are least biased by these acquisition differences.
The Moral Foundations Weibo Corpus
Authors: Renjie Cao, Miaoyan Hu, Jiahan Wei, Baha Ihnaini
Date: 2024-11-14 00:00:00
Tasks: Decision Making, Sentiment Analysis, Sentiment Classification

Moral sentiments expressed in natural language significantly influence both online and offline environments, shaping behavioral styles and interaction patterns, including social media selfpresentation, cyberbullying, adherence to social norms, and ethical decision-making. To effectively measure moral sentiments in natural language processing texts, it is crucial to utilize large, annotated datasets that provide nuanced understanding for accurate analysis and modeltraining. However, existing corpora, while valuable, often face linguistic limitations. To address this gap in the Chinese language domain,we introduce the Moral Foundation Weibo Corpus. This corpus consists of 25,671 Chinese comments on Weibo, encompassing six diverse topic areas. Each comment is manually annotated by at least three systematically trained annotators based on ten moral categories derived from a grounded theory of morality. To assess annotator reliability, we present the kappa testresults, a gold standard for measuring consistency. Additionally, we apply several the latest large language models to supplement the manual annotations, conducting analytical experiments to compare their performance and report baseline results for moral sentiment classification.
Latency Optimization in LEO Satellite Communications with Hybrid Beam Pattern and Interference Control
Authors: Qianqian Zhang, Ye Hu, Minchae Jung
Date: 2024-11-14 00:00:00
Tasks: Graph Generation, Scheduling

The rapid advancement of low Earth orbit (LEO) satellite communication systems has significantly enhanced global connectivity, offering high-capacity, low-latency services crucial for next-generation applications. However, the dense configuration of LEO constellations poses challenges in resource allocation optimization and interference management, complicating coexistence with other communication systems. To address these limitations, this paper proposes a novel framework for optimizing the beam scheduling and resource allocation in multi-beam LEO systems. To satisfy the uneven terrestrial traffic demand, a hybrid beam pattern is employed to enhance the downlink quality of service and minimize the transmission latency from LEO satellites to ground user terminals. Additionally, a dynamic co-channel interference (CCI) control mechanism is developed to mitigate inter-beam interference within the LEO constellation and limit cross-system interference affecting protected users from other networks. The problem of user-beam-frequency allocation with power optimization is formulated as a mixed-integer dynamic programming model and solved using a low-complexity neural network-based graph generation algorithm. Simulation results show that the proposed approach outperforms the baseline methods of full frequency reuse and single-channel transmission, and highlights the potential for further performance improvement with multi-user transmissions.
Expert Study on Interpretable Machine Learning Models with Missing Data
Authors: Lena Stempfle, Arthur James, Julie Josse, Tobias Gauss, Fredrik D. Johansson
Date: 2024-11-14 00:00:00
Tasks: Decision Making, Imputation, Interpretable Machine Learning, Missing Values

Inherently interpretable machine learning (IML) models provide valuable insights for clinical decision-making but face challenges when features have missing values. Classical solutions like imputation or excluding incomplete records are often unsuitable in applications where values are missing at test time. In this work, we conducted a survey with 71 clinicians from 29 trauma centers across France, including 20 complete responses to study the interaction between medical professionals and IML applied to data with missing values. This provided valuable insights into how missing data is interpreted in clinical machine learning. We used the prediction of hemorrhagic shock as a concrete example to gauge the willingness and readiness of the participants to adopt IML models from three classes of methods. Our findings show that, while clinicians value interpretability and are familiar with common IML methods, classical imputation techniques often misalign with their intuition, and that models that natively handle missing values are preferred. These results emphasize the need to integrate clinical intuition into future IML models for better human-computer interaction.