GenAI4ELab/papercli-papers huggingface dataset:
xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology
Authors: Julius Hense; Mina Jamshidi Idaji; Oliver Eberle; Thomas Schnake; Jonas Dippel; Laure Ciernik; Oliver Buchstab; Andreas Mock; Frederick Klauschen; Klaus Robert Muller
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Multiple instance learning (MIL) is an effective and widely used approach for weakly supervised machine learning. In histopathology, MIL models have achieved remarkable success in tasks like tumor detection, biomarker prediction, and outcome prognostication. However, MIL explanation methods are still lagging behind, as they are limited to small bag sizes or disregard instance interactions. We revisit MIL through the lens of explainable AI (XAI) and introduce xMIL, a refined framework with more general assumptions. We demonstrate how to obtain improved MIL explanations using layer-wise relevance propagation (LRP) and conduct extensive evaluation experiments on three toy settings and four real-world histopathology datasets. Our approach consistently outperforms previous explanation attempts with particularly improved faithfulness scores on challenging biomarker prediction tasks. Finally, we showcase how xMIL explanations enable pathologists to extract insights from MIL models, representing a significant advance for knowledge discovery and model debugging in digital histopathology.
Leveraging Tumor Heterogeneity: Heterogeneous Graph Representation Learning for Cancer Survival Prediction in Whole Slide Images
Authors: Junxian Wu; Xinyi Ke; Xiaoming Jiang; Huanwen Wu; Youyong Kong; Lizhi Shao
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Survival prediction is a significant challenge in cancer management. Tumor micro-environment is a highly sophisticated ecosystem consisting of cancer cells, immune cells, endothelial cells, fibroblasts, nerves and extracellular matrix. The intratumor heterogeneity and the interaction across multiple tissue types profoundly impacts the prognosis. However, current methods often neglect the fact that the contribution to prognosis differs with tissue types. In this paper, we propose ProtoSurv, a novel heterogeneous graph model for WSI survival prediction. The learning process of ProtoSurv is not only driven by data but also incorporates pathological domain knowledge, including the awareness of tissue heterogeneity, the emphasis on prior knowledge of prognostic-related tissues, and the depiction of spatial interaction across multiple tissues. We validate ProtoSurv across five different cancer types from TCGA (i.e., BRCA, LGG, LUAD, COAD and PAAD), and demonstrate the superiority of our method over the state-of-the-art methods.
Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning
Authors: Dan Braun; Jordan Taylor; Nicholas Goldowsky-Dill; Lee Sharkey
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Identifying the features learned by neural networks is a core challenge in mechanistic interpretability. Sparse autoencoders (SAEs), which learn a sparse, overcomplete dictionary that reconstructs a network's internal activations, have been used to identify these features. However, SAEs may learn more about the structure of the datatset than the computational structure of the network. There is therefore only indirect reason to believe that the directions found in these dictionaries are functionally important to the network. We propose end-to-end (e2e) sparse dictionary learning, a method for training SAEs that ensures the features learned are functionally important by minimizing the KL divergence between the output distributions of the original model and the model with SAE activations inserted. Compared to standard SAEs, e2e SAEs offer a Pareto improvement: They explain more network performance, require fewer total features, and require fewer simultaneously active features per datapoint, all with no cost to interpretability. We explore geometric and qualitative differences between e2e SAE features and standard SAE features. E2e dictionary learning brings us closer to methods that can explain network behavior concisely and accurately. We release our library for training e2e SAEs and reproducing our analysis at
https://github.com/ApolloResearch/e2e_sae.
Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars
Authors: Zhaoxuan Wu; Xiaoqiang Lin; Zhongxiang Dai; Wenyang Hu; Yao Shu; See-Kiong Ng; Patrick Jaillet; Bryan Kian Hsiang Low
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Large language models (LLMs) have shown impressive capabilities in real-world applications. The capability of *in-context learning* (ICL) allows us to adapt an LLM to downstream tasks by including input-label exemplars in the prompt without model fine-tuning. However, the quality of these exemplars in the prompt greatly impacts performance, highlighting the need for an effective automated exemplar selection method. Recent studies have explored retrieval-based approaches to select exemplars tailored to individual test queries, which can be undesirable due to extra test-time computation and an increased risk of data exposure. Moreover, existing methods fail to adequately account for the impact of exemplar ordering on the performance. On the other hand, the impact of the *instruction*, another essential component in the prompt given to the LLM, is often overlooked in existing exemplar selection methods. To address these challenges, we propose a novel method named $\texttt{EASE}$, which leverages the hidden embedding from a pre-trained language model to represent ordered sets of exemplars and uses a neural bandit algorithm to optimize the sets of exemplars *while accounting for exemplar ordering*. Our  $\texttt{EASE}$ can efficiently find an ordered set of exemplars that *performs well for all test queries* from a given task, thereby eliminating test-time computation. Importantly,  $\texttt{EASE}$ can be readily extended to *jointly optimize both the exemplars and the instruction*. Through extensive empirical evaluations (including novel tasks), we demonstrate the superiority of  $\texttt{EASE}$ over existing methods, and reveal practical insights about the impact of exemplar selection on ICL, which may be of independent interest. Our code is available at https://github.com/ZhaoxuanWu/EASE-Prompt-Optimization.
Do causal predictors generalize better to new domains?
Authors: Vivian Yvonne Nastl; Moritz Hardt
Venue: NeurIPS 2024 (NeurIPS 2024 spotlight)

Abstract: We study how well machine learning models trained on causal features generalize across domains. We consider 16 prediction tasks on tabular datasets covering applications in health, employment, education, social benefits, and politics. Each dataset comes with multiple domains, allowing us to test how well a model trained in one domain performs in another. For each prediction task, we select features that have a causal influence on the target of prediction. Our goal is to test the hypothesis that models trained on causal features generalize better across domains. Without exception, we find that predictors using all available features, regardless of causality, have better in-domain and out-of-domain accuracy than predictors using causal features. Moreover, even the absolute drop in accuracy from one domain to the other is no better for causal predictors than for models that use all features.  In addition, we show that recent causal machine learning methods for domain generalization do not perform better in our evaluation than standard predictors trained on the set of causal features. Likewise, causal discovery algorithms either fail to run or select causal variables that perform no better than our selection. Extensive robustness checks confirm that our findings are stable under variable misclassification.
ChronoEpilogi: Scalable Time Series Selection with Multiple Solutions
Authors: Etienne Vareille; Michele Linardi; Ioannis Tsamardinos; Vassilis Christophides
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: We consider the problem of selecting all the minimal-size subsets of multivariate time-series (TS) variables whose past leads to an optimal predictive model for the future (forecasting) of a given target variable (multiple feature selection problem for times-series). Identifying these subsets leads to gaining insights, domain intuition,and a better understanding of the data-generating mechanism; it is often the first step in causal modeling. While identifying a single solution to the feature selection problem suffices for forecasting purposes, identifying all such minimal-size, optimally predictive subsets is necessary for knowledge discovery and important to avoid misleading a practitioner. We develop the theory of multiple feature selection for time-series data, propose the ChronoEpilogi algorithm, and prove its soundness and completeness under two mild, broad, non-parametric distributional assumptions, namely Compositionality of the distribution and Interchangeability of time-series variables in solutions. Experiments on synthetic and real datasets demonstrate the scalability of ChronoEpilogi to hundreds of TS variables and its efficacy in identifying multiple solutions. In the real datasets, ChronoEpilogi is shown to reduce the number of TS variables by 96% (on average) by conserving or even improving forecasting performance. Furthermore, it is on par with GroupLasso performance, with the added benefit of providing multiple solutions.
Learning Distinguishable Trajectory Representation with Contrastive Loss
Authors: Tianxu Li; Kun Zhu; Juan Li; Yang Zhang
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Policy network parameter sharing is a commonly used technique in advanced deep multi-agent reinforcement learning (MARL) algorithms to improve learning efficiency by reducing the number of policy parameters and sharing experiences among agents. Nevertheless, agents that share the policy parameters tend to learn similar behaviors. To encourage multi-agent diversity, prior works typically maximize the mutual information between trajectories and agent identities using variational inference. However, this category of methods easily leads to inefficient exploration due to limited trajectory visitations. To resolve this limitation, inspired by the learning of pre-trained models, in this paper, we propose a novel Contrastive Trajectory Representation (CTR) method based on learning distinguishable trajectory representations to encourage multi-agent diversity. Specifically, CTR maps the trajectory of an agent into a latent trajectory representation space by an encoder and an autoregressive model. To achieve the distinguishability among trajectory representations of different agents, we introduce contrastive learning to maximize the mutual information between the trajectory representations and learnable identity representations of different agents. We implement CTR on top of QMIX and evaluate its performance in various cooperative multi-agent tasks. The empirical results demonstrate that our proposed CTR yields significant performance improvement over the state-of-the-art methods.
On Mesa-Optimization in Autoregressively Trained Transformers: Emergence and Capability
Authors: Chenyu Zheng; Wei Huang; Rongzhen Wang; Guoqiang Wu; Jun Zhu; Chongxuan Li
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Autoregressively trained transformers have brought a profound revolution to the world, especially with their in-context learning (ICL) ability to address downstream tasks. 
Recently, several studies suggest that transformers learn a mesa-optimizer during autoregressive (AR) pretraining to implement ICL. Namely, the forward pass of the trained transformer is equivalent to optimizing an inner objective function in-context.
However, whether the practical non-convex training dynamics will converge to the ideal mesa-optimizer is still unclear.
Towards filling this gap, we investigate the non-convex dynamics of a one-layer linear causal self-attention model autoregressively trained by gradient flow, where the sequences are generated by an AR process $x_{t+1} = W x_t$. First, under a certain condition of data distribution, we prove that an autoregressively trained transformer learns $W$ by implementing one step of gradient descent to minimize an ordinary least squares (OLS) problem in-context. It then applies the learned $\widehat{W}$ for next-token prediction, thereby verifying the mesa-optimization hypothesis. Next, under the same data conditions, we explore the capability limitations of the obtained mesa-optimizer. We show that a stronger assumption related to the moments of data is the sufficient and necessary condition that the learned mesa-optimizer recovers the distribution. Besides, we conduct exploratory analyses beyond the first data condition 
and prove that generally, the trained transformer will not perform vanilla gradient descent for the OLS problem. Finally, our simulation results verify the theoretical results.
General Detection-based Text Line Recognition
Authors: Raphael Baena; Syrine Kalleli; Mathieu Aubry
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: We introduce a general detection-based approach to text line recognition, be it printed (OCR) or handwritten text (HTR), with latin, chinese or ciphered characters. Detection-based approaches have until now largely been discarded for HTR because reading characters separately is often challenging, and character-level annotation is difficult and expensive. We overcome these challenges thanks to three main insights: (i) synthetic pre-training with diverse enough data to learn reasonable character localization in any script; (ii) modern transformer-based detectors can jointly detect a large number of instances and, if trained with an adequate masking strategy, leverage consistency between the different detections; (iii) once a pre-trained detection model with approximate character localization is available, it is possible to fine-tune it with line-level annotation on real data, even with a different alphabet.  Our approach thus builds on a completely different paradigm than most state-of-the-art methods, which rely on autoregressive decoding, predicting character values one by one, while we treat a complete line in parallel. Remarkably, our method demonstrates good performance on range of scripts, usually tackled with specialized approaches: latin script, chinese script, and ciphers, for which we significantly improve state-of-the-art performances. 
Our code and models are available at [https://github.com/raphael-baena/DTLR](https://github.com/raphael-baena/DTLR).
EEGPT: Pretrained Transformer for Universal and Reliable Representation of EEG Signals
Authors: Guangyu Wang; Wenchao Liu; Yuhong He; Cong Xu; Lin Ma; Haifeng Li
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Electroencephalography (EEG) is crucial for recording brain activity, 
  with applications in medicine, neuroscience, and brain-computer interfaces (BCI). 
  However, challenges such as low signal-to-noise ratio (SNR), high inter-subject variability, and channel mismatch complicate the extraction of robust, 
  universal EEG representations. 
  We propose EEGPT, a novel 10-million-parameter pretrained transformer model designed for universal EEG feature extraction. 
  In EEGPT, a mask-based dual self-supervised learning method for efficient feature extraction is designed. 
  Compared to other mask-based self-supervised learning methods, 
  EEGPT introduces spatio-temporal representation alignment. 
  This involves constructing a self-supervised task based on 
  EEG representations that possess high SNR and rich semantic information, 
  rather than on raw signals. 
  Consequently, this approach mitigates the issue of poor feature quality typically 
  extracted from low SNR signals.
  Additionally, EEGPT's hierarchical structure processes spatial and temporal information separately, 
  reducing computational complexity while increasing flexibility and adaptability for BCI applications. 
  By training on a large mixed multi-task EEG dataset, we fully exploit EEGPT's capabilities.
  The experiment validates the efficacy and scalability of EEGPT, 
  achieving state-of-the-art performance on a range of downstream tasks with linear-probing.
  Our research advances EEG representation learning, offering innovative solutions for bio-signal processing and AI applications.
  The code for this paper is available at: https://github.com/BINE022/EEGPT
Contextual Active Model Selection
Authors: Xuefeng Liu; Fangfang Xia; Rick L. Stevens; Yuxin Chen
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: While training models and labeling data are resource-intensive, a wealth of pre-trained models and unlabeled data exists. To effectively utilize these resources, we present an approach to actively select pre-trained models while minimizing labeling costs. We frame this as an online contextual active model selection problem: At each round, the learner receives an unlabeled data point as a context. The objective is to adaptively select the best model to make a prediction while limiting label requests. To tackle this problem, we propose CAMS, a contextual active model selection algorithm that relies on two novel components: (1) a contextual model selection mechanism, which leverages context information to make informed decisions about which model is likely to perform best for a given context, and (2)
an active query component, which strategically chooses when to request labels for data points, minimizing the overall labeling cost. We provide rigorous theoretical analysis for the regret and query complexity under both adversarial and stochastic settings. Furthermore, we demonstrate the effectiveness of our algorithm on a diverse collection of benchmark classification tasks. Notably, CAMS requires substantially less labeling effort (less than 10%) compared to existing methods on CIFAR10 and DRIFT benchmarks, while achieving similar or better accuracy.
If You Want to Be Robust, Be Wary of Initialization
Authors: Sofiane ENNADIR; Johannes F. Lutzeyer; Michalis Vazirgiannis; El houcine Bergou
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Graph Neural Networks (GNNs) have demonstrated remarkable performance across a spectrum of graph-related tasks, however concerns persist regarding their vulnerability to adversarial perturbations. While prevailing defense strategies focus primarily on pre-processing techniques and adaptive message-passing schemes, this study delves into an under-explored dimension: the impact of weight initialization and associated hyper-parameters, such as training epochs, on a model’s robustness.
We introduce a theoretical framework bridging the connection between initialization strategies and a network's resilience to adversarial perturbations. Our analysis reveals a direct relationship between initial weights, number of training epochs and the model’s vulnerability, offering new insights into adversarial robustness beyond conventional defense mechanisms. While our primary focus is on GNNs, we extend our theoretical framework, providing a general upper-bound applicable to Deep Neural Networks.
Extensive experiments, spanning diverse models and real-world datasets subjected to various adversarial attacks, validate our findings. We illustrate that selecting appropriate initialization not only ensures performance on clean datasets but also enhances model robustness against adversarial perturbations, with observed gaps of up to 50\% compared to alternative initialization approaches.
A theoretical design of concept sets: improving the predictability of concept bottleneck models
Authors: Max Ruiz Luyten; Mihaela van der Schaar
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Concept-based learning, a promising approach in machine learning, emphasizes the value of high-level representations called concepts. However, despite growing interest in concept-bottleneck models (CBMs), there is a lack of clear understanding regarding the properties of concept sets and their impact on model performance. In this work, we define concepts within the machine learning context, highlighting their core properties: 'expressiveness' and 'model-aware inductive bias', and we make explicit the underlying assumption of CBMs. We establish theoretical results for concept-bottleneck models (CBMs), revealing how these properties guide the design of concept sets that optimize model performance. Specifically, we demonstrate that well-chosen concept sets can improve sample efficiency and out-of-distribution robustness in the appropriate regimes. Based on these insights, we propose a method to effectively identify informative and non-redundant concepts. We validate our approach with experiments on CIFAR-10 and MetaShift, showing that concept-bottleneck models outperform the foundational embedding counterpart, particularly in low-data regimes and under distribution shifts. We also examine failure modes and discuss how they can be tackled.
Reinforcement Learning with Adaptive Regularization for Safe Control of Critical Systems
Authors: Haozhe Tian; Homayoun Hamedmoghadam; Robert Noel Shorten; Pietro Ferraro
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Reinforcement Learning (RL) is a powerful method for controlling dynamic systems, but its learning mechanism can lead to unpredictable actions that undermine the safety of critical systems. Here, we propose RL with Adaptive Regularization (RL-AR), an algorithm that enables safe RL exploration by combining the RL policy with a policy regularizer that hard-codes the safety constraints. RL-AR performs policy combination via a "focus module," which determines the appropriate combination depending on the state—relying more on the safe policy regularizer for less-exploited states while allowing unbiased convergence for well-exploited states. In a series of critical control applications, we demonstrate that RL-AR not only ensures safety during training but also achieves a return competitive with the standards of model-free RL that disregards safety.
Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists
Authors: Joachim Baumann; Celestine Mendler-Dünner
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: We investigate algorithmic collective action in transformer-based recommender systems. Our use case is a collective of fans aiming to promote the visibility of an underrepresented artist by strategically placing one of their songs in the existing playlists they control. We introduce two easily implementable strategies to select the position at which to insert the song and boost recommendations at test time. The strategies exploit statistical properties of the learner to leverage discontinuities in the recommendations, and the long-tail nature of song distributions. We evaluate the efficacy of our strategies using a publicly available recommender system model released by a major music streaming platform. Our findings reveal that even small collectives (controlling less than 0.01\% of the training data) can achieve up to $40\times$ more test time recommendations than songs with similar training set occurrences, on average. Focusing on the externalities of the strategy, we find that the recommendations of other songs are largely preserved, and the newly gained recommendations are distributed across various artists. Together, our findings demonstrate how carefully designed collective action strategies can be effective while not necessarily being adversarial.