GenAI4ELab/papercli-papers huggingface dataset:
A Reinforcement Learning Framework for Dynamic Mediation Analysis
Authors: Lin Ge; Jitao Wang; Chengchun Shi; Zhenke Wu; Rui Song
Venue: ICML 2023 (ICML 2023 Poster)

Abstract: Mediation analysis learns the causal effect transmitted via mediator variables between treatments and outcomes, and receives increasing attention in various scientific domains to elucidate causal relations. Most existing works focus on point-exposure studies where each subject only receives one treatment at a single time point. However, there are a number of applications (e.g., mobile health) where the treatments are sequentially assigned over time and the dynamic mediation effects are of primary interest. Proposing a reinforcement learning (RL) framework, we are the first to evaluate dynamic mediation effects in settings with infinite horizons. We decompose the average treatment effect into an immediate direct effect, an immediate mediation effect, a delayed direct effect, and a delayed mediation effect. Upon the identification of each effect component, we further develop robust and semi-parametrically efficient estimators under the RL framework to infer these causal effects. The superior performance of the proposed method is demonstrated through extensive numerical studies, theoretical results, and an analysis of a mobile health dataset. A Python implementation of the proposed procedure is available at https://github.com/linlinlin97/MediationRL.
On the Functional Similarity of Robust and Non-Robust Neural Representations
Authors: András Balogh; Márk Jelasity
Venue: ICML 2023 (ICML 2023 Poster)

Abstract: Model stitching---where the internal representations of two neural networks are aligned linearly---helped demonstrate that the representations of different neural networks for the same task are surprisingly similar in a functional sense. At the same time, the representations of adversarially robust networks are considered to be different from non-robust representations. For example, robust image classifiers are invertible, while non-robust networks are not. Here, we investigate the functional similarity of robust and non-robust representations for image classification with the help of model stitching. We find that robust and non-robust networks indeed have different representations. However, these representations are compatible regarding accuracy. From the point of view of robust accuracy, compatibility decreases quickly after the first few layers but the representations become compatible again in the last layers, in the sense that the properties of the front model can be recovered. Moreover, this is true even in the case of cross-task stitching. Our results suggest that stitching in the initial, preprocessing layers and the final, abstract layers test different kinds of compatibilities. In particular, the final layers are easy to match, because their representations depend mostly on the same abstract task specification, in our case, the classification of the input into $n$ classes.
Compressed Decentralized Proximal Stochastic Gradient Method for Nonconvex Composite Problems with Heterogeneous Data
Authors: Yonggui Yan; Jie Chen; Pin-Yu Chen; Xiaodong Cui; Songtao Lu; Yangyang Xu
Venue: ICML 2023 (ICML 2023 Poster)

Abstract: We first propose a decentralized proximal stochastic gradient tracking method (DProxSGT) for nonconvex stochastic composite problems, with data heterogeneously distributed on multiple workers in a decentralized connected network. To save communication cost, we then extend DProxSGT to a compressed method by compressing the communicated information. Both methods need only $\mathcal{O}(1)$ samples per worker for each proximal update, which is important to achieve good generalization performance on training deep neural networks. With a smoothness condition on the expected loss function (but not on each sample function), the proposed methods can achieve an optimal sample complexity result to produce a near-stationary point. Numerical experiments on training neural networks demonstrate the significantly better generalization performance of our methods over large-batch training methods and momentum variance-reduction methods and also, the ability of handling heterogeneous data by the gradient tracking scheme.
Multi-Agent Best Arm Identification with Private Communications
Authors: Alexandre Rio; Merwan Barlier; Igor Colin; Marta Soare
Venue: ICML 2023 (ICML 2023 Poster)

Abstract: We address multi-agent best arm identification with privacy guarantees. In this setting, agents collaborate by communicating to find the optimal arm. To avoid leaking sensitive data through messages, we consider two notions of privacy withholding different kinds of information: differential privacy and $(\epsilon, \eta)$-privacy. For each privacy definition, we propose an algorithm based on a two-level successive elimination scheme. We provide theoretical guarantees for the privacy level, accuracy and sample complexity of our algorithms. Experiments on various settings support our theoretical findings.
Evidential Interactive Learning for Medical Image Captioning
Authors: Ervine Zheng; Qi Yu
Venue: ICML 2023 (ICML 2023 Poster)

Abstract: Medical image captioning alleviates the burden of physicians and possibly reduces medical errors by automatically generating text descriptions to describe image contents and convey findings. It is more challenging than conventional image captioning due to the complexity of medical images and the difficulty of aligning image regions with medical terms. In this paper, we propose an evidential interactive learning framework that leverages evidence-based uncertainty estimation and interactive machine learning to improve image captioning with limited labeled data. The interactive learning process involves three stages: keyword prediction, caption generation, and model retraining. First, the model predicts a list of keywords with evidence-based uncertainty and selects the most informative keywords to seek user feedback. Second, user-approved keywords are used as model input to guide the model to generate satisfactory captions. Third, the model is updated based on user-approved keywords and captions, where evidence-based uncertainty is used to allocate different weights to different data instances. Experiments on two medical image datasets illustrate that the proposed framework can effectively learn from human feedback and improve the model's performance in the future.
Generalization Bounds using Data-Dependent Fractal Dimensions
Authors: Benjamin Dupuis; George Deligiannidis; Umut Simsekli
Venue: ICML 2023 (ICML 2023 Poster)

Abstract: Providing generalization guarantees for modern neural networks has been a crucial task in statistical learning. Recently, several studies have attempted to analyze the generalization error in such settings by using tools from fractal geometry. While these works have successfully introduced new mathematical tools to apprehend generalization, they heavily rely on a Lipschitz continuity assumption, which in general does not hold for neural networks and might make the bounds vacuous. In this work, we address this issue and prove fractal geometry-based generalization bounds without requiring any Lipschitz assumption. To achieve this goal, we build up on a classical covering argument in learning theory and introduce a data-dependent fractal dimension. Despite introducing a significant amount of technical complications, this new notion lets us control the generalization error (over either fixed or random hypothesis spaces) along with certain mutual information (MI) terms. To provide a clearer interpretation to the newly introduced MI terms, as a next step, we introduce a notion of `geometric stability' and link our bounds to the prior art. Finally, we make a rigorous connection between the proposed data-dependent dimension and topological data analysis tools, which then enables us to compute the dimension in a numerically efficient way. We support our theory with experiments conducted on various settings.
Quantitative Universal Approximation Bounds for Deep Belief Networks
Authors: Julian Sieber; Johann Gehringer
Venue: ICML 2023 (ICML 2023 Poster)

Abstract: We show that deep belief networks with binary hidden units can approximate any multivariate probability density under very mild integrability requirements on the parental density of the visible nodes. The approximation is measured in the $L^q$-norm for $q\in[1,\infty]$ ($q=\infty$ corresponding to the supremum norm) and in Kullback-Leibler divergence. Furthermore, we establish sharp quantitative bounds on the approximation error in terms of the number of hidden units.
Learn to Accumulate Evidence from All Training Samples: Theory and Practice
Authors: Deep Shankar Pandey; Qi Yu
Venue: ICML 2023 (ICML 2023 Poster)

Abstract: Evidential deep learning, built upon belief theory and subjective logic, offers a principled and computationally efficient way to turn a deterministic neural network uncertainty-aware. The resultant evidential models can quantify fine-grained uncertainty using the learned evidence. To ensure theoretically sound evidential models, the evidence needs to be non-negative, which requires special activation functions for model training and inference. This constraint often leads to inferior predictive performance compared to standard softmax models, making it challenging to extend them to many large-scale datasets. To unveil the real cause of this undesired behavior, we theoretically investigate evidential models and identify a fundamental limitation that explains the inferior performance: existing evidential activation functions create *zero evidence regions*, which prevent the model to learn from training samples falling into such regions. A deeper analysis of evidential activation functions based on our theoretical underpinning inspires the design of a novel regularizer that effectively alleviates this fundamental limitation. Extensive experiments over many challenging real-world datasets and settings confirm our theoretical findings and demonstrate the effectiveness of our proposed approach.
Multi-agent Online Scheduling: MMS Allocations for Indivisible Items
Authors: Shengwei Zhou; Rufan Bai; Xiaowei Wu
Venue: ICML 2023 (ICML 2023 Poster)

Abstract: We consider the problem of fairly allocating a sequence of indivisible items that arrive online in an arbitrary order to a group of $n$ agents with additive normalized valuation functions, we consider the allocation of goods and chores separately and propose algorithms for approximating maximin share (MMS) allocations for both settings. When agents have identical valuation functions the problem coincides with the semi-online machine covering problem (when items are goods) and load balancing problem (when items are chores), for both of which optimal competitive ratios have been achieved. In this paper we consider the case when agents have general additive valuation functions. For the allocation of goods we show that no competitive algorithm exists even when there are only three agents and propose an optimal $0.5$-competitive algorithm for the case of two agents. For the allocation of chores we propose a $(2-1/n)$-competitive algorithm for $n\geq 3$ agents and a $\sqrt{2}\approx 1.414$-competitive algorithm for two agents. Additionally, we show that no algorithm can do better than $15/11\approx 1.364$-competitive for two agents.
High Fidelity Image Counterfactuals with Probabilistic Causal Models
Authors: Fabio De Sousa Ribeiro; Tian Xia; Miguel Monteiro; Nick Pawlowski; Ben Glocker
Venue: ICML 2023 (ICML 2023 Poster)

Abstract: We present a general causal generative modelling framework for accurate estimation of high fidelity image counterfactuals with deep structural causal models. Estimation of interventional and counterfactual queries for high-dimensional structured variables, such as images, remains a challenging task. We leverage ideas from causal mediation analysis and advances in generative modelling to design new deep causal mechanisms for structured variables in causal models. Our experiments demonstrate that our proposed mechanisms are capable of accurate abduction and estimation of direct, indirect and total effects as measured by axiomatic soundness of counterfactuals.
Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy
Authors: Blake Woodworth; Konstantin Mishchenko; Francis Bach
Venue: ICML 2023 (ICML 2023 Poster)

Abstract: We present an algorithm for minimizing an objective with hard-to-compute gradients by using a related, easier-to-access function as a proxy. Our algorithm is based on approximate proximal-point iterations on the proxy combined with relatively few stochastic gradients from the objective. When the difference between the objective and the proxy is $\delta$-smooth, our algorithm guarantees convergence at a rate matching stochastic gradient descent on a $\delta$-smooth objective, which can lead to substantially better sample efficiency. Our algorithm has many potential applications in machine learning, and provides a principled means of leveraging synthetic data, physics simulators, mixed public and private data, and more.
Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning
Authors: Nader Asadi; MohammadReza Davari; Sudhir Mudur; Rahaf Aljundi; Eugene Belilovsky
Venue: ICML 2023 (ICML 2023 Poster)

Abstract: In Continual learning (CL) balancing effective adaptation while combating catastrophic forgetting is a central challenge. Many of the recent best-performing methods utilize various forms of prior task data, e.g. a replay buffer, to tackle the catastrophic forgetting problem. Having access to previous task data can be restrictive in many real-world scenarios, for example when task data is sensitive or proprietary. To overcome the necessity of using previous tasks' data, in this work, we start with strong representation learning methods that have been shown to be less prone to forgetting. We propose a holistic approach to jointly learn the representation and class prototypes while maintaining the relevance of old class prototypes and their embedded similarities. Specifically, samples are mapped to an embedding space where the representations are learned using a supervised contrastive loss. Class prototypes are evolved continually in the same latent space, enabling learning and prediction at any point. To continually adapt the prototypes without keeping any prior task data, we propose a novel distillation loss that constrains class prototypes to maintain relative similarities as compared to new task data. This method yields state-of-the-art performance in the task-incremental setting, outperforming methods relying on large amounts of data, and provides strong performance in the class-incremental setting without using any stored data points.
Sequential Changepoint Detection via Backward Confidence Sequences
Authors: Shubhanshu Shekhar; Aaditya Ramdas
Venue: ICML 2023 (ICML 2023 Poster)

Abstract: We present a simple reduction from sequential estimation to sequential changepoint detection (SCD). In short, suppose we are interested in detecting changepoints in some parameter or functional $\theta$ of the underlying distribution. We demonstrate that if we can construct a confidence sequence (CS) for $\theta$, then we can also successfully perform SCD for $\theta$. This is accomplished by checking if two CSs --- one forwards and the other backwards --- ever fail to intersect. Since the literature on CSs has been rapidly evolving recently, the reduction provided in this paper immediately solves several old and new change detection problems. Further, our ``backward CS'', constructed by reversing time, is new and potentially of independent interest. We provide strong nonasymptotic guarantees on the frequency of false alarms and detection delay, and demonstrate numerical effectiveness on several problems.
On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology
Authors: Francesco Di Giovanni; Lorenzo Giusti; Federico Barbero; Giulia Luise; Pietro Lio; Michael M. Bronstein
Venue: ICML 2023 (ICML 2023 Poster)

Abstract: Message Passing Neural Networks (MPNNs) are instances of Graph Neural Networks that leverage the graph to send messages over the edges. This inductive bias leads to a phenomenon known as over-squashing, where a node feature is insensitive to information contained at distant nodes. Despite recent methods introduced to mitigate this issue, an understanding of the causes for over-squashing and of possible solutions are lacking. In this theoretical work, we prove that: (i) Neural network width can mitigate over-squashing, but at the cost of making the whole network more sensitive; (ii) Conversely, depth cannot help mitigate over-squashing: increasing the number of layers leads to over-squashing being dominated by vanishing gradients; (iii) The graph topology plays the greatest role, since over-squashing occurs between nodes at high commute time. Our analysis provides a unified framework to study different recent methods introduced to cope with over-squashing and serves as a justification for a class of methods that fall under graph rewiring.
Causal Bounds in Quasi-Markovian Graphs
Authors: Madhumitha Shridharan; Garud Iyengar
Venue: ICML 2023 (ICML 2023 Poster)

Abstract: We consider the problem of computing bounds for causal queries on quasi-Markovian graphs with unobserved confounders and discrete valued observed variables, where identifiability does not hold. Existing non-parametric approaches for computing such bounds use multilinear programming (MP) formulations that are often intractable for existing solvers when the degree of the polynomial objective is greater than two. Hence, one often has to resort to either fast approximate heuristics which are not guaranteed to contain the true query value, or more accurate but computationally intensive procedures. We show how to construct an equivalent MP with a polynomial objective of lower degree. In particular, the degree of the objective in the new MP is equal to only the number of C-components that are intervened upon, instead of the total number of C-components. As a result, we can compute exact bounds for significantly larger causal inference problems as compared to what is possible using existing techniques. We also propose a very efficient Frank-Wolfe heuristic that produces very high quality bounds, and scales to large multilinear problems of higher degree.
Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data
Authors: Hien Dang; Tho Tran Huu; Stanley Osher; Hung Tran-The; Nhat Ho; Tan Minh Nguyen
Venue: ICML 2023 (ICML 2023 Poster)

Abstract: Modern deep neural networks have achieved impressive performance on tasks from image classification to natural language processing. Surprisingly, these complex systems with massive amounts of parameters exhibit the same structural properties in their last-layer features and classifiers across canonical datasets when training until convergence. In particular, it has been observed that the last-layer features collapse to their class-means, and those class-means are the vertices of a simplex Equiangular Tight Frame (ETF). This phenomenon is known as Neural Collapse (NC). Recent papers have theoretically shown that NC emerges in the global minimizers of training problems with the simplified ``unconstrained feature model''. In this context, we take a step further and prove the NC occurrences in deep linear networks for the popular mean squared error (MSE) and cross entropy (CE) losses, showing that global solutions exhibit NC properties across the linear layers. Furthermore, we extend our study to imbalanced data for MSE loss and present the first geometric analysis of NC under bias-free setting. Our results demonstrate the convergence of the last-layer features and classifiers to a geometry consisting of orthogonal vectors, whose lengths depend on the amount of data in their corresponding classes. Finally, we empirically validate our theoretical analyses on synthetic and practical network architectures with both balanced and imbalanced scenarios.
Continual Task Allocation in Meta-Policy Network via Sparse Prompting
Authors: Yijun Yang; Tianyi Zhou; Jing Jiang; Guodong Long; Yuhui Shi
Venue: ICML 2023 (ICML 2023 Poster)

Abstract: How to train a generalizable meta-policy by continually learning a sequence of tasks? It is a natural human skill yet challenging to achieve by current reinforcement learning: the agent is expected to quickly adapt to new tasks (plasticity) meanwhile retaining the common knowledge from previous tasks (stability). We address it by "Continual Task Allocation via Sparse Prompting (CoTASP)", which learns over-complete dictionaries to produce sparse masks as prompts extracting a sub-network for each task from a meta-policy network. CoTASP trains a policy for each task by optimizing the prompts and the sub-network weights alternatively. The dictionary is then updated to align the optimized prompts with tasks' embedding, thereby capturing tasks' semantic correlations. Hence, relevant tasks share more neurons in the meta-policy network due to similar prompts while cross-task interference causing forgetting is effectively restrained. Given a meta-policy and dictionaries trained on previous tasks, new task adaptation reduces to highly efficient sparse prompting and sub-network finetuning. In experiments, CoTASP achieves a promising plasticity-stability trade-off without storing or replaying any past tasks' experiences. It outperforms existing continual and multi-task RL methods on all seen tasks, forgetting reduction, and generalization to unseen tasks.
Algorithmic Stability of Heavy-Tailed SGD with General Loss Functions
Authors: Anant Raj; Lingjiong Zhu; Mert Gurbuzbalaban; Umut Simsekli
Venue: ICML 2023 (ICML 2023 Poster)

Abstract: Heavy-tail phenomena in stochastic gradient descent (SGD) have been reported in several empirical studies. Experimental evidence in previous works suggests a strong interplay between the heaviness of the tails and generalization behavior of SGD. To address this empirical phenomena theoretically, several works have made strong topological and statistical assumptions to link the generalization error to heavy tails. Very recently, new generalization bounds have been proven, indicating a non-monotonic relationship between the generalization error and heavy tails, which is more pertinent to the reported empirical observations. While these bounds do not require additional topological assumptions given that SGD can be modeled using a heavy-tailed stochastic differential equation (SDE), they can only apply to simple quadratic problems. In this paper, we build on this line of research and develop generalization bounds for a more general class of objective functions, which includes non-convex functions as well. Our approach is based on developing Wasserstein stability bounds for heavy-tailed SDEs and their discretizations, which we then convert to generalization bounds. Our results do not require any nontrivial assumptions; yet, they shed more light to the empirical observations, thanks to the generality of the loss functions.