GenAI4ELab/papercli-papers huggingface dataset:
MF-CLR: Multi-Frequency Contrastive Learning Representation for Time Series
Authors: Jufang Duan; Wei Zheng; Yangzhou Du; Wenfa Wu; Haipeng Jiang; Hongsheng Qi
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Learning a decent representation from unlabeled time series is a challenging task, especially when the time series data is derived from diverse channels at different sampling rates. Our motivation stems from the financial domain, where sparsely labeled covariates are commonly collected at different frequencies, *e.g.*, daily stock market index, monthly unemployment rate and quarterly net revenue of a certain listed corporation. This paper presents **M**ulti-**F**requency **C**ontrastive **L**earning **R**epresentation (MF-CLR), aimed at learning a good representation of multi-frequency time series in a self-supervised paradigm by leveraging the ability of contrastive learning. MF-CLR introduces a hierarchical mechanism that spans across different frequencies along the feature dimension. Within each contrastive block, two groups of subseries with adjacent frequencies are embedded based on our proposed cross-frequency consistency. To validate the effectiveness of MF-CLR, we conduct extensive experiments on five downstream tasks, including long-term and short-term forecasting, classification, anomaly detection and imputation. Experimental evidence shows that MF-CLR delivers a leading performance in all the downstream tasks and keeps consistent performance across different target dataset scales in the transfer learning scenario.
Unlock the Cognitive Generalization of Deep Reinforcement Learning via Granular Ball Representation
Authors: Jiashun Liu; Jianye HAO; Yi Ma; Shuyin Xia
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: The policies learned by humans in simple scenarios can be deployed in complex scenarios with the same task logic through limited feature alignment training, a process referred to as cognitive generalization or systematic generalization. Thus, a plausible conjecture is that unlocking cognitive generalization in DRL could enable effective generalization of policies from simple to complex scenarios through reward-agnostic fine-tuning. This would eliminate the need for designing reward functions in complex scenarios, thus reducing environment-building costs. In this paper, we propose a general framework to enhance the cognitive generalization ability of standard DRL methods. Our framework builds a cognitive latent space in a simple scenario, then segments the latent space to cluster samples with similar environmental influences into same subregion. During the fine-tuning in the complex scenario, the policy uses cognitive latent space to align the new sample with the same subregion sample collected from the simple scenario and approximates the rewards and Q values of the new samples for policy update. Based on this framework, we propose *Granular Ball Reinforcement Leaning* (GBRL), a practical algorithm via Variational Autoencoder (VAE) and Granular Ball Representation. GBRL achieves effective policy generalization on various difficult scenarios with the same task logic.
How do Transformers Perform In-Context Autoregressive Learning ?
Authors: Michael Eli Sander; Raja Giryes; Taiji Suzuki; Mathieu Blondel; Gabriel Peyré
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Transformers have achieved state-of-the-art performance in language modeling tasks. However, the reasons behind their tremendous success are still unclear. In this paper, towards a better understanding, we train a Transformer model on a simple next token prediction task, where sequences are generated as a first-order autoregressive process $s_{t+1} = W s_t$. We show how a trained Transformer predicts the next token by first learning $W$ in-context, then applying a prediction mapping. We call the resulting procedure *in-context autoregressive learning*. More precisely, focusing on commuting orthogonal matrices $W$, we first show that a trained one-layer linear Transformer implements one step of gradient descent for the minimization of an inner objective function, when considering augmented tokens. When the tokens are not augmented, we characterize the global minima of a one-layer diagonal linear multi-head Transformer. Importantly, we exhibit orthogonality between heads and show that positional encoding captures trigonometric relations in the data. On the experimental side, we consider the general case of non-commuting orthogonal matrices and generalize our theoretical findings.
Privacy Backdoors: Stealing Data with Corrupted Pretrained Models
Authors: Shanglun Feng; Florian Tramèr
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Practitioners commonly download pretrained machine learning models from open repositories and finetune them to fit specific applications. We show that this practice introduces a new risk of privacy backdoors. By tampering with a pretrained model’s weights, an attacker can fully compromise the privacy of the finetuning data. We show how to build privacy backdoors for a variety of models, including transformers, which enable an attacker to reconstruct individual finetuning samples, with a guaranteed success! We further show that backdoored models allow for tight privacy attacks on models trained with differential privacy (DP). The common optimistic practice of training DP models with loose privacy guarantees is thus insecure if the model is not trusted. Overall, our work highlights a crucial and overlooked supply chain attack on machine learning privacy.
On the Universality of Volume-Preserving and Coupling-Based Normalizing Flows
Authors: Felix Draxler; Stefan Wahl; Christoph Schnoerr; Ullrich Koethe
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: We present a novel theoretical framework for understanding the expressive power of normalizing flows. Despite their prevalence in scientific applications, a comprehensive understanding of flows remains elusive due to their restricted architectures. Existing theorems fall short as they require the use of arbitrarily ill-conditioned neural networks, limiting practical applicability. We propose a distributional universality theorem for well-conditioned coupling-based normalizing flows such as RealNVP. In addition, we show that volume-preserving normalizing flows are not universal, what distribution they learn instead, and how to fix their expressivity. Our results support the general wisdom that affine and related couplings are expressive and in general outperform volume-preserving flows, bridging a gap between empirical results and theoretical understanding.
On the Complexity of Finite-Sum Smooth Optimization under the Polyak–Łojasiewicz Condition
Authors: Yunyan Bai; Yuxing Liu; Luo Luo
Venue: ICML 2024 (ICML 2024 Spotlight)

Abstract: This paper considers the optimization problem of the form $\min_{{\bf x}\in{\mathbb R}^d} f({\bf x})\triangleq \frac{1}{n}\sum_{i=1}^n f_i({\bf x})$, where $f(\cdot)$ satisfies the Polyak–Łojasiewicz (PL) condition with parameter $\mu$ and $\{f_i(\cdot)\}_{i=1}^n$ is $L$-mean-squared smooth. We show that any gradient method requires at least $\Omega(n+\kappa\sqrt{n}\log(1/\epsilon))$ incremental first-order oracle (IFO) calls to find an $\epsilon$-suboptimal solution, where $\kappa\triangleq L/\mu$ is the condition number of the problem. This result nearly matches upper bounds of IFO complexity for best-known first-order methods. We also study the problem of minimizing the PL function in the distributed setting such that the individuals $f_1(\cdot),\dots,f_n(\cdot)$ are located on a connected network of $n$ agents. We provide lower bounds of $\Omega(\kappa/\sqrt{\gamma}\log(1/\epsilon))$, $\Omega((\kappa+\tau\kappa/\sqrt{\gamma})\log(1/\epsilon))$ and $\Omega\big(n+\kappa\sqrt{n}\log(1/\epsilon)\big)$ for communication rounds, time cost and local first-order oracle calls respectively, where $\gamma\in(0,1]$ is the spectral gap of the mixing matrix associated with the network and $\tau>0$ is the time cost of per communication round. Furthermore, we propose a decentralized first-order method that nearly matches above lower bounds in expectation.
On the Minimal Degree Bias in Generalization on the Unseen for non-Boolean Functions
Authors: Denys Pushkin; Raphaël Berthier; Emmanuel Abbe
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: We investigate the out-of-domain generalization of random feature (RF) models and Transformers. We first prove that in the `generalization on the unseen (GOTU)' setting, where training data is fully seen in some part of the domain but testing is made on another part, and for RF models in the small feature regime, the convergence takes place to interpolators of minimal degree as in the Boolean case (Abbe et al., 2023). We then consider the sparse target regime and explain how this regime relates to the small feature regime, but with a different regularization term that can alter the picture in the non-Boolean case. We show two different outcomes for the sparse regime with q-ary data tokens: (1) if the data is embedded with roots of unities, then a min-degree interpolator is learned like in the Boolean case for RF models, (2) if the data is not embedded as such, e.g., simply as integers, then RF models and Transformers may not learn minimal degree interpolators. This shows that the Boolean setting and its roots of unities generalization are special cases where the minimal degree interpolator offers a rare characterization of how learning takes place. For more general integer and real-valued settings, a more nuanced picture remains to be fully characterized.
IBD-PSC: Input-level Backdoor Detection via Parameter-oriented Scaling Consistency
Authors: Linshan Hou; Ruili Feng; Zhongyun Hua; Wei Luo; Leo Yu Zhang; Yiming Li
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Deep neural networks (DNNs) are vulnerable to backdoor attacks, where adversaries can maliciously trigger model misclassifications by implanting a hidden backdoor during model training. This paper proposes a simple yet effective input-level backdoor detection (dubbed IBD-PSC) as a `firewall' to filter out malicious testing images. Our method is motivated by an intriguing phenomenon, i.e., parameter-oriented scaling consistency (PSC), where the prediction confidences of poisoned samples are significantly more consistent than those of benign ones when amplifying model parameters. In particular, we provide theoretical analysis to safeguard the foundations of the PSC phenomenon. We also design an adaptive method to select BN layers to scale up for effective detection. Extensive experiments are conducted on benchmark datasets, verifying the effectiveness and efficiency of our IBD-PSC method and its resistance to adaptive attacks. Codes are available at https://github.com/THUYimingLi/BackdoorBox.
Refining Minimax Regret for Unsupervised Environment Design
Authors: Michael Beukman; Samuel Coward; Michael Matthews; Mattie Fellows; Minqi Jiang; Michael D Dennis; Jakob Nicolaus Foerster
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: In unsupervised environment design, reinforcement learning agents are trained on environment configurations (levels) generated by an adversary that maximises some objective. Regret is a commonly used objective that theoretically results in a minimax regret (MMR) policy with desirable robustness guarantees; in particular, the agent's maximum regret is bounded. However, once the agent reaches this regret bound on all levels, the adversary will only sample levels where regret cannot be further reduced. Although there may be possible performance improvements to be made outside of these regret-maximising levels, learning stagnates. In this work, we introduce *Bayesian level-perfect MMR* (BLP), a refinement of the minimax regret objective that overcomes this limitation. We formally show that solving for this objective results in a subset of MMR policies, and that BLP policies act consistently with a Perfect Bayesian policy over all levels. We further introduce an algorithm, *ReMiDi*, that results in a BLP policy at convergence. We empirically demonstrate that training on levels from a minimax regret adversary causes learning to prematurely stagnate, but that ReMiDi continues learning.
Equivariant Diffusion for Crystal Structure Prediction
Authors: Peijia Lin; Pin Chen; Rui Jiao; Qing Mo; Cen Jianhuan; Wenbing Huang; Yang Liu; Dan Huang; Yutong Lu
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: In addressing the challenge of Crystal Structure Prediction (CSP), symmetry-aware deep learning models, particularly diffusion models, have been extensively studied, which treat CSP as a conditional generation task. However, ensuring permutation, rotation, and periodic translation equivariance during diffusion process remains incompletely addressed. In this work, we propose EquiCSP, a novel equivariant diffusion-based generative model. We not only address the overlooked issue of lattice permutation equivariance in existing models, but also develop a unique noising algorithm that rigorously maintains periodic translation equivariance throughout both training and inference processes. Our experiments indicate that EquiCSP significantly surpasses existing models in terms of generating accurate structures and demonstrates faster convergence during the training process.
Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict?
Authors: Fan Yao; Chuanhao Li; Denis Nekipelov; Hongning Wang; Haifeng Xu
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: The advent of generative AI (GenAI) technology produces a transformative impact on the content creation landscape, offering alternative approaches to produce diverse, good-quality content across media, thereby reshaping online ecosystems but also raising concerns about market over-saturation and the potential marginalization of human creativity. Our work introduces a competition model generalized from the Tullock contest to analyze the tension between human creators and GenAI. Our theory and simulations suggest that despite challenges, a stable equilibrium between human and AI-generated content is possible. Our work contributes to understanding the competitive dynamics in the content creation industry, offering insights into the future interplay between human creativity and technological advancements in GenAI.
Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning
Authors: Idan Achituve; Idit Diamant; Arnon Netzer; Gal Chechik; Ethan Fetaya
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: As machine learning becomes more prominent there is a growing demand to perform several inference tasks in parallel. Multi-task learning (MTL) addresses this challenge by learning a single model that solves several tasks simultaneously and efficiently. Often optimizing MTL models entails first computing the gradient of the loss for each task, and then aggregating all the gradients to obtain a combined update direction. However, common methods following this approach do not consider an important aspect, the sensitivity in the dimensions of the gradients. Some dimensions may be more lenient for changes while others may be more restrictive. Here, we introduce a novel gradient aggregation procedure using Bayesian inference. We place a probability distribution over the task-specific parameters, which in turn induce a *distribution* over the gradients of the tasks. This valuable information allows us to quantify the uncertainty associated with each of the gradients' dimensions which is factored in when aggregating them. We empirically demonstrate the benefits of our approach in a variety of datasets, achieving state-of-the-art performance.
CauDiTS: Causal Disentangled Domain Adaptation of Multivariate Time Series
Authors: junxin lu; Shiliang Sun
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Unsupervised domain adaptation of multivariate time series aims to train a model to adapt its classification ability from a labeled source domain to an unlabeled target domain, where there are differences in the distribution between domains. Existing methods extract domain-invariant features directly via a shared feature extractor, neglecting the exploration of the underlying causal patterns, which undermines their reliability, especially in complex multivariate dynamic systems. To address this problem, we propose CauDiTS, an innovative framework for unsupervised domain adaptation of multivariate time series. CauDiTS adopts an adaptive rationale disentangler to disentangle domain-common causal rationales and domain-specific correlations from variable interrelationships. The stability of causal rationales across domains is vital for filtering domainspecific perturbations and facilitating the extraction of domain-invariant representations. Moreover, we promote the cross-domain consistency of intra-class causal rationales employing the learning strategies of causal prototype consistency and domain-intervention causality invariance. CauDiTS is evaluated on four benchmark datasets, demonstrating its effectiveness and outperforming state-of-the-art methods.
On The Fairness Impacts of Hardware Selection in Machine Learning
Authors: Sree Harsha Nelaturu; Nishaanth Kanna Ravichandran; Cuong Tran; Sara Hooker; Ferdinando Fioretto
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: In the machine learning ecosystem, hardware selection is often regarded as a mere utility, overshadowed by the spotlight on algorithms and data. This is especially relevant in contexts like ML-as-a-service platforms, where users often lack control over the hardware used for model deployment. This paper investigates the influence of hardware on the delicate balance between model performance and fairness. We demonstrate that hardware choices can exacerbate existing disparities, attributing these discrepancies to variations in gradient flows and loss surfaces across different demographic groups. Through both theoretical and empirical analysis, the paper not only identifies the underlying factors but also proposes an effective strategy for mitigating hardware-induced performance imbalances.
Repoformer: Selective Retrieval for Repository-Level Code Completion
Authors: Di Wu; Wasi Uddin Ahmad; Dejiao Zhang; Murali Krishna Ramanathan; Xiaofei Ma
Venue: ICML 2024 (ICML 2024 Oral)

Abstract: Recent advances in retrieval-augmented generation (RAG) have initiated a new era in repository-level code completion. However, the invariable use of retrieval in existing methods exposes issues in both efficiency and robustness, with a large proportion of the retrieved contexts proving unhelpful or harmful to code language models (code LMs). In this paper, we propose a selective RAG framework to avoid retrieval when unnecessary. To power this framework, we design a self-supervised learning approach to enable a code LM to accurately self-evaluate whether retrieval can improve its output quality and robustly leverage the potentially noisy retrieved contexts. Using this LM as both the selective RAG policy and the generation model, our framework achieves state-of-the-art repository-level code completion performance on diverse benchmarks including RepoEval, CrossCodeEval, and CrossCodeLongEval, a new long-form code completion benchmark. Meanwhile, our analyses show that selectively retrieving brings as much as 70% inference speedup in the online serving setting without harming the performance. We further demonstrate that our framework is able to accommodate different generation models, retrievers, and programming languages. These advancements position our framework as an important step towards more accurate and efficient repository-level code completion.
Riemannian coordinate descent algorithms on matrix manifolds
Authors: Andi Han; Pratik Jawanpuria; Bamdev Mishra
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Many machine learning applications are naturally formulated as optimization problems on Riemannian manifolds. The main idea behind Riemannian optimization is to maintain the feasibility of the variables while moving along a descent direction on the manifold. This results in updating all the variables at every iteration. In this work, we provide a general framework for developing computationally efficient coordinate descent (CD) algorithms on matrix manifolds that allows updating only a few variables at every iteration while adhering to the manifold constraint. In particular, we propose CD algorithms for various manifolds such as Stiefel, Grassmann, (generalized) hyperbolic, symplectic, and symmetric positive (semi)definite. While the cost per iteration of the proposed CD algorithms is low, we further develop a more efficient variant via a first-order approximation of the objective function. We analyze their convergence and complexity, and empirically illustrate their efficacy in several applications.
Learning Label Shift Correction for Test-Agnostic Long-Tailed Recognition
Authors: Tong Wei; Zhen Mao; Zi-Hao Zhou; Yuanyu Wan; Min-Ling Zhang
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Long-tail learning primarily focuses on mitigating the label distribution shift between long-tailed training data and uniformly distributed test data. However, in real-world applications, we often encounter a more intricate challenge where the test label distribution is agnostic. To address this problem, we first theoretically establish the substantial potential for reducing the generalization error if we can precisely estimate the test label distribution. Motivated by the theoretical insight, we introduce a simple yet effective solution called label shift correction (LSC). LSC estimates the test label distribution within the proposed framework of generalized black box shift estimation, and adjusts the predictions from a pre-trained model to align with the test distribution. Theoretical analyses confirm that accurate estimation of test label distribution can effectively reduce the generalization error. Extensive experimental results demonstrate that our method significantly outperforms previous state-of-the-art approaches, especially when confronted with non-uniform test label distribution. Notably, the proposed method is general and complements existing long-tail learning approaches, consistently improving their performance. The source code is available at https://github.com/Stomach-ache/label-shift-correction
Human Alignment of Large Language Models through Online Preference Optimisation
Authors: Daniele Calandriello; Zhaohan Daniel Guo; Remi Munos; Mark Rowland; Yunhao Tang; Bernardo Avila Pires; Pierre Harvey Richemond; Charline Le Lan; Michal Valko; Tianqi Liu; Rishabh Joshi; Zeyu Zheng; Bilal Piot
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Ensuring alignment of language model's outputs with human preferences is critical to guarantee a useful, safe, and pleasant user experience. Thus, human alignment has been extensively studied recently and several methods such as Reinforcement Learning from Human Feedback (RLHF), Direct Policy Optimisation (DPO) and Sequence Likelihood Calibration (SLiC) have emerged. In this paper, our contribution is two-fold. First, we show the equivalence between two recent alignment methods, namely Identity Policy Optimisation (IPO) and Nash Mirror Descent (Nash-MD). Second, we introduce a generalisation of IPO, named IPO-MD, that leverages the regularised sampling approach proposed by Nash-MD. This equivalence may seem surprising at first sight, since IPO is an offline method whereas Nash-MD is an online method using a preference model. However, this equivalence can be proven when we consider the online version of IPO, that is when both generations are sampled by the online policy and annotated by a trained preference model. Optimising the IPO loss with such a stream of data becomes then equivalent to finding the Nash equilibrium of the preference model through self-play. Building on this equivalence, we introduce the IPO-MD algorithm that generates data with a mixture policy (between the online and reference policy) similarly as the general Nash-MD algorithm. We compare online-IPO and IPO-MD to different online versions of existing losses on preference data such as DPO and SLiC on a summarisation task.