pwc-archive/papers-with-abstracts huggingface dataset:
Comparing Comparators in Generalization Bounds
Authors: Fredrik Hellström, Benjamin Guedj
Date: 2023-10-16 00:00:00
Tasks: Generalization Bounds

We derive generic information-theoretic and PAC-Bayesian generalization bounds involving an arbitrary convex comparator function, which measures the discrepancy between the training and population loss. The bounds hold under the assumption that the cumulant-generating function (CGF) of the comparator is upper-bounded by the corresponding CGF within a family of bounding distributions. We show that the tightest possible bound is obtained with the comparator being the convex conjugate of the CGF of the bounding distribution, also known as the Cram\'er function. This conclusion applies more broadly to generalization bounds with a similar structure. This confirms the near-optimality of known bounds for bounded and sub-Gaussian losses and leads to novel bounds under other bounding distributions.
Learning optimal integration of spatial and temporal information in noisy chemotaxis
Authors: Albert Alonso, Julius B. Kirkegaard
Date: 2023-10-16 00:00:00
Tasks: Deep Reinforcement Learning

We investigate the boundary between chemotaxis driven by spatial estimation of gradients and chemotaxis driven by temporal estimation. While it is well known that spatial chemotaxis becomes disadvantageous for small organisms at high noise levels, it is unclear whether there is a discontinuous switch of optimal strategies or a continuous transition exists. Here, we employ deep reinforcement learning to study the possible integration of spatial and temporal information in an a priori unconstrained manner. We parameterize such a combined chemotactic policy by a recurrent neural network and evaluate it using a minimal theoretical model of a chemotactic cell. By comparing with constrained variants of the policy, we show that it converges to purely temporal and spatial strategies at small and large cell sizes, respectively. We find that the transition between the regimes is continuous, with the combined strategy outperforming in the transition region both the constrained variants as well as models that explicitly integrate spatial and temporal information. Finally, by utilizing the attribution method of integrated gradients, we show that the policy relies on a non-trivial combination of spatially and temporally derived gradient information in a ratio that varies dynamically during the chemotactic trajectories.
From Spectral Theorem to Statistical Independence with Application to System Identification
Authors: Muhammad Abdullah Naeem, Amir Khazraei, Miroslav Pajic
Date: 2023-10-16 00:00:00

High dimensional random dynamical systems are ubiquitous, including -- but not limited to -- cyber-physical systems, daily return on different stocks of S&P 1500 and velocity profile of interacting particle systems around McKeanVlasov limit. Mathematically, underlying phenomenon can be captured via a stable $n$-dimensional linear transformation `$A$' and additive randomness. System identification aims at extracting useful information about underlying dynamical system, given a length $N$ trajectory from it (corresponds to an $n \times N$ dimensional data matrix). We use spectral theorem for non-Hermitian operators to show that spatio-temperal correlations are dictated by the discrepancy between algebraic and geometric multiplicity of distinct eigenvalues corresponding to state transition matrix. Small discrepancies imply that original trajectory essentially comprises of multiple lower dimensional random dynamical systems living on $A$ invariant subspaces and are statistically independent of each other. In the process, we provide first quantitative handle on decay rate of finite powers of state transition matrix $\|A^{k}\|$ . It is shown that when a stable dynamical system has only one distinct eigenvalue and discrepancy of $n-1$: $\|A\|$ has a dependence on $n$, resulting dynamics are spatially inseparable and consequently there exist at least one row with covariates of typical size $\Theta\big(\sqrt{N-n+1}$ $e^{n}\big)$ i.e., even under stability assumption, covariates can suffer from curse of dimensionality. In the light of these findings we set the stage for non-asymptotic error analysis in estimation of state transition matrix $A$ via least squares regression on observed trajectory by showing that element-wise error is essentially a variant of well-know Littlewood-Offord problem.
Reproducing Bayesian Posterior Distributions for Exoplanet Atmospheric Parameter Retrievals with a Machine Learning Surrogate Model
Authors: Eyup B. Unlu, Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva
Date: 2023-10-16 00:00:00
Tasks: Retrieval

We describe a machine-learning-based surrogate model for reproducing the Bayesian posterior distributions for exoplanet atmospheric parameters derived from transmission spectra of transiting planets with typical retrieval software such as TauRex. The model is trained on ground truth distributions for seven parameters: the planet radius, the atmospheric temperature, and the mixing ratios for five common absorbers: $H_2O$, $CH_4$, $NH_3$, $CO$ and $CO_2$. The model performance is enhanced by domain-inspired preprocessing of the features and the use of semi-supervised learning in order to leverage the large amount of unlabelled training data available. The model was among the winning solutions in the 2023 Ariel Machine Learning Data Challenge.
ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models
Authors: Ziniu Li, Tian Xu, Yushun Zhang, Zhihang Lin, Yang Yu, Ruoyu Sun, Zhi-Quan Luo
Date: 2023-10-16 00:00:00
Tasks: General Reinforcement Learning, GPU, reinforcement-learning

Reinforcement Learning from Human Feedback (RLHF) is key to aligning Large Language Models (LLMs), typically paired with the Proximal Policy Optimization (PPO) algorithm. While PPO is a powerful method designed for general reinforcement learning tasks, it is overly sophisticated for LLMs, leading to laborious hyper-parameter tuning and significant computation burdens. To make RLHF efficient, we present ReMax, which leverages 3 properties of RLHF: fast simulation, deterministic transitions, and trajectory-level rewards. These properties are not exploited in PPO, making it less suitable for RLHF. Building on the renowned REINFORCE algorithm, ReMax does not require training an additional value model as in PPO and is further enhanced with a new variance reduction technique. ReMax offers several benefits over PPO: it is simpler to implement, eliminates more than 4 hyper-parameters in PPO, reduces GPU memory usage, and shortens training time. ReMax can save about 46% GPU memory than PPO when training a 7B model and enables training on A800-80GB GPUs without the memory-saving offloading technique needed by PPO. Applying ReMax to a Mistral-7B model resulted in a 94.78% win rate on the AlpacaEval leaderboard and a 7.739 score on MT-bench, setting a new SOTA for open-source 7B models. These results show the effectiveness of ReMax while addressing the limitations of PPO in LLMs.
Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies
Authors: Kieran Wood, Samuel Kessler, Stephen J. Roberts, Stefan Zohren
Date: 2023-10-16 00:00:00
Tasks: Few-Shot Learning, Time Series

Forecasting models for systematic trading strategies do not adapt quickly when financial market conditions rapidly change, as was seen in the advent of the COVID-19 pandemic in 2020, causing many forecasting models to take loss-making positions. To deal with such situations, we propose a novel time-series trend-following forecaster that can quickly adapt to new market conditions, referred to as regimes. We leverage recent developments from the deep learning community and use few-shot learning. We propose the Cross Attentive Time-Series Trend Network -- X-Trend -- which takes positions attending over a context set of financial time-series regimes. X-Trend transfers trends from similar patterns in the context set to make forecasts, then subsequently takes positions for a new distinct target regime. By quickly adapting to new financial regimes, X-Trend increases Sharpe ratio by 18.9% over a neural forecaster and 10-fold over a conventional Time-series Momentum strategy during the turbulent market period from 2018 to 2023. Our strategy recovers twice as quickly from the COVID-19 drawdown compared to the neural-forecaster. X-Trend can also take zero-shot positions on novel unseen financial assets obtaining a 5-fold Sharpe ratio increase versus a neural time-series trend forecaster over the same period. Furthermore, the cross-attention mechanism allows us to interpret the relationship between forecasts and patterns in the context set.
Passive Inference Attacks on Split Learning via Adversarial Regularization
Authors: Xiaochen Zhu, Xinjian Luo, Yuncheng Wu, Yangfan Jiang, Xiaokui Xiao, Beng Chin Ooi
Date: 2023-10-16 00:00:00
Tasks: Federated Learning

Split Learning (SL) has emerged as a practical and efficient alternative to traditional federated learning. While previous attempts to attack SL have often relied on overly strong assumptions or targeted easily exploitable models, we seek to develop more capable attacks. We introduce SDAR, a novel attack framework against SL with an honest-but-curious server. SDAR leverages auxiliary data and adversarial regularization to learn a decodable simulator of the client's private model, which can effectively infer the client's private features under the vanilla SL, and both features and labels under the U-shaped SL. We perform extensive experiments in both configurations to validate the effectiveness of our proposed attacks. Notably, in challenging scenarios where existing passive attacks struggle to reconstruct the client's private data effectively, SDAR consistently achieves significantly superior attack performance, even comparable to active attacks. On CIFAR-10, at the deep split level of 7, SDAR achieves private feature reconstruction with less than 0.025 mean squared error in both the vanilla and the U-shaped SL, and attains a label inference accuracy of over 98% in the U-shaped setting, while existing attacks fail to produce non-trivial results.
Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems
Authors: Yunli Wang, Zhiqiang Wang, Jian Yang, Shiyang Wen, Dongying Kong, Han Li, Kun Gai
Date: 2023-10-16 00:00:00
Tasks: Learning-To-Rank, Multi-Task Learning, Recommendation Systems

Cascade ranking is widely used for large-scale top-k selection problems in online advertising and recommendation systems, and learning-to-rank is an important way to optimize the models in cascade ranking. Previous works on learning-to-rank usually focus on letting the model learn the complete order or top-k order, and adopt the corresponding rank metrics (e.g. OPA and NDCG@k) as optimization targets. However, these targets can not adapt to various cascade ranking scenarios with varying data complexities and model capabilities; and the existing metric-driven methods such as the Lambda framework can only optimize a rough upper bound of limited metrics, potentially resulting in sub-optimal and performance misalignment. To address these issues, we propose a novel perspective on optimizing cascade ranking systems by highlighting the adaptability of optimization targets to data complexities and model capabilities. Concretely, we employ multi-task learning to adaptively combine the optimization of relaxed and full targets, which refers to metrics Recall@m@k and OPA respectively. We also introduce permutation matrix to represent the rank metrics and employ differentiable sorting techniques to relax hard permutation matrix with controllable approximate error bound. This enables us to optimize both the relaxed and full targets directly and more appropriately. We named this method as Adaptive Neural Ranking Framework (abbreviated as ARF). Furthermore, we give a specific practice under ARF. We use the NeuralSort to obtain the relaxed permutation matrix and draw on the variant of the uncertainty weight method in multi-task learning to optimize the proposed losses jointly. Experiments on a total of 4 public and industrial benchmarks show the effectiveness and generalization of our method, and online experiment shows that our method has significant application value.
A Geometric Insight into Equivariant Message Passing Neural Networks on Riemannian Manifolds
Authors: Ilyes Batatia
Date: 2023-10-16 00:00:00

This work proposes a geometric insight into equivariant message passing on Riemannian manifolds. As previously proposed, numerical features on Riemannian manifolds are represented as coordinate-independent feature fields on the manifold. To any coordinate-independent feature field on a manifold comes attached an equivariant embedding of the principal bundle to the space of numerical features. We argue that the metric this embedding induces on the numerical feature space should optimally preserve the principal bundle's original metric. This optimality criterion leads to the minimization of a twisted form of the Polyakov action with respect to the graph of this embedding, yielding an equivariant diffusion process on the associated vector bundle. We obtain a message passing scheme on the manifold by discretizing the diffusion equation flow for a fixed time step. We propose a higher-order equivariant diffusion process equivalent to diffusion on the cartesian product of the base manifold. The discretization of the higher-order diffusion process on a graph yields a new general class of equivariant GNN, generalizing the ACE and MACE formalism to data on Riemannian manifolds.
Taming the Sigmoid Bottleneck: Provably Argmaxable Sparse Multi-Label Classification
Authors: Andreas Grivas, Antonio Vergari, Adam Lopez
Date: 2023-10-16 00:00:00
Tasks: Multi-class Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Sigmoid output layers are widely used in multi-label classification (MLC) tasks, in which multiple labels can be assigned to any input. In many practical MLC tasks, the number of possible labels is in the thousands, often exceeding the number of input features and resulting in a low-rank output layer. In multi-class classification, it is known that such a low-rank output layer is a bottleneck that can result in unargmaxable classes: classes which cannot be predicted for any input. In this paper, we show that for MLC tasks, the analogous sigmoid bottleneck results in exponentially many unargmaxable label combinations. We explain how to detect these unargmaxable outputs and demonstrate their presence in three widely used MLC datasets. We then show that they can be prevented in practice by introducing a Discrete Fourier Transform (DFT) output layer, which guarantees that all sparse label combinations with up to $k$ active labels are argmaxable. Our DFT layer trains faster and is more parameter efficient, matching the F1@k score of a sigmoid layer while using up to 50% fewer trainable parameters. Our code is publicly available at https://github.com/andreasgrv/sigmoid-bottleneck.
Equivariant Matrix Function Neural Networks
Authors: Ilyes Batatia, Lars L. Schaaf, Huajie Chen, Gábor Csányi, Christoph Ortner, Felix A. Faber
Date: 2023-10-16 00:00:00
Tasks: Graph Regression

Graph Neural Networks (GNNs), especially message-passing neural networks (MPNNs), have emerged as powerful architectures for learning on graphs in diverse applications. However, MPNNs face challenges when modeling non-local interactions in graphs such as large conjugated molecules, and social networks due to oversmoothing and oversquashing. Although Spectral GNNs and traditional neural networks such as recurrent neural networks and transformers mitigate these challenges, they often lack generalizability, or fail to capture detailed structural relationships or symmetries in the data. To address these concerns, we introduce Matrix Function Neural Networks (MFNs), a novel architecture that parameterizes non-local interactions through analytic matrix equivariant functions. Employing resolvent expansions offers a straightforward implementation and the potential for linear scaling with system size. The MFN architecture achieves stateof-the-art performance in standard graph benchmarks, such as the ZINC and TU datasets, and is able to capture intricate non-local interactions in quantum systems, paving the way to new state-of-the-art force fields.
Continuously Adapting Random Sampling (CARS) for Power Electronics Parameter Design
Authors: Dominik Happel, Philipp Brendel, Andreas Rosskopf, Stefan Ditze
Date: 2023-10-16 00:00:00

To date, power electronics parameter design tasks are usually tackled using detailed optimization approaches with detailed simulations or using brute force grid search grid search with very fast simulations. A new method, named "Continuously Adapting Random Sampling" (CARS) is proposed, which provides a continuous method in between. This allows for very fast, and / or large amounts of simulations, but increasingly focuses on the most promising parameter ranges. Inspirations are drawn from multi-armed bandit research and lead to prioritized sampling of sub-domains in one high-dimensional parameter tensor. Performance has been evaluated on three exemplary power electronic use-cases, where resulting designs appear competitive to genetic algorithms, but additionally allow for highly parallelizable simulation, as well as continuous progression between explorative and exploitative settings.
Towards Fair and Calibrated Models
Authors: Anand Brahmbhatt, Vipul Rathore, Mausam, Parag Singla
Date: 2023-10-16 00:00:00
Tasks: Fairness

Recent literature has seen a significant focus on building machine learning models with specific properties such as fairness, i.e., being non-biased with respect to a given set of attributes, calibration i.e., model confidence being aligned with its predictive accuracy, and explainability, i.e., ability to be understandable to humans. While there has been work focusing on each of these aspects individually, researchers have shied away from simultaneously addressing more than one of these dimensions. In this work, we address the problem of building models which are both fair and calibrated. We work with a specific definition of fairness, which closely matches [Biswas et. al. 2019], and has the nice property that Bayes optimal classifier has the maximum possible fairness under our definition. We show that an existing negative result towards achieving a fair and calibrated model [Kleinberg et. al. 2017] does not hold for our definition of fairness. Further, we show that ensuring group-wise calibration with respect to the sensitive attributes automatically results in a fair model under our definition. Using this result, we provide a first cut approach for achieving fair and calibrated models, via a simple post-processing technique based on temperature scaling. We then propose modifications of existing calibration losses to perform group-wise calibration, as a way of achieving fair and calibrated models in a variety of settings. Finally, we perform extensive experimentation of these techniques on a diverse benchmark of datasets, and present insights on the pareto-optimality of the resulting solutions.
Towards a Better Understanding of Variations in Zero-Shot Neural Machine Translation Performance
Authors: Shaomu Tan, Christof Monz
Date: 2023-10-16 00:00:00
Tasks: Machine Translation, NMT, Translation

Multilingual Neural Machine Translation (MNMT) facilitates knowledge sharing but often suffers from poor zero-shot (ZS) translation qualities. While prior work has explored the causes of overall low ZS performance, our work introduces a fresh perspective: the presence of high variations in ZS performance. This suggests that MNMT does not uniformly exhibit poor ZS capability; instead, certain translation directions yield reasonable results. Through systematic experimentation involving 1,560 language directions spanning 40 languages, we identify three key factors contributing to high variations in ZS NMT performance: 1) target side translation capability 2) vocabulary overlap 3) linguistic properties. Our findings highlight that the target side translation quality is the most influential factor, with vocabulary overlap consistently impacting ZS performance. Additionally, linguistic properties, such as language family and writing system, play a role, particularly with smaller models. Furthermore, we suggest that the off-target issue is a symptom of inadequate ZS performance, emphasizing that zero-shot translation challenges extend beyond addressing the off-target problem. We release the data and models serving as a benchmark to study zero-shot for future research at https://github.com/Smu-Tan/ZS-NMT-Variations
Multi-Factor Spatio-Temporal Prediction based on Graph Decomposition Learning
Authors: Jiahao Ji, Jingyuan Wang, Yu Mou, Cheng Long
Date: 2023-10-16 00:00:00
Tasks: Prediction

Spatio-temporal (ST) prediction is an important and widely used technique in data mining and analytics, especially for ST data in urban systems such as transportation data. In practice, the ST data generation is usually influenced by various latent factors tied to natural phenomena or human socioeconomic activities, impacting specific spatial areas selectively. However, existing ST prediction methods usually do not refine the impacts of different factors, but directly model the entangled impacts of multiple factors. This amplifies the modeling complexity of ST data and compromises model interpretability. To this end, we propose a multi-factor ST prediction task that predicts partial ST data evolution under different factors, and combines them for a final prediction. We make two contributions to this task: an effective theoretical solution and a portable instantiation framework. Specifically, we first propose a theoretical solution called decomposed prediction strategy and prove its effectiveness from the perspective of information entropy theory. On top of that, we instantiate a novel model-agnostic framework, named spatio-temporal graph decomposition learning (STGDL), for multi-factor ST prediction. The framework consists of two main components: an automatic graph decomposition module that decomposes the original graph structure inherent in ST data into subgraphs corresponding to different factors, and a decomposed learning network that learns the partial ST data on each subgraph separately and integrates them for the final prediction. We conduct extensive experiments on four real-world ST datasets of two types of graphs, i.e., grid graph and network graph. Results show that our framework significantly reduces prediction errors of various ST models by 9.41% on average (35.36% at most). Furthermore, a case study reveals the interpretability potential of our framework.
Machine learning in physics: a short guide
Authors: Francisco A. Rodrigues
Date: 2023-10-16 00:00:00
Tasks: Causal Inference, regression, reinforcement-learning, Reinforcement Learning, Symbolic Regression

Machine learning is a rapidly growing field with the potential to revolutionize many areas of science, including physics. This review provides a brief overview of machine learning in physics, covering the main concepts of supervised, unsupervised, and reinforcement learning, as well as more specialized topics such as causal inference, symbolic regression, and deep learning. We present some of the principal applications of machine learning in physics and discuss the associated challenges and perspectives.