pwc-archive/papers-with-abstracts huggingface dataset:
Version age-based client scheduling policy for federated learning
Authors: Xinyi Hu, Nikolaos Pappas, Howard H. Yang
Date: 2024-02-08 00:00:00
Tasks: Federated Learning, Privacy Preserving, Scheduling

Federated Learning (FL) has emerged as a privacy-preserving machine learning paradigm facilitating collaborative training across multiple clients without sharing local data. Despite advancements in edge device capabilities, communication bottlenecks present challenges in aggregating a large number of clients; only a portion of the clients can update their parameters upon each global aggregation. This phenomenon introduces the critical challenge of stragglers in FL and the profound impact of client scheduling policies on global model convergence and stability. Existing scheduling strategies address staleness but predominantly focus on either timeliness or content. Motivated by this, we introduce the novel concept of Version Age of Information (VAoI) to FL. Unlike traditional Age of Information metrics, VAoI considers both timeliness and content staleness. Each client's version age is updated discretely, indicating the freshness of information. VAoI is incorporated into the client scheduling policy to minimize the average VAoI, mitigating the impact of outdated local updates and enhancing the stability of FL systems.
Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes
Authors: Lucio Dery, Steven Kolawole, Jean-François Kagy, Virginia Smith, Graham Neubig, Ameet Talwalkar
Date: 2024-02-08 00:00:00
Tasks: GPU

Structured pruning is a promising approach to create smaller, faster LLMs. However, existing methods typically rely on backward passes, which can inflate memory requirements and compute costs. In this work we introduce Bonsai, a gradient-free structured pruning method that eliminates the need for backpropagation, significantly reducing memory requirements and compute costs while achieving state-of-the-art pruning performance. Bonsai uses forward-pass-only perturbative pruning to enable efficient compression of large models on a broader range of hardware configurations. Unlike existing structured pruning approaches, Bonsai not only achieves better compression with fewer resources, but also produces models that are twice as fast as those generated by semi-structured pruning. As a concrete demonstration, we use Bonsai to prune an 8B LLaMA-3 model to 50% sparsity on a single A6000 GPU -- a task infeasible with backprop-based methods, which require 2-3x memory. Our results show that removing backprop as a requirement not only enables pruning larger models on constrained hardware but can also lead to state-of-the-art efficiency and performance.
Adaptive Activation Functions for Predictive Modeling with Sparse Experimental Data
Authors: Farhad Pourkamali-Anaraki, Tahamina Nasrin, Robert E. Jensen, Amy M. Peterson, Christopher J. Hansen
Date: 2024-02-08 00:00:00
Tasks: image-classification, Image Classification

A pivotal aspect in the design of neural networks lies in selecting activation functions, crucial for introducing nonlinear structures that capture intricate input-output patterns. While the effectiveness of adaptive or trainable activation functions has been studied in domains with ample data, like image classification problems, significant gaps persist in understanding their influence on classification accuracy and predictive uncertainty in settings characterized by limited data availability. This research aims to address these gaps by investigating the use of two types of adaptive activation functions. These functions incorporate shared and individual trainable parameters per hidden layer and are examined in three testbeds derived from additive manufacturing problems containing fewer than one hundred training instances. Our investigation reveals that adaptive activation functions, such as Exponential Linear Unit (ELU) and Softplus, with individual trainable parameters, result in accurate and confident prediction models that outperform fixed-shape activation functions and the less flexible method of using identical trainable activation functions in a hidden layer. Therefore, this work presents an elegant way of facilitating the design of adaptive neural networks in scientific and engineering problems.
Trade-Offs of Diagonal Fisher Information Matrix Estimators
Authors: Alexander Soen, Ke Sun
Date: 2024-02-08 00:00:00
Tasks: Navigate

The Fisher information matrix can be used to characterize the local geometry of the parameter space of neural networks. It elucidates insightful theories and useful tools to understand and optimize neural networks. Given its high computational cost, practitioners often use random estimators and evaluate only the diagonal entries. We examine two popular estimators whose accuracy and sample complexity depend on their associated variances. We derive bounds of the variances and instantiate them in neural networks for regression and classification. We navigate trade-offs for both estimators based on analytical and numerical studies. We find that the variance quantities depend on the non-linearity wrt different parameter groups and should not be neglected when estimating the Fisher information.
Reduced-order modeling of unsteady fluid flow using neural network ensembles
Authors: Rakesh Halder, Mohammadmehdi Ataei, Hesam Salehipour, Krzysztof Fidkowski, Kevin Maki
Date: 2024-02-08 00:00:00
Tasks: Ensemble Learning, Prediction, Time Series, Time Series Prediction

The use of deep learning has become increasingly popular in reduced-order models (ROMs) to obtain low-dimensional representations of full-order models. Convolutional autoencoders (CAEs) are often used to this end as they are adept at handling data that are spatially distributed, including solutions to partial differential equations. When applied to unsteady physics problems, ROMs also require a model for time-series prediction of the low-dimensional latent variables. Long short-term memory (LSTM) networks, a type of recurrent neural network useful for modeling sequential data, are frequently employed in data-driven ROMs for autoregressive time-series prediction. When making predictions at unseen design points over long time horizons, error propagation is a frequently encountered issue, where errors made early on can compound over time and lead to large inaccuracies. In this work, we propose using bagging, a commonly used ensemble learning technique, to develop a fully data-driven ROM framework referred to as the CAE-eLSTM ROM that uses CAEs for spatial reconstruction of the full-order model and LSTM ensembles for time-series prediction. When applied to two unsteady fluid dynamics problems, our results show that the presented framework effectively reduces error propagation and leads to more accurate time-series prediction of latent variables at unseen points.
Noise Contrastive Alignment of Language Models with Explicit Rewards
Authors: Huayu Chen, Guande He, Lifan Yuan, Ganqu Cui, Hang Su, Jun Zhu
Date: 2024-02-08 00:00:00
Tasks: Language Modelling, Math

User intentions are typically formalized as evaluation rewards to be maximized when fine-tuning language models (LMs). Existing alignment methods, such as Direct Preference Optimization (DPO), are mainly tailored for pairwise preference data where rewards are implicitly defined rather than explicitly given. In this paper, we introduce a general framework for LM alignment, leveraging Noise Contrastive Estimation (NCE) to bridge the gap in handling reward datasets explicitly annotated with scalar evaluations. Our framework comprises two parallel algorithms, NCA and InfoNCA, both enabling the direct extraction of an LM policy from reward data as well as preference data. Notably, we show that the DPO loss is a special case of our proposed InfoNCA objective under pairwise preference settings, thereby integrating and extending current alignment theories. By comparing NCA and InfoNCA, we demonstrate that the well-observed decreasing-likelihood trend of DPO/InfoNCA is caused by their focus on adjusting relative likelihood across different responses. In contrast, NCA optimizes the absolute likelihood for each response, thereby effectively preventing the chosen likelihood from decreasing. We evaluate our methods in both reward and preference settings with Mistral-8*7B and 7B models. Experiments suggest that InfoNCA/NCA surpasses various preference baselines when reward datasets are available. We also find NCA significantly outperforms DPO in complex reasoning tasks like math and coding.
Principled Preferential Bayesian Optimization
Authors: Wenjie Xu, Wenbin Wang, Yuning Jiang, Bratislav Svetozarevic, Colin N. Jones
Date: 2024-02-08 00:00:00
Tasks: Bayesian Optimization, Gaussian Processes

We study the problem of preferential Bayesian optimization (BO), where we aim to optimize a black-box function with only preference feedback over a pair of candidate solutions. Inspired by the likelihood ratio idea, we construct a confidence set of the black-box function using only the preference feedback. An optimistic algorithm with an efficient computational method is then developed to solve the problem, which enjoys an information-theoretic bound on the total cumulative regret, a first-of-its-kind for preferential BO. This bound further allows us to design a scheme to report an estimated best solution, with a guaranteed convergence rate. Experimental results on sampled instances from Gaussian processes, standard test functions, and a thermal comfort optimization problem all show that our method stably achieves better or competitive performance as compared to the existing state-of-the-art heuristics, which, however, do not have theoretical guarantees on regret bounds or convergence.
Exploring Learning Complexity for Efficient Downstream Dataset Pruning
Authors: Wenyu Jiang, Zhenlong Liu, Zejian Xie, Songxin Zhang, BingYi Jing, Hongxin Wei
Date: 2024-02-08 00:00:00
Tasks: Informativeness

The ever-increasing fine-tuning cost of large-scale pre-trained models gives rise to the importance of dataset pruning, which aims to reduce dataset size while maintaining task performance. However, existing dataset pruning methods require training on the entire dataset, which is impractical for large-scale pre-trained models. In this paper, we propose a straightforward, novel, and training-free hardness score named Distorting-based Learning Complexity (DLC), to identify informative images and instructions from the downstream dataset efficiently. Our method is motivated by the observation that easy samples learned faster can also be learned with fewer parameters. Specifically, we define the Learning Complexity to quantify sample hardness and utilize a lightweight weights masking process for fast estimation, instead of the costly SGD optimization. Based on DLC, we further design a flexible under-sampling with randomness (dubbed FlexRand), replacing the top-K strategy, to alleviate the severe subset distribution shift. Extensive experiments with downstream image and instructions dataset pruning benchmarks demonstrate the effectiveness and efficiency of the proposed approach. In the images pruning benchmark, DLC significantly reduces the pruning time by 35x while establishing state-of-the-art performance with FlexRand.
Revisiting Early-Learning Regularization When Federated Learning Meets Noisy Labels
Authors: Taehyeon Kim, Donggyu Kim, Se-Young Yun
Date: 2024-02-08 00:00:00
Tasks: Federated Learning, Memorization

In the evolving landscape of federated learning (FL), addressing label noise presents unique challenges due to the decentralized and diverse nature of data collection across clients. Traditional centralized learning approaches to mitigate label noise are constrained in FL by privacy concerns and the heterogeneity of client data. This paper revisits early-learning regularization, introducing an innovative strategy, Federated Label-mixture Regularization (FLR). FLR adeptly adapts to FL's complexities by generating new pseudo labels, blending local and global model predictions. This method not only enhances the accuracy of the global model in both i.i.d. and non-i.i.d. settings but also effectively counters the memorization of noisy labels. Demonstrating compatibility with existing label noise and FL techniques, FLR paves the way for improved generalization in FL environments fraught with label inaccuracies.
Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference
Authors: Luca Masserano, Alex Shen, Michele Doro, Tommaso Dorigo, Rafael Izbicki, Ann B. Lee
Date: 2024-02-08 00:00:00
Tasks: Domain Adaptation, Uncertainty Quantification, valid

An open scientific challenge is how to classify events with reliable measures of uncertainty, when we have a mechanistic model of the data-generating process but the distribution over both labels and latent nuisance parameters is different between train and target data. We refer to this type of distributional shift as generalized label shift (GLS). Direct classification using observed data $\mathbf{X}$ as covariates leads to biased predictions and invalid uncertainty estimates of labels $Y$. We overcome these biases by proposing a new method for robust uncertainty quantification that casts classification as a hypothesis testing problem under nuisance parameters. The key idea is to estimate the classifier's receiver operating characteristic (ROC) across the entire nuisance parameter space, which allows us to devise cutoffs that are invariant under GLS. Our method effectively endows a pre-trained classifier with domain adaptation capabilities and returns valid prediction sets while maintaining high power. We demonstrate its performance on two challenging scientific problems in biology and astroparticle physics with data from realistic mechanistic models.
Navigating the Knowledge Sea: Planet-scale answer retrieval using LLMs
Authors: Dipankar Sarkar
Date: 2024-02-07 00:00:00
Tasks: Information Retrieval, Retrieval

Information retrieval is a rapidly evolving field of information retrieval, which is characterized by a continuous refinement of techniques and technologies, from basic hyperlink-based navigation to sophisticated algorithm-driven search engines. This paper aims to provide a comprehensive overview of the evolution of Information Retrieval Technology, with a particular focus on the role of Large Language Models (LLMs) in bridging the gap between traditional search methods and the emerging paradigm of answer retrieval. The integration of LLMs in the realms of response retrieval and indexing signifies a paradigm shift in how users interact with information systems. This paradigm shift is driven by the integration of large language models (LLMs) like GPT-4, which are capable of understanding and generating human-like text, thus enabling them to provide more direct and contextually relevant answers to user queries. Through this exploration, we seek to illuminate the technological milestones that have shaped this journey and the potential future directions in this rapidly changing field.
Investigating Generalization Behaviours of Generative Flow Networks
Authors: Lazar Atanackovic, Emmanuel Bengio
Date: 2024-02-07 00:00:00

Generative Flow Networks (GFlowNets, GFNs) are a generative framework for learning unnormalized probability mass functions over discrete spaces. Since their inception, GFlowNets have proven to be useful for learning generative models in applications where the majority of the discrete space is unvisited during training. This has inspired some to hypothesize that GFlowNets, when paired with deep neural networks (DNNs), have favorable generalization properties. In this work, we empirically verify some of the hypothesized mechanisms of generalization of GFlowNets. We accomplish this by introducing a novel graph-based benchmark environment where reward difficulty can be easily varied, $p(x)$ can be computed exactly, and an unseen test set can be constructed to quantify generalization performance. Using this graph-based environment, we are able to systematically test the hypothesized mechanisms of generalization of GFlowNets and put forth a set of empirical observations that summarize our findings. In particular, we find (and confirm) that the functions that GFlowNets learn to approximate have an implicit underlying structure which facilitate generalization. Surprisingly -- and somewhat contradictory to existing knowledge -- we also find that GFlowNets are sensitive to being trained offline and off-policy. However, the reward implicitly learned by GFlowNets is robust to changes in the training distribution.
Graph convolutional network as a fast statistical emulator for numerical ice sheet modeling
Authors: Maryam Rahnemoonfar, Younghyun Koo
Date: 2024-02-07 00:00:00
Tasks: GPU, Graph Attention

The Ice-sheet and Sea-level System Model (ISSM) provides numerical solutions for ice sheet dynamics using finite element and fine mesh adaption. However, considering ISSM is compatible only with central processing units (CPUs), it has limitations in economizing computational time to explore the linkage between climate forcings and ice dynamics. Although several deep learning emulators using graphic processing units (GPUs) have been proposed to accelerate ice sheet modeling, most of them rely on convolutional neural networks (CNNs) designed for regular grids. Since they are not appropriate for the irregular meshes of ISSM, we use a graph convolutional network (GCN) to replicate the adapted mesh structures of the ISSM. When applied to transient simulations of the Pine Island Glacier (PIG), Antarctica, the GCN successfully reproduces ice thickness and velocity with a correlation coefficient of approximately 0.997, outperforming non-graph models, including fully convolutional network (FCN) and multi-layer perceptron (MLP). Compared to the fixed-resolution approach of the FCN, the flexible-resolution structure of the GCN accurately captures detailed ice dynamics in fast-ice regions. By leveraging 60-100 times faster computational time of the GPU-based GCN emulator, we efficiently examine the impacts of basal melting rates on the ice sheet dynamics in the PIG.
Analyzing Adversarial Inputs in Deep Reinforcement Learning
Authors: Davide Corsi, Guy Amir, Guy Katz, Alessandro Farinelli
Date: 2024-02-07 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning

In recent years, Deep Reinforcement Learning (DRL) has become a popular paradigm in machine learning due to its successful applications to real-world and complex systems. However, even the state-of-the-art DRL models have been shown to suffer from reliability concerns -- for example, their susceptibility to adversarial inputs, i.e., small and abundant input perturbations that can fool the models into making unpredictable and potentially dangerous decisions. This drawback limits the deployment of DRL systems in safety-critical contexts, where even a small error cannot be tolerated. In this work, we present a comprehensive analysis of the characterization of adversarial inputs, through the lens of formal verification. Specifically, we introduce a novel metric, the Adversarial Rate, to classify models based on their susceptibility to such perturbations, and present a set of tools and algorithms for its computation. Our analysis empirically demonstrates how adversarial inputs can affect the safety of a given DRL system with respect to such perturbations. Moreover, we analyze the behavior of these configurations to suggest several useful practices and guidelines to help mitigate the vulnerability of trained DRL networks.
No Dimensional Sampling Coresets for Classification
Authors: Meysam Alishahi, Jeff M. Phillips
Date: 2024-02-07 00:00:00
Tasks: Classification, Sensitivity

We refine and generalize what is known about coresets for classification problems via the sensitivity sampling framework. Such coresets seek the smallest possible subsets of input data, so one can optimize a loss function on the coreset and ensure approximation guarantees with respect to the original data. Our analysis provides the first no dimensional coresets, so the size does not depend on the dimension. Moreover, our results are general, apply for distributional input and can use iid samples, so provide sample complexity bounds, and work for a variety of loss functions. A key tool we develop is a Radamacher complexity version of the main sensitivity sampling approach, which can be of independent interest.
Safety Filters for Black-Box Dynamical Systems by Learning Discriminating Hyperplanes
Authors: Will Lavanakul, Jason J. Choi, Koushil Sreenath, Claire J. Tomlin
Date: 2024-02-07 00:00:00
Tasks: Reinforcement Learning (RL)

Learning-based approaches are emerging as an effective approach for safety filters for black-box dynamical systems. Existing methods have relied on certificate functions like Control Barrier Functions (CBFs) and Hamilton-Jacobi (HJ) reachability value functions. The primary motivation for our work is the recognition that ultimately, enforcing the safety constraint as a control input constraint at each state is what matters. By focusing on this constraint, we can eliminate dependence on any specific certificate function-based design. To achieve this, we define a discriminating hyperplane that shapes the half-space constraint on control input at each state, serving as a sufficient condition for safety. This concept not only generalizes over traditional safety methods but also simplifies safety filter design by eliminating dependence on specific certificate functions. We present two strategies to learn the discriminating hyperplane: (a) a supervised learning approach, using pre-verified control invariant sets for labeling, and (b) a reinforcement learning (RL) approach, which does not require such labels. The main advantage of our method, unlike conventional safe RL approaches, is the separation of performance and safety. This offers a reusable safety filter for learning new tasks, avoiding the need to retrain from scratch. As such, we believe that the new notion of the discriminating hyperplane offers a more generalizable direction towards designing safety filters, encompassing and extending existing certificate-function-based or safe RL methodologies.