OpenRaiser/Intern-Atlas huggingface dataset:
Personalized Binomial DAGs Learning with Network Structured Covariates
Venue: arXiv cs.LG 2024
Year: 2024

The causal dependence in data is often characterized by Directed Acyclic Graphical (DAG) models, widely used in many areas. Causal discovery aims to recover the DAG structure using observational data. This paper focuses on causal discovery with multi-variate count data. We are motivated by real-world web visit data, recording individual user visits to multiple websites. Building a causal diagram can help understand user behavior in transitioning between websites, inspiring operational strategy. A challenge in modeling is user heterogeneity, as users with different backgrounds exhibit varied behaviors. Additionally, social network connections can result in similar behaviors among friends. We introduce personalized Binomial DAG models to address heterogeneity and network dependency between observations, which are common in real-world applications. To learn the proposed DAG model, we develop an algorithm that embeds the network structure into a dimension-reduced covariate, learns each node's neighborhood to reduce the DAG search space, and explores the variance-mean relation to determine the ordering. Simulations show our algorithm outperforms state-of-the-art competitors in heterogeneous data. We demonstrate its practical usefulness on a real-world web visit dataset.
FLUX: Fast Software-based Communication Overlap On GPUs Through Kernel Fusion
Venue: arXiv cs.LG 2024
Year: 2024

Large deep learning models have demonstrated strong ability to solve many tasks across a wide range of applications. Those large models typically require training and inference to be distributed. Tensor parallelism is a common technique partitioning computation of an operation or layer across devices to overcome the memory capacity limitation of a single processor, and/or to accelerate computation to meet a certain latency requirement. However, this kind of parallelism introduces additional communication that might contribute a significant portion of overall runtime. Thus limits scalability of this technique within a group of devices with high speed interconnects, such as GPUs with NVLinks in a node. This paper proposes a novel method, Flux, to significantly hide communication latencies with dependent computations for GPUs. Flux over-decomposes communication and computation operations into much finer-grained operations and further fuses them into a larger kernel to effectively hide communication without compromising kernel efficiency. Flux can potentially overlap up to 96% of communication given a fused kernel. Overall, it can achieve up to 1.24x speedups for training over Megatron-LM on a cluster of 128 GPUs with various GPU generations and interconnects, and up to 1.66x and 1.30x speedups for prefill and decoding inference over vLLM on a cluster with 8 GPUs with various GPU generations and interconnects.
Nonlinear time-series embedding by monotone variational inequality
Venue: arXiv cs.LG 2024
Year: 2024

In the wild, we often encounter collections of sequential data such as electrocardiograms, motion capture, genomes, and natural language, and sequences may be multichannel or symbolic with nonlinear dynamics. We introduce a new method to learn low-dimensional representations of nonlinear time series without supervision and can have provable recovery guarantees. The learned representation can be used for downstream machine-learning tasks such as clustering and classification. The method is based on the assumption that the observed sequences arise from a common domain, but each sequence obeys its own autoregressive models that are related to each other through low-rank regularization. We cast the problem as a computationally efficient convex matrix parameter recovery problem using monotone Variational Inequality and encode the common domain assumption via low-rank constraint across the learned representations, which can learn the geometry for the entire domain as well as faithful representations for the dynamics of each individual sequence using the domain information in totality. We show the competitive performance of our method on real-world time-series data with the baselines and demonstrate its effectiveness for symbolic text modeling and RNA sequence clustering.
On the Communication Complexity of Secure Multi-Party Computation With Aborts
Venue: arXiv cs.CR 2024
Year: 2024

A central goal of cryptography is Secure Multi-party Computation (MPC), where $n$ parties desire to compute a function of their joint inputs without letting any party learn about the inputs of its peers. Unfortunately, it is well-known that MPC guaranteeing output delivery to every party is infeasible when a majority of the parties are malicious. In fact, parties operating over a point-to-point network (i.e. without access to a broadcast channel) cannot even reach an agreement on the output when more than one third of the parties are malicious (Lamport, Shostak, and Pease, JACM 1980). Motivated by this infeasibility in the point-to-point model, Goldwasser and Lindell (J. Cryptol 2005) introduced a definition of MPC that does not require agreement, referred to as MPC with selective abort. Under this definition, any party may abort the protocol if they detect malicious behavior. They showed that MPC with selective abort is feasible for any number of malicious parties by implementing a broadcast functionality with abort. While the model of MPC with abort has attracted much attention over the years, little is known about its communication complexity over point-to-point networks. In this work, we study the communication complexity of MPC with abort and devise nearly-optimal communication efficient protocols in this model. Namely, we prove trade-offs between the number of honest parties $h$, the communication complexity, and the locality of the protocols. Here, locality is a bound on the number of peers with which each party must communicate.
Sparse Bayesian Networks: Efficient Uncertainty Quantification in Medical Image Analysis
Venue: arXiv cs.CV 2024
Year: 2024

Efficiently quantifying predictive uncertainty in medical images remains a challenge. While Bayesian neural networks (BNN) offer predictive uncertainty, they require substantial computational resources to train. Although Bayesian approximations such as ensembles have shown promise, they still suffer from high training and inference costs. Existing approaches mainly address the costs of BNN inference post-training, with little focus on improving training efficiency and reducing parameter complexity. This study introduces a training procedure for a sparse (partial) Bayesian network. Our method selectively assigns a subset of parameters as Bayesian by assessing their deterministic saliency through gradient sensitivity analysis. The resulting network combines deterministic and Bayesian parameters, exploiting the advantages of both representations to achieve high task-specific performance and minimize predictive uncertainty. Demonstrated on multi-label ChestMNIST for classification and ISIC, LIDC-IDRI for segmentation, our approach achieves competitive performance and predictive uncertainty estimation by reducing Bayesian parameters by over 95\%, significantly reducing computational expenses compared to fully Bayesian and ensemble methods.
CAAP: Context-Aware Action Planning Prompting to Solve Computer Tasks with Front-End UI Only
Venue: arXiv cs.AI 2024
Year: 2024

Software robots have long been used in Robotic Process Automation (RPA) to automate mundane and repetitive computer tasks. With the advent of Large Language Models (LLMs) and their advanced reasoning capabilities, these agents are now able to handle more complex or previously unseen tasks. However, LLM-based automation techniques in recent literature frequently rely on HTML source code for input or application-specific API calls for actions, limiting their applicability to specific environments. We propose an LLM-based agent that mimics human behavior in solving computer tasks. It perceives its environment solely through screenshot images, which are then converted into text for an LLM to process. By leveraging the reasoning capability of the LLM, we eliminate the need for large-scale human demonstration data typically required for model training. The agent only executes keyboard and mouse operations on Graphical User Interface (GUI), removing the need for pre-provided APIs to function. To further enhance the agent's performance in this setting, we propose a novel prompting strategy called Context-Aware Action Planning (CAAP) prompting, which enables the agent to thoroughly examine the task context from multiple perspectives. Our agent achieves an average success rate of 94.5% on MiniWoB++ and an average task score of 62.3 on WebShop, outperforming all previous studies of agents that rely solely on screen images. This method demonstrates potential for broader applications, particularly for tasks requiring coordination across multiple applications on desktops or smartphones, marking a significant advancement in the field of automation agents. Codes and models are accessible at https://github.com/caap-agent/caap-agent.
Triple-domain Feature Learning with Frequency-aware Memory Enhancement for Moving Infrared Small Target Detection
Venue: arXiv cs.CV 2024
Year: 2024

As a sub-field of object detection, moving infrared small target detection presents significant challenges due to tiny target sizes and low contrast against backgrounds. Currently-existing methods primarily rely on the features extracted only from spatio-temporal domain. Frequency domain has hardly been concerned yet, although it has been widely applied in image processing. To extend feature source domains and enhance feature representation, we propose a new Triple-domain Strategy (Tridos) with the frequency-aware memory enhancement on spatio-temporal domain for infrared small target detection. In this scheme, it effectively detaches and enhances frequency features by a local-global frequency-aware module with Fourier transform. Inspired by human visual system, our memory enhancement is designed to capture the spatial relations of infrared targets among video frames. Furthermore, it encodes temporal dynamics motion features via differential learning and residual enhancing. Additionally, we further design a residual compensation to reconcile possible cross-domain feature mismatches. To our best knowledge, proposed Tridos is the first work to explore infrared target feature learning comprehensively in spatio-temporal-frequency domains. The extensive experiments on three datasets (i.e., DAUB, ITSDT-15K and IRDST) validate that our triple-domain infrared feature learning scheme could often be obviously superior to state-of-the-art ones. Source codes are available at https://github.com/UESTC-nnLab/Tridos.
Distributional MIPLIB: a Multi-Domain Library for Advancing ML-Guided MILP Methods
Venue: arXiv cs.LG 2024
Year: 2024

Mixed Integer Linear Programming (MILP) is a fundamental tool for modeling combinatorial optimization problems. Recently, a growing body of research has used machine learning to accelerate MILP solving. Despite the increasing popularity of this approach, there is a lack of a common repository that provides distributions of similar MILP instances across different domains, at different hardness levels, with standardized test sets. In this paper, we introduce Distributional MIPLIB, a multi-domain library of problem distributions for advancing ML-guided MILP methods. We curate MILP distributions from existing work in this area as well as real-world problems that have not been used, and classify them into different hardness levels. It will facilitate research in this area by enabling comprehensive evaluation on diverse and realistic domains. We empirically illustrate the benefits of using Distributional MIPLIB as a research vehicle in two ways. We evaluate the performance of ML-guided variable branching on previously unused distributions to identify potential areas for improvement. Moreover, we propose to learn branching policies from a mix of distributions, demonstrating that mixed distributions achieve better performance compared to homogeneous distributions when there is limited data and generalize well to larger instances. The dataset is publicly available at https://sites.google.com/usc.edu/distributional-miplib/home.
TraceMesh: Scalable and Streaming Sampling for Distributed Traces
Venue: arXiv cs.DC 2024
Year: 2024

Distributed tracing serves as a fundamental element in the monitoring of cloud-based and datacenter systems. It provides visibility into the full lifecycle of a request or operation across multiple services, which is essential for understanding system dependencies and performance bottlenecks. To mitigate computational and storage overheads, most tracing frameworks adopt a uniform sampling strategy, which inevitably captures overlapping and redundant information. More advanced methods employ learning-based approaches to bias the sampling toward more informative traces. However, existing methods fall short of considering the high-dimensional and dynamic nature of trace data, which is essential for the production deployment of trace sampling. To address these practical challenges, in this paper we present TraceMesh, a scalable and streaming sampler for distributed traces. TraceMesh employs Locality-Sensitivity Hashing (LSH) to improve sampling efficiency by projecting traces into a low-dimensional space while preserving their similarity. In this process, TraceMesh accommodates previously unseen trace features in a unified and streamlined way. Subsequently, TraceMesh samples traces through evolving clustering, which dynamically adjusts the sampling decision to avoid over-sampling of recurring traces. The proposed method is evaluated with trace data collected from both open-source microservice benchmarks and production service systems. Experimental results demonstrate that TraceMesh outperforms state-of-the-art methods by a significant margin in both sampling accuracy and efficiency.
DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach
Venue: arXiv cs.LG 2024
Year: 2024

The rapid advancement of Artificial Intelligence (AI) has introduced Deep Neural Network (DNN)-based tasks to the ecosystem of vehicular networks. These tasks are often computation-intensive, requiring substantial computation resources, which are beyond the capability of a single vehicle. To address this challenge, Vehicular Edge Computing (VEC) has emerged as a solution, offering computing services for DNN-based tasks through resource pooling via Vehicle-to-Vehicle/Infrastructure (V2V/V2I) communications. In this paper, we formulate the problem of joint DNN partitioning, task offloading, and resource allocation in VEC as a dynamic long-term optimization. Our objective is to minimize the DNN-based task completion time while guaranteeing the system stability over time. To this end, we first leverage a Lyapunov optimization technique to decouple the original long-term optimization with stability constraints into a per-slot deterministic problem. Afterwards, we propose a Multi-Agent Diffusion-based Deep Reinforcement Learning (MAD2RL) algorithm, incorporating the innovative use of diffusion models to determine the optimal DNN partitioning and task offloading decisions. Furthermore, we integrate convex optimization techniques into MAD2RL as a subroutine to allocate computation resources, enhancing the learning efficiency. Through simulations under real-world movement traces of vehicles, we demonstrate the superior performance of our proposed algorithm compared to existing benchmark solutions.
Position Paper: Technical Research and Talent is Needed for Effective AI Governance
Venue: arXiv cs.CY 2024
Year: 2024

In light of recent advancements in AI capabilities and the increasingly widespread integration of AI systems into society, governments worldwide are actively seeking to mitigate the potential harms and risks associated with these technologies through regulation and other governance tools. However, there exist significant gaps between governance aspirations and the current state of the technical tooling necessary for their realisation. In this position paper, we survey policy documents published by public-sector institutions in the EU, US, and China to highlight specific areas of disconnect between the technical requirements necessary for enacting proposed policy actions, and the current technical state of the art. Our analysis motivates a call for tighter integration of the AI/ML research community within AI governance in order to i) catalyse technical research aimed at bridging the gap between current and supposed technical underpinnings of regulatory action, as well as ii) increase the level of technical expertise within governing institutions so as to inform and guide effective governance of AI.
Breaking Free: Efficient Multi-Party Private Set Union Without Non-Collusion Assumptions
Venue: arXiv cs.CR 2024
Year: 2024

Multi-party private set union (MPSU) protocol enables $m$ $(m > 2)$ parties, each holding a set, to collectively compute the union of their sets without revealing any additional information to other parties. There are two main categories of multi-party private set union (MPSU) protocols: The first category builds on public-key techniques, where existing works require a super-linear number of public-key operations, resulting in their poor practical efficiency. The second category builds on oblivious transfer and symmetric-key techniques. The only work in this category, proposed by Liu and Gao (ASIACRYPT 2023), features the best concrete performance among all existing protocols, but still has super-linear computation and communication. Moreover, it does not achieve the standard semi-honest security, as it inherently relies on a non-collusion assumption, which is unlikely to hold in practice. There remains two significant open problems so far: no MPSU protocol achieves semi-honest security based on oblivious transfer and symmetric-key techniques, and no MPSU protocol achieves both linear computation and linear communication complexity. In this work, we resolve both of them. - We propose the first MPSU protocol based on oblivious transfer and symmetric-key techniques in the standard semi-honest model. This protocol is $3.9-10.0 \times$ faster than Liu and Gao in the LAN setting. Concretely, our protocol requires only $4.4$ seconds in online phase for 3 parties with sets of $2^{20}$ items each. - We propose the first MPSU protocol achieving both linear computation and linear communication complexity, based on public-key operations. This protocol has the lowest overall communication costs and shows a factor of $3.0-36.5\times$ improvement in terms of overall communication compared to Liu and Gao.
DARA: Decomposition-Alignment-Reasoning Autonomous Language Agent for Question Answering over Knowledge Graphs
Venue: arXiv cs.CL 2024
Year: 2024

Answering Questions over Knowledge Graphs (KGQA) is key to well-functioning autonomous language agents in various real-life applications. To improve the neural-symbolic reasoning capabilities of language agents powered by Large Language Models (LLMs) in KGQA, we propose the DecompositionAlignment-Reasoning Agent (DARA) framework. DARA effectively parses questions into formal queries through a dual mechanism: high-level iterative task decomposition and low-level task grounding. Importantly, DARA can be efficiently trained with a small number of high-quality reasoning trajectories. Our experimental results demonstrate that DARA fine-tuned on LLMs (e.g. Llama-2-7B, Mistral) outperforms both in-context learning-based agents with GPT-4 and alternative fine-tuned agents, across different benchmarks in zero-shot evaluation, making such models more accessible for real-life applications. We also show that DARA attains performance comparable to state-of-the-art enumerating-and-ranking-based methods for KGQA.
The Treatment of Ties in Rank-Biased Overlap
Venue: arXiv cs.IR 2024
Year: 2024

Rank-Biased Overlap (RBO) is a similarity measure for indefinite rankings: it is top-weighted, and can be computed when only a prefix of the rankings is known or when they have only some items in common. It is widely used for instance to analyze differences between search engines by comparing the rankings of documents they retrieve for the same queries. In these situations, though, it is very frequent to find tied documents that have the same score. Unfortunately, the treatment of ties in RBO remains superficial and incomplete, in the sense that it is not clear how to calculate it from the ranking prefixes only. In addition, the existing way of dealing with ties is very different from the one traditionally followed in the field of Statistics, most notably found in rank correlation coefficients such as Kendall's and Spearman's. In this paper we propose a generalized formulation for RBO to handle ties, thanks to which we complete the original definitions by showing how to perform prefix evaluation. We also use it to fully develop two variants that align with the ones found in the Statistics literature: one when there is a reference ranking to compare to, and one when there is not. Overall, these three variants provide researchers with flexibility when comparing rankings with RBO, by clearly determining what ties mean, and how they should be treated. Finally, using both synthetic and TREC data, we demonstrate the use of these new tie-aware RBO measures. We show that the scores may differ substantially from the original tie-unaware RBO measure, where ties had to be broken at random or by arbitrary criteria such as by document ID. Overall, these results evidence the need for a proper account of ties in rank similarity measures such as RBO.
EEG classification for visual brain decoding with spatio-temporal and transformer based paradigms
Venue: arXiv cs.HC 2024
Year: 2024

In this work, we delve into the EEG classification task in the domain of visual brain decoding via two frameworks, involving two different learning paradigms. Considering the spatio-temporal nature of EEG data, one of our frameworks is based on a CNN-BiLSTM model. The other involves a CNN-Transformer architecture which inherently involves the more versatile attention based learning paradigm. In both cases, a special 1D-CNN feature extraction module is used to generate the initial embeddings with 1D convolutions in the time and the EEG channel domains. Considering the EEG signals are noisy, non stationary and the discriminative features are even less clear (than in semantically structured data such as text or image), we also follow a window-based classification followed by majority voting during inference, to yield labels at a signal level. To illustrate how brain patterns correlate with different image classes, we visualize t-SNE plots of the BiLSTM embeddings alongside brain activation maps for the top 10 classes. These visualizations provide insightful revelations into the distinct neural signatures associated with each visual category, showcasing the BiLSTM's capability to capture and represent the discriminative brain activity linked to visual stimuli. We demonstrate the performance of our approach on the updated EEG-Imagenet dataset with positive comparisons with state-of-the-art methods.