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DPPs have been successfully employed in Machine Learning applications to select a diverse, yet representative subset of data. In these applications, a set of parameters that maximize the likelihood of the data is typically desirable. The algorithms used for this task to date either optimize over a limited family","authors":["Grigorescu, Elena","Juba, Brendan","Wimmer, Karl","Xie, Ning"],"url":"https://arxiv.org/abs/2205.12377","arxiv_id":"2205.12377","published":"2026-02-27"},{"title":"VSRQ: Quantitative Assessment Method for Safety Risk of Vehicle Intelligent Connected System","abstract":"The field of intelligent connected in modern vehicles continues to expand, and the functions of vehicles become more and more complex with the development of the times. This has also led to an increasing number of vehicle vulnerabilities and many safety issues. Therefore, it is particularly important to identify high-risk vehicle intelligent connected systems, because it can inform security personnel which systems ar","authors":["Zhang, Tian","Guan, Wenshan","Miao, Hao","Huang, Xiujie","Liu, Zhiquan","Wang, Chaonan"],"url":"https://arxiv.org/abs/2305.01898","arxiv_id":"2305.01898","published":"2026-02-27"},{"title":"Online Inventory Problems: Beyond the i.i.d. Setting with Online Convex Optimization","abstract":"We study multi-product inventory control problems where a manager makes sequential replenishment decisions based on partial historical information in order to minimize its cumulative losses. Our motivation is to consider general demands, losses and dynamics to go beyond standard models which usually rely on newsvendor-type losses, fixed dynamics, and unrealistic i.i.d. demand assumptions. We propose MaxCOSD, an onlin","authors":["Hihat, Massil","Gaïffas, Stéphane","Garrigos, Guillaume","Bussy, Simon"],"url":"https://arxiv.org/abs/2307.06048","arxiv_id":"2307.06048","published":"2026-02-27"},{"title":"BSDM: Background Suppression Diffusion Model for Hyperspectral Anomaly Detection","abstract":"Hyperspectral anomaly detection (HAD) is widely used in Earth observation and deep space exploration. A major challenge for HAD is the complex background of the input hyperspectral images (HSIs), resulting in anomalies confused in the background. On the other hand, the lack of labeled samples for HSIs leads to poor generalization of existing HAD methods. This paper starts the first attempt to study a new and generali","authors":["Ma, Jitao","Xie, Weiying","Li, Yunsong","Fang, Leyuan"],"url":"https://arxiv.org/abs/2307.09861","arxiv_id":"2307.09861","published":"2026-02-27"},{"title":"Entropic Matching for Expectation Propagation of Markov Jump Processes","abstract":"We propose a novel, tractable latent state inference scheme for Markov jump processes, for which exact inference is often intractable. Our approach is based on an entropic matching framework that can be embedded into the well-known expectation propagation algorithm. We demonstrate the effectiveness of our method by providing closed-form results for a simple family of approximate distributions and apply it to the gene","authors":["Eich, Yannick","Alt, Bastian","Koeppl, Heinz"],"url":"https://arxiv.org/abs/2309.15604","arxiv_id":"2309.15604","published":"2026-02-27"},{"title":"Differentiable Particle Filtering using Optimal Placement Resampling","abstract":"Particle filters are a frequent choice for inference tasks in nonlinear and non-Gaussian state-space models. They can either be used for state inference by approximating the filtering distribution or for parameter inference by approximating the marginal data (observation) likelihood. A good proposal distribution and a good resampling scheme are crucial to obtain low variance estimates. However, traditional methods li","authors":["Csuzdi, Domonkos","Törő, Olivér","Bécsi, Tamás"],"url":"https://arxiv.org/abs/2402.16639","arxiv_id":"2402.16639","published":"2026-02-27"},{"title":"Not All Attention is Needed: Parameter and Computation Efficient Transfer Learning for Multi-modal Large Language Models","abstract":"In this paper, we propose a novel parameter and computation efficient tuning method for Multi-modal Large Language Models (MLLMs), termed Efficient Attention Skipping (EAS). Concretely, we first reveal that multi-head attentions (MHAs), the main computational overhead of MLLMs, are often redundant to downstream tasks. Based on this observation, EAS evaluates the attention redundancy and skips the less important MHAs ","authors":["Wu, Qiong","Ye, Weihao","Zhou, Yiyi","Sun, Xiaoshuai","Ji, Rongrong"],"url":"https://arxiv.org/abs/2403.15226","arxiv_id":"2403.15226","published":"2026-02-27"},{"title":"Procedural Fairness in Machine Learning","abstract":"Fairness in machine learning (ML) has garnered significant attention. However, current research has mainly concentrated on the distributive fairness of ML models, with limited focus on another dimension of fairness, i.e., procedural fairness. In this paper, we first define the procedural fairness of ML models by drawing from the established understanding of procedural fairness in philosophy and psychology fields, and","authors":["Wang, Ziming","Huang, Changwu","Tang, Ke","Yao, Xin"],"url":"https://arxiv.org/abs/2404.01877","arxiv_id":"2404.01877","published":"2026-02-27"},{"title":"Approximation Error and Complexity Bounds for ReLU Networks on Low-Regular Function Spaces","abstract":"In this work, we consider the approximation of a large class of bounded functions, with minimal regularity assumptions, by ReLU neural networks. We show that the approximation error can be bounded from above by a quantity proportional to the uniform norm of the target function and inversely proportional to the product of network width and depth. We inherit this approximation error bound from Fourier features residual","authors":["Davis, Owen","Geraci, Gianluca","Motamed, Mohammad"],"url":"https://arxiv.org/abs/2405.06727","arxiv_id":"2405.06727","published":"2026-02-27"},{"title":"RLSF: Fine-tuning LLMs via Symbolic Feedback","abstract":"Large Language Models (LLMs) have transformed AI but often struggle with tasks that require domain-specific reasoning and logical alignment. Traditional fine-tuning methods do not leverage the vast amount of symbolic domain-knowledge available to us via symbolic reasoning tools (e.g., provers), and are further limited by sparse rewards and unreliable reward models. We introduce Reinforcement Learning via Symbolic Fee","authors":["Jha, Piyush","Jana, Prithwish","Suresh, Pranavkrishna","Arora, Arnav","Ganesh, Vijay"],"url":"https://arxiv.org/abs/2405.16661","arxiv_id":"2405.16661","published":"2026-02-27"},{"title":"Meta-Designing Quantum Experiments with Language Models","abstract":"Artificial Intelligence (AI) can solve complex scientific problems beyond human capabilities, but the resulting solutions offer little insight into the underlying physical principles. One prominent example is quantum physics, where computers can discover experiments for the generation of specific quantum states, but it is unclear how finding general design concepts can be automated. Here, we address this challenge by","authors":["Arlt, Sören","Duan, Haonan","Li, Felix","Xie, Sang Michael","Wu, Yuhuai","Krenn, Mario"],"url":"https://arxiv.org/abs/2406.02470","arxiv_id":"2406.02470","published":"2026-02-27"},{"title":"StableMaterials: Enhancing Diversity in Material Generation via Semi-Supervised Learning","abstract":"We introduce StableMaterials, a novel approach for generating photorealistic physical-based rendering (PBR) materials that integrate semi-supervised learning with Latent Diffusion Models (LDMs). Our method employs adversarial training to distill knowledge from existing large-scale image generation models, minimizing the reliance on annotated data and enhancing the diversity in generation. This distillation approach a","authors":["Vecchio, Giuseppe"],"url":"https://arxiv.org/abs/2406.09293","arxiv_id":"2406.09293","published":"2026-02-27"},{"title":"SGIFormer: Semantic-guided and Geometric-enhanced Interleaving Transformer for 3D Instance Segmentation","abstract":"In recent years, transformer-based models have exhibited considerable potential in point cloud instance segmentation. Despite the promising performance achieved by existing methods, they encounter challenges such as instance query initialization problems and excessive reliance on stacked layers, rendering them incompatible with large-scale 3D scenes. This paper introduces a novel method, named SGIFormer, for 3D insta","authors":["Yao, Lei","Wang, Yi","Liu, Moyun","Chau, Lap-Pui"],"url":"https://arxiv.org/abs/2407.11564","arxiv_id":"2407.11564","published":"2026-02-27"},{"title":"Parameter-Efficient Fine-Tuning for Continual Learning: A Neural Tangent Kernel Perspective","abstract":"Parameter-efficient fine-tuning for continual learning (PEFT-CL) has shown promise in adapting pre-trained models to sequential tasks while mitigating catastrophic forgetting problem. However, understanding the mechanisms that dictate continual performance in this paradigm remains elusive. To unravel this mystery, we undertake a rigorous analysis of PEFT-CL dynamics to derive relevant metrics for continual scenarios ","authors":["Liu, Jingren","Ji, Zhong","Yu, YunLong","Cao, Jiale","Pang, Yanwei","Han, Jungong"],"url":"https://arxiv.org/abs/2407.17120","arxiv_id":"2407.17120","published":"2026-02-27"},{"title":"Efficient Graph Coloring with Neural Networks: A Physics-Inspired Approach for Large Graphs","abstract":"Combinatorial optimization problems near algorithmic phase transitions represent a fundamental challenge for both classical algorithms and machine learning approaches. Among them, graph coloring stands as a prototypical constraint satisfaction problem exhibiting sharp dynamical and satisfiability thresholds. Here we introduce a physics-inspired neural framework that learns to solve large-scale graph coloring instance","authors":["Colantonio, Lorenzo","Cacioppo, Andrea","Scarpati, Federico","Angelini, Maria Chiara","Ricci-Tersenghi, Federico","Giagu, Stefano"],"url":"https://arxiv.org/abs/2408.01503","arxiv_id":"2408.01503","published":"2026-02-27"},{"title":"Impact of Comprehensive Data Preprocessing on Predictive Modelling of COVID-19 Mortality","abstract":"Accurate predictive models are crucial for analysing COVID-19 mortality trends. This study evaluates the impact of a custom data preprocessing pipeline on ten machine learning models predicting COVID-19 mortality using data from Our World in Data (OWID). Our pipeline differs from a standard preprocessing pipeline through four key steps. Firstly, it transforms weekly reported totals into daily updates, correcting repo","authors":["Das, Sangita","Maji, Subhrajyoti"],"url":"https://arxiv.org/abs/2408.08142","arxiv_id":"2408.08142","published":"2026-02-27"},{"title":"Evaluating the Evaluator: Measuring LLMs' Adherence to Task Evaluation Instructions","abstract":"LLMs-as-a-judge is a recently popularized method which replaces human judgements in task evaluation (Zheng et al. 2024) with automatic evaluation using LLMs. Due to widespread use of RLHF (Reinforcement Learning from Human Feedback), state-of-the-art LLMs like GPT4 and Llama3 are expected to have strong alignment with human preferences when prompted for a quality judgement, such as the coherence of a text. While this","authors":["Murugadoss, Bhuvanashree","Poelitz, Christian","Drosos, Ian","Le, Vu","McKenna, Nick","Negreanu, Carina Suzana"],"url":"https://arxiv.org/abs/2408.08781","arxiv_id":"2408.08781","published":"2026-02-27"},{"title":"Decision MetaMamba: Enhancing Selective SSM in Offline RL with Heterogeneous Sequence Mixing","abstract":"Mamba-based models have drawn much attention in offline RL. However, their selective mechanism often detrimental when key steps in RL sequences are omitted. To address these issues, we propose a simple yet effective structure, called Decision MetaMamba (DMM), which replaces Mamba's token mixer with a dense layer-based sequence mixer and modifies positional structure to preserve local information. By performing se","authors":["Kim, Wall","Song, Chaeyoung","Kim, Hanul"],"url":"https://arxiv.org/abs/2408.10517","arxiv_id":"2408.10517","published":"2026-02-27"},{"title":"Open-Set Deepfake Detection: A Parameter-Efficient Adaptation Method with Forgery Style Mixture","abstract":"Open-set face forgery detection poses significant security threats and presents substantial challenges for existing detection models. These detectors primarily have two limitations: they cannot generalize across unknown forgery domains and inefficiently adapt to new data. To address these issues, we introduce an approach that is both general and parameter-efficient for face forgery detection. It builds on the assumpt","authors":["Kong, Chenqi","Luo, Anwei","Bao, Peijun","Li, Haoliang","Wan, Renjie","Zheng, Zengwei"],"url":"https://arxiv.org/abs/2408.12791","arxiv_id":"2408.12791","published":"2026-02-27"},{"title":"Abstracted Gaussian Prototypes for True One-Shot Concept Learning","abstract":"We introduce a cluster-based generative image segmentation framework to encode higher-level representations of visual concepts based on one-shot learning inspired by the Omniglot Challenge. The inferred parameters of each component of a Gaussian Mixture Model (GMM) represent a distinct topological subpart of a visual concept. Sampling new data from these parameters generates augmented subparts to build a more robust ","authors":["Zou, Chelsea","Kurtz, Kenneth J."],"url":"https://arxiv.org/abs/2408.17251","arxiv_id":"2408.17251","published":"2026-02-27"},{"title":"On the Complexity of Neural Computation in Superposition","abstract":"Superposition, the ability of neural networks to represent more features than neurons, is increasingly seen as key to the efficiency of large models. This paper investigates the theoretical foundations of computing in superposition, establishing complexity bounds for explicit, provably correct algorithms. We present the first lower bounds for a neural network computing in superposition, showing that for a broad class","authors":["Adler, Micah","Shavit, Nir"],"url":"https://arxiv.org/abs/2409.15318","arxiv_id":"2409.15318","published":"2026-02-27"},{"title":"Beyond Attribution: Unified Concept-Level Explanations","abstract":"There is an increasing need to integrate model-agnostic explanation techniques with concept-based approaches, as the former can explain models across different architectures while the latter makes explanations more faithful and understandable to end-users. However, existing concept-based model-agnostic explanation methods are limited in scope, mainly focusing on attribution-based explanations while neglecting diverse","authors":["Liu, Junhao","Yu, Haonan","Zhang, Xin"],"url":"https://arxiv.org/abs/2410.12439","arxiv_id":"2410.12439","published":"2026-02-27"},{"title":"Testing the Efficacy of Hyperparameter Optimization Algorithms in Short-Term Load Forecasting","abstract":"Accurate forecasting of electrical demand is essential for maintaining a stable and reliable power grid, optimizing the allocation of energy resources, and promoting efficient energy consumption practices. This study investigates the effectiveness of five hyperparameter optimization (HPO) algorithms -- Random Search, Covariance Matrix Adaptation Evolution Strategy (CMA--ES), Bayesian Optimization, Partial Swarm Optim","authors":["Hakyemez, Tugrul Cabir","Adar, Omer"],"url":"https://arxiv.org/abs/2410.15047","arxiv_id":"2410.15047","published":"2026-02-27"},{"title":"Multi-view biomedical foundation models for molecule-target and property prediction","abstract":"Quality molecular representations are key to foundation model development in bio-medical research. Previous efforts have typically focused on a single representation or molecular view, which may have strengths or weaknesses on a given task. We develop Multi-view Molecular Embedding with Late Fusion (MMELON), an approach that integrates graph, image and text views in a foundation model setting and may be readily exten","authors":["Suryanarayanan, Parthasarathy","Qiu, Yunguang","Sethi, Shreyans","Mahajan, Diwakar","Li, Hongyang","Yang, Yuxin"],"url":"https://arxiv.org/abs/2410.19704","arxiv_id":"2410.19704","published":"2026-02-27"},{"title":"Toward Automated Validation of Language Model Synthesized Test Cases using Semantic Entropy","abstract":"Modern Large Language Model (LLM)-based programming agents often rely on test execution feedback to refine their generated code. These tests are synthetically generated by LLMs. However, LLMs may produce invalid or hallucinated test cases, which can mislead feedback loops and degrade the performance of agents in refining and improving code. This paper introduces VALTEST, a novel framework that leverages semantic entr","authors":["Taherkhani, Hamed","Shin, Jiho","Tahir, Muhammad Ammar","Misu, Md Rakib Hossain","Gattani, Vineet Sunil","Hemmati, Hadi"],"url":"https://arxiv.org/abs/2411.08254","arxiv_id":"2411.08254","published":"2026-02-27"}],"history":{"events":[{"year":2015,"text":"Russian statesman and politician Boris Nemtsov, an outspoken critic of Vladimir Putin, was assassinated in central Moscow.","url":"https://en.wikipedia.org/wiki/Boris_Nemtsov"},{"year":2002,"text":"Violent riots, perceived to have been instigated by a train fire that killed 59 Hindu pilgrims, broke out in the Indian state of Gujarat, killing at least 1,000 people, mostly Muslims, over three days.","url":"https://en.wikipedia.org/wiki/2002_Gujarat_violence"},{"year":1996,"text":"The multimedia franchise Pokémon was launched with the release of the video games Pocket Monsters Red and Green.","url":"https://en.wikipedia.org/wiki/Pok%C3%A9mon"},{"year":1988,"text":"The Armenian community of Sumgait in Azerbaijan was the target of a violent pogrom (memorial pictured).","url":"https://en.wikipedia.org/wiki/Sumgait"},{"year":1982,"text":"The D'Oyly Carte Opera Company, known for its performances of Gilbert and Sullivan's Savoy operas, gave its final performance.","url":"https://en.wikipedia.org/wiki/D'Oyly_Carte_Opera_Company"},{"year":1972,"text":"The Sunday People revealed that James Humphreys, a notorious pornographer, had bribed a senior London police officer with a holiday to Cyprus and Beirut.","url":"https://en.wikipedia.org/wiki/Sunday_People"},{"year":1962,"text":"Two dissident Republic of Vietnam Air Force pilots bombed the Independence Palace in Saigon in a failed attempt to assassinate President Ngo Dinh Diem.","url":"https://en.wikipedia.org/wiki/South_Vietnam_Air_Force"},{"year":1940,"text":"American biochemists Martin Kamen and Sam Ruben discovered carbon-14, a radioactive isotope of carbon, which is now used extensively as the basis of the radiocarbon dating method to date archaeological and geological samples.","url":"https://en.wikipedia.org/wiki/Martin_Kamen"},{"year":1933,"text":"The Reichstag building, the seat of the German parliament in Berlin, was set on fire in a pivotal event in the establishment of the Nazi regime.","url":"https://en.wikipedia.org/wiki/Reichstag_building"},{"year":1900,"text":"FC Bayern Munich, Germany's most successful football club, was founded.","url":"https://en.wikipedia.org/wiki/FC_Bayern_Munich"},{"year":1870,"text":"The current flag of Japan was first adopted as a civil ensign for Japanese merchant ships.","url":"https://en.wikipedia.org/wiki/Flag_of_Japan"},{"year":1814,"text":"Peninsular War: In the south of France, Spanish, British and Portuguese soldiers under the command of the Marquess of Wellington defeated French soldiers in the Battle of Orthez, causing the French to retreat east.","url":"https://en.wikipedia.org/wiki/Peninsular_War"},{"year":1776,"text":"American Revolutionary War: A Patriot victory at the Battle of Moore's Creek Bridge resulted in the arrests of 850 Loyalists over the following days.","url":"https://en.wikipedia.org/wiki/American_Revolutionary_War"},{"year":1617,"text":"Sweden signed the Treaty of Stolbovo to end the Ingrian War with Russia, making large territorial gains.","url":"https://en.wikipedia.org/wiki/Treaty_of_Stolbovo"},{"year":1560,"text":"The Treaty of Berwick was signed, setting the terms under which an English fleet and army could enter Scotland to expel French troops defending the regency of Mary of Guise (pictured).","url":"https://en.wikipedia.org/wiki/Treaty_of_Berwick_(1560)"}],"births":[{"year":2002,"text":"Johnny Davis, American basketball player","url":"https://en.wikipedia.org/wiki/Johnny_Davis_(basketball%2C_born_2002)"},{"year":1998,"text":"Todd Cantwell, English footballer","url":"https://en.wikipedia.org/wiki/Todd_Cantwell"},{"year":1996,"text":"Chris Godwin, American football player","url":"https://en.wikipedia.org/wiki/Chris_Godwin"},{"year":1996,"text":"Ten, Thai singer and dancer","url":"https://en.wikipedia.org/wiki/Ten_(singer)"},{"year":1995,"text":"Laura Gulbe, Latvian tennis player","url":"https://en.wikipedia.org/wiki/Laura_Gulbe"},{"year":1995,"text":"Sergej Milinković-Savić, Serbian footballer","url":"https://en.wikipedia.org/wiki/Sergej_Milinkovi%C4%87-Savi%C4%87"},{"year":1995,"text":"Tomáš Souček, Czech footballer","url":"https://en.wikipedia.org/wiki/Tom%C3%A1%C5%A1_Sou%C4%8Dek"},{"year":1994,"text":"Mike Matheson, Canadian ice hockey player","url":"https://en.wikipedia.org/wiki/Mike_Matheson"}],"deaths":[{"year":2025,"text":"Boris Spassky, Russian chess grandmaster (born 1937)","url":"https://en.wikipedia.org/wiki/Boris_Spassky"},{"year":2023,"text":"Gérard Latortue, Haitian prime minister (born 1934)","url":"https://en.wikipedia.org/wiki/G%C3%A9rard_Latortue"},{"year":2021,"text":"Ng Man-tat, Hong Kong actor (born 1952)","url":"https://en.wikipedia.org/wiki/Ng_Man-tat"},{"year":2019,"text":"France-Albert René, Seychellois politician, 2nd President of Seychelles (born 1935)","url":"https://en.wikipedia.org/wiki/France-Albert_Ren%C3%A9"},{"year":2018,"text":"Steve Folkes, Australian rugby league player and coach (born 1959)","url":"https://en.wikipedia.org/wiki/Steve_Folkes"},{"year":2016,"text":"Yi Cheol-seung, South Korean lawyer and politician (born 1922)","url":"https://en.wikipedia.org/wiki/Lee_Cheol-seung"},{"year":2016,"text":"James Z. Davis, American lawyer and judge (born 1943)","url":"https://en.wikipedia.org/wiki/James_Z._Davis"},{"year":2015,"text":"Boris Nemtsov, Russian academic and politician, First Deputy Prime Minister of Russia (born 1959)","url":"https://en.wikipedia.org/wiki/Boris_Nemtsov"}]},"census":null,"sources":[{"id":"attention","name":"Wikipedia most-read","url":"https://api.wikimedia.org","licence":"CC BY-SA 4.0","attribution":"Wikipedia contributors"},{"id":"frontier","name":"Hacker News","url":"https://news.ycombinator.com","licence":"Public API — titles and links, credited","attribution":"Hacker News / Y Combinator"},{"id":"research","name":"arXiv cs.AI","url":"https://arxiv.org","licence":"Open metadata, per arXiv API terms","attribution":"arXiv.org"},{"id":"history","name":"Wikipedia On this day","url":"https://api.wikimedia.org","licence":"CC BY-SA 4.0","attribution":"Wikipedia contributors"}],"licence":"Records are CC BY-SA 4.0, inheriting from Wikipedia. Every item links to its source."}