🎯 Top Personalized Recommendations
AI Summary
- Tighter regulation could lead to more significant price reductions and improved welfare outcomes. [3]
- The study examines the effect of the Anti-Manipulation Procedure (AMP) on electricity prices in two US markets: ISO-NE and NYISO. [2]
Abstract
In auction markets that are prone to market power abuse, preventive mitigation of bid prices can be applied through automated mitigation procedures (AMP). Despite the widespread application of AMP in US electricity markets, there exists scarce evidence on how firms strategically react to such price-cap-and-penalty regulation: when the price cap rarely leads to penalty mitigation, it is difficult to distinguish whether AMP are an effective deterrent or simply too lax. We investigate their impact on the bids of generation firms, using 2019 data from the New York and New England electricity markets (NYISO, ISO-NE). We employ a regression discontinuity design, which exploits the fact that the price cap with penalty is only activated when a structural index (e.g., congestion, pivotality) exceeds a certain cutoff. By estimating the Local Average Treatment Effect (LATE) of screening activation, we can causally identify successful deterrence of anti-competitive behavior. Around 30-40% of the analyzed bidders per market exhibit a significant strategic response - corresponding to a decrease in maximum bid prices of 4-10 $/MWh to avoid the penalty. However, there is significant heterogeneity between firms, and the regulatory impact on the overall market is not statistically detectable, suggesting lax mitigation thresholds. Using a merit-order simulation, we estimate the welfare impact of more stringent thresholds to lie between 350 and 980 thousand dollars of increased buyer surplus per mitigated hour, with the associated number of mitigated hours being below 33 hours/year. Our results motivate the empirical calibration of mitigation thresholds to improve the efficiency of AMP regulation.
In auction markets that are prone to market power abuse, preventive mitigation of bid prices can be applied through automated mitigation procedures (AMP). Despite the widespread application of AMP in US electricity markets, there exists scarce evidence on how firms strategically react to such price-cap-and-penalty regulation: when the price cap rarely leads to penalty mitigation, it is difficult to distinguish whether AMP are an effective deterrent or simply too lax. We investigate their impact on the bids of generation firms, using 2019 data from the New York and New England electricity markets (NYISO, ISO-NE). We employ a regression discontinuity design, which exploits the fact that the price cap with penalty is only activated when a structural index (e.g., congestion, pivotality) exceeds a certain cutoff. By estimating the Local Average Treatment Effect (LATE) of screening activation, we can causally identify successful deterrence of anti-competitive behavior. Around 30-40% of the analyzed bidders per market exhibit a significant strategic response - corresponding to a decrease in maximum bid prices of 4-10 $/MWh to avoid the penalty. However, there is significant heterogeneity between firms, and the regulatory impact on the overall market is not statistically detectable, suggesting lax mitigation thresholds. Using a merit-order simulation, we estimate the welfare impact of more stringent thresholds to lie between 350 and 980 thousand dollars of increased buyer surplus per mitigated hour, with the associated number of mitigated hours being below 33 hours/year. Our results motivate the empirical calibration of mitigation thresholds to improve the efficiency of AMP regulation.
Why we think this paper is great for you:
This paper directly addresses strategic bidding in auction markets, offering insights into how firms react to mitigation procedures. You will find its focus on strategic bid response highly relevant.
This paper directly addresses strategic bidding in auction markets, offering insights into how firms react to mitigation procedures. You will find its focus on strategic bid response highly relevant.
Abstract
This paper unifies two foundational constructs from economics and algorithmic game theory, the Arctic Auction and the linear Fisher market, to address the efficient allocation of differentiated goods in complex markets. Our main contributions are showing that an equilibrium for the Arctic Auction is captured by a Rational Convex Program, and deriving the first combinatorial polynomial-time algorithm for computing Arctic Auction equilibria.
This paper unifies two foundational constructs from economics and algorithmic game theory, the Arctic Auction and the linear Fisher market, to address the efficient allocation of differentiated goods in complex markets. Our main contributions are showing that an equilibrium for the Arctic Auction is captured by a Rational Convex Program, and deriving the first combinatorial polynomial-time algorithm for computing Arctic Auction equilibria.
Why we think this paper is great for you:
Delving into auction theory and efficient allocation in complex markets, this paper provides foundational knowledge. Its exploration of Arctic Auctions and Fisher markets aligns well with your interests.
Delving into auction theory and efficient allocation in complex markets, this paper provides foundational knowledge. Its exploration of Arctic Auctions and Fisher markets aligns well with your interests.
AI Summary
- The dataset is called PVIT (Personalized Visual Instruction Tuning) and consists of 100,000 image-concept pairs with corresponding questions and answers. [3]
- It also mentions recent advances in visual language understanding tasks such as image captioning, visual question answering, and visual reasoning. [3]
- The method uses a large dataset of images with multiple concepts and corresponding questions to test the computer's ability to identify and describe these concepts accurately. [3]
- The paper proposes a new dataset and evaluation framework for visual language models (VLMs) that can understand and describe concepts in images. [2]
Abstract
Personalized Visual Language Models (VLMs) are gaining increasing attention for their formidable ability in user-specific concepts aligned interactions (e.g., identifying a user's bike). Existing methods typically require the learning of separate embeddings for each new concept, which fails to support real-time adaptation during testing. This limitation becomes particularly pronounced in large-scale scenarios, where efficient retrieval of concept embeddings is not achievable. To alleviate this gap, we propose Online-PVLM, a framework for online concept learning by leveraging hyperbolic representations. Our approach makes a train-free paradigm for concept embeddings generation at test time, making the use of personalized VLMs both scalable and efficient. In addition, we develop OP-Eval, a comprehensive and large-scale benchmark comprising 1,292 concepts and over 30K high-quality instances with diverse question types, designed to rigorously assess online concept learning in realistic scenarios. Extensive experiments demonstrate the state-of-the-art performance of our proposed framework. Our source code and dataset will be made available.
Personalized Visual Language Models (VLMs) are gaining increasing attention for their formidable ability in user-specific concepts aligned interactions (e.g., identifying a user's bike). Existing methods typically require the learning of separate embeddings for each new concept, which fails to support real-time adaptation during testing. This limitation becomes particularly pronounced in large-scale scenarios, where efficient retrieval of concept embeddings is not achievable. To alleviate this gap, we propose Online-PVLM, a framework for online concept learning by leveraging hyperbolic representations. Our approach makes a train-free paradigm for concept embeddings generation at test time, making the use of personalized VLMs both scalable and efficient. In addition, we develop OP-Eval, a comprehensive and large-scale benchmark comprising 1,292 concepts and over 30K high-quality instances with diverse question types, designed to rigorously assess online concept learning in realistic scenarios. Extensive experiments demonstrate the state-of-the-art performance of our proposed framework. Our source code and dataset will be made available.
Why we think this paper is great for you:
This paper focuses on advancing personalization through user-specific concept learning in visual language models. It offers valuable insights into tailoring interactions to individual users.
This paper focuses on advancing personalization through user-specific concept learning in visual language models. It offers valuable insights into tailoring interactions to individual users.
AI Summary
- The DataSquad program has been highly effective in providing students with practical experience and skills in data science, software engineering, and project management. [3]
- The program's emphasis on teamwork, communication, and client interaction has helped students develop valuable soft skills. [3]
- The DataSquad environment is highly encouraging, with 100% of alumni reporting that they felt encouraged to participate in the program. [3]
- Statistical Analysis: Collecting, exploring and presenting large amounts of data to discover underlying patterns and trends Database Design/Cloud Systems: Designing a safe place to capture your data (in SQL or other), working with data capture or management tools like Qualtrics or Google Forms Coding, Software Engineering: Using programming languages, such as Python, R, etc., and utilizing file management tools like Git Project Management/Planning: Organizing tasks, managing time, and coordinating resources to achieve goals Effective Teamwork: Collaborating well with others, supporting teammates, and achieving shared objectives [3]
- Many students experienced multiple roles during their tenure, gaining breadth across the program's offerings. [2]
Abstract
The DataSquad at Carleton College addresses a common problem at small liberal arts colleges: limited capacity for data services and few opportunities for students to gain practical experience with data and software development. Academic Technologist Paula Lackie designed the program as a work-study position that trains undergraduates through structured peer mentorship and real client projects. Students tackle data problems of increasing complexity-from basic data analysis to software development-while learning FAIR data principles and open science practices. The model's core components (peer mentorship structure, project-based learning, and communication training) make it adaptable to other institutions. UCLA and other colleges have adopted the model using openly shared materials through "DataSquad International." This paper describes the program's implementation at Carleton College and examines how structured peer mentorship can simultaneously improve institutional data services and provide students with professional skills and confidence.
The DataSquad at Carleton College addresses a common problem at small liberal arts colleges: limited capacity for data services and few opportunities for students to gain practical experience with data and software development. Academic Technologist Paula Lackie designed the program as a work-study position that trains undergraduates through structured peer mentorship and real client projects. Students tackle data problems of increasing complexity-from basic data analysis to software development-while learning FAIR data principles and open science practices. The model's core components (peer mentorship structure, project-based learning, and communication training) make it adaptable to other institutions. UCLA and other colleges have adopted the model using openly shared materials through "DataSquad International." This paper describes the program's implementation at Carleton College and examines how structured peer mentorship can simultaneously improve institutional data services and provide students with professional skills and confidence.
Why we think this paper is great for you:
You will find valuable insights here regarding structuring data services and preparing data scientists for practical work. This directly addresses challenges in data science organizations and management.
You will find valuable insights here regarding structuring data services and preparing data scientists for practical work. This directly addresses challenges in data science organizations and management.
Abstract
The utility of an explanation method critically depends on its fidelity to the underlying machine learning model. Especially in high-stakes medical settings, clinicians and regulators require explanations that faithfully reflect the model's decision process. Existing fidelity metrics such as Infidelity rely on Monte Carlo approximation, which demands numerous model evaluations and introduces uncertainty due to random sampling. This work proposes a novel metric for evaluating the fidelity of local feature attribution methods by modifying the existing Prediction Change (PC) metric within the Guided Perturbation Experiment. By incorporating the direction of both perturbation and attribution, the proposed Directed Prediction Change (DPC) metric achieves an almost tenfold speedup and eliminates randomness, resulting in a deterministic and trustworthy evaluation procedure that measures the same property as local Infidelity. DPC is evaluated on two datasets (skin lesion images and financial tabular data), two black-box models, seven explanation algorithms, and a wide range of hyperparameters. Across $4\,744$ distinct explanations, the results demonstrate that DPC, together with PC, enables a holistic and computationally efficient evaluation of both baseline-oriented and local feature attribution methods, while providing deterministic and reproducible outcomes.
The utility of an explanation method critically depends on its fidelity to the underlying machine learning model. Especially in high-stakes medical settings, clinicians and regulators require explanations that faithfully reflect the model's decision process. Existing fidelity metrics such as Infidelity rely on Monte Carlo approximation, which demands numerous model evaluations and introduces uncertainty due to random sampling. This work proposes a novel metric for evaluating the fidelity of local feature attribution methods by modifying the existing Prediction Change (PC) metric within the Guided Perturbation Experiment. By incorporating the direction of both perturbation and attribution, the proposed Directed Prediction Change (DPC) metric achieves an almost tenfold speedup and eliminates randomness, resulting in a deterministic and trustworthy evaluation procedure that measures the same property as local Infidelity. DPC is evaluated on two datasets (skin lesion images and financial tabular data), two black-box models, seven explanation algorithms, and a wide range of hyperparameters. Across $4\,744$ distinct explanations, the results demonstrate that DPC, together with PC, enables a holistic and computationally efficient evaluation of both baseline-oriented and local feature attribution methods, while providing deterministic and reproducible outcomes.
Why we think this paper is great for you:
This paper focuses on assessing the fidelity of feature attribution methods, which is crucial for understanding model decisions. It provides a framework for ensuring trustworthy explanations in machine learning.
This paper focuses on assessing the fidelity of feature attribution methods, which is crucial for understanding model decisions. It provides a framework for ensuring trustworthy explanations in machine learning.
Abstract
Recommender systems shape how people discover information, form opinions, and connect with society. Yet, as their influence grows, traditional metrics, e.g., accuracy, clicks, and engagement, no longer capture what truly matters to humans. The workshop on Human-Centered Recommender Systems (HCRS) calls for a paradigm shift from optimizing engagement toward designing systems that truly understand, involve, and benefit people. It brings together researchers in recommender systems, human-computer interaction, AI safety, and social computing to explore how human values, e.g., trust, safety, fairness, transparency, and well-being, can be integrated into recommendation processes. Centered around three thematic axes-Human Understanding, Human Involvement, and Human Impact-HCRS features keynotes, panels, and papers covering topics from LLM-based interactive recommenders to societal welfare optimization. By fostering interdisciplinary collaboration, HCRS aims to shape the next decade of responsible and human-aligned recommendation research.
Recommender systems shape how people discover information, form opinions, and connect with society. Yet, as their influence grows, traditional metrics, e.g., accuracy, clicks, and engagement, no longer capture what truly matters to humans. The workshop on Human-Centered Recommender Systems (HCRS) calls for a paradigm shift from optimizing engagement toward designing systems that truly understand, involve, and benefit people. It brings together researchers in recommender systems, human-computer interaction, AI safety, and social computing to explore how human values, e.g., trust, safety, fairness, transparency, and well-being, can be integrated into recommendation processes. Centered around three thematic axes-Human Understanding, Human Involvement, and Human Impact-HCRS features keynotes, panels, and papers covering topics from LLM-based interactive recommenders to societal welfare optimization. By fostering interdisciplinary collaboration, HCRS aims to shape the next decade of responsible and human-aligned recommendation research.
Why we think this paper is great for you:
Exploring human-centered aspects of recommender systems, this paper discusses metrics beyond traditional accuracy, clicks, and engagement. It aligns with optimizing customer experiences and personalization.
Exploring human-centered aspects of recommender systems, this paper discusses metrics beyond traditional accuracy, clicks, and engagement. It aligns with optimizing customer experiences and personalization.
AI Summary
- The semiotic capacity framework is a novel, quantitative approach to Large Language Model (LLM)-mediated communication, analyzing the trade-off between expressive richness (semiotic breadth) and interpretive stability (decipherability). [3]
- Operationalizability is key: concrete measurement protocols are provided for estimating semiotic breadth, decipherability, and channel capacity. [3]
- Semiotics: The study of signs and symbols in communication. [3]
- Decipherability: The ability to understand or interpret a message accurately. [3]
- Semiotic breadth: The range of possible meanings that can be conveyed by a sign or symbol. [3]
- The framework relies on information theory to translate the qualitative trade-off into a measurable, falsifiable claim constrained by semiotic channel capacity. [2]
- Peirce: An American philosopher who developed a theory of signs and their relationship to meaning. [1]
Abstract
This paper proposes a novel semiotic framework for analyzing Large Language Models (LLMs), conceptualizing them as stochastic semiotic engines whose outputs demand active, asymmetric human interpretation. We formalize the trade-off between expressive richness (semiotic breadth) and interpretive stability (decipherability) using information-theoretic tools. Breadth is quantified as source entropy, and decipherability as the mutual information between messages and human interpretations. We introduce a generative complexity parameter (lambda) that governs this trade-off, as both breadth and decipherability are functions of lambda. The core trade-off is modeled as an emergent property of their distinct responses to $λ$. We define a semiotic channel, parameterized by audience and context, and posit a capacity constraint on meaning transmission, operationally defined as the maximum decipherability by optimizing lambda. This reframing shifts analysis from opaque model internals to observable textual artifacts, enabling empirical measurement of breadth and decipherability. We demonstrate the framework's utility across four key applications: (i) model profiling; (ii) optimizing prompt/context design; (iii) risk analysis based on ambiguity; and (iv) adaptive semiotic systems. We conclude that this capacity-based semiotic approach offers a rigorous, actionable toolkit for understanding, evaluating, and designing LLM-mediated communication.
This paper proposes a novel semiotic framework for analyzing Large Language Models (LLMs), conceptualizing them as stochastic semiotic engines whose outputs demand active, asymmetric human interpretation. We formalize the trade-off between expressive richness (semiotic breadth) and interpretive stability (decipherability) using information-theoretic tools. Breadth is quantified as source entropy, and decipherability as the mutual information between messages and human interpretations. We introduce a generative complexity parameter (lambda) that governs this trade-off, as both breadth and decipherability are functions of lambda. The core trade-off is modeled as an emergent property of their distinct responses to $λ$. We define a semiotic channel, parameterized by audience and context, and posit a capacity constraint on meaning transmission, operationally defined as the maximum decipherability by optimizing lambda. This reframing shifts analysis from opaque model internals to observable textual artifacts, enabling empirical measurement of breadth and decipherability. We demonstrate the framework's utility across four key applications: (i) model profiling; (ii) optimizing prompt/context design; (iii) risk analysis based on ambiguity; and (iv) adaptive semiotic systems. We conclude that this capacity-based semiotic approach offers a rigorous, actionable toolkit for understanding, evaluating, and designing LLM-mediated communication.
Why we think this paper is great for you:
This paper proposes a framework for analyzing Large Language Models and their capacity for meaning. Understanding LLM communication can be beneficial for applications in personalized interactions and marketing.
This paper proposes a framework for analyzing Large Language Models and their capacity for meaning. Understanding LLM communication can be beneficial for applications in personalized interactions and marketing.
Attribution
Abstract
Extreme event attribution (EEA), an approach for assessing the extent to which disasters are caused by climate change, is crucial for informing climate policy and legal proceedings. Machine learning is increasingly used for EEA by modeling rare weather events otherwise too complex or computationally intensive to model using traditional simulation methods. However, the validity of using machine learning in this context remains unclear, particularly as high-stakes machine learning applications in general are criticized for inherent bias and lack of robustness. Here we use machine learning and simulation analyses to evaluate EEA in the context of California wildfire data from 2003-2020. We identify three major threats to validity: (1) individual event attribution estimates are highly sensitive to algorithmic design choices; (2) common performance metrics like area under the ROC curve or Brier score are not strongly correlated with attribution error, facilitating suboptimal model selection; and (3) distribution shift -- changes in temperature across climate scenarios -- substantially degrades predictive performance. To address these challenges, we propose a more valid and robust attribution analysis based on aggregate machine learning estimates, using an additional metric -- mean calibration error -- to assess model performance, and using subgroup and propensity diagnostics to assess distribution shift.
Extreme event attribution (EEA), an approach for assessing the extent to which disasters are caused by climate change, is crucial for informing climate policy and legal proceedings. Machine learning is increasingly used for EEA by modeling rare weather events otherwise too complex or computationally intensive to model using traditional simulation methods. However, the validity of using machine learning in this context remains unclear, particularly as high-stakes machine learning applications in general are criticized for inherent bias and lack of robustness. Here we use machine learning and simulation analyses to evaluate EEA in the context of California wildfire data from 2003-2020. We identify three major threats to validity: (1) individual event attribution estimates are highly sensitive to algorithmic design choices; (2) common performance metrics like area under the ROC curve or Brier score are not strongly correlated with attribution error, facilitating suboptimal model selection; and (3) distribution shift -- changes in temperature across climate scenarios -- substantially degrades predictive performance. To address these challenges, we propose a more valid and robust attribution analysis based on aggregate machine learning estimates, using an additional metric -- mean calibration error -- to assess model performance, and using subgroup and propensity diagnostics to assess distribution shift.
AI Summary
- The accuracy of FAR estimates decreases with distribution shift, as demonstrated by regret calculations. [3]
- Prediction-powered inference (PPI) does not improve the accuracy of FAR estimates, even when using propensity score weighting. [3]
- Brier score and accuracy of the risk ratio are correlated, but selected data is omitted for clarity of visualization. [3]
- Brier Skill Score: A metric that measures the accuracy of a model's predictions compared to a baseline or reference model. [3]
- Machine learning methods can be used to estimate the fraction of attributable risk (FAR) in climate science. [2]
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