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Tabular foundation models (TFMs) such as TabPFN (Tabular Prior-Data Fitted Network) are designed to generalize across heterogeneous tabular datasets through in-context learning (ICL). They perform prediction in a single forward pass conditioned on labeled examples without dataset-specific parameter updates. This paradigm is particularly attractive in industrial domains (e.g., finance and healthcare) where tabular prediction is pervasive. Retraining a bespoke model for each new table can be costly or infeasible in these settings, while data quality issues such as […]

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Tabular foundation models (TFMs) such as TabPFN (Tabular Prior-Data Fitted Network) are designed to generalize across heterogeneous tabular datasets through in-context learning (ICL). They perform prediction in a single forward pass conditioned on labeled examples without dataset-specific parameter updates. This paradigm is particularly attractive in industrial domains (e.g., finance and healthcare) where tabular prediction is pervasive. Retraining a bespoke model for each new table can be costly or infeasible in these settings, while data quality issues such as […]

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Objective: Algorithmic fairness is essential for equitable and trustworthy machine learning in healthcare. Most fairness tools emphasize single-axis demographic comparisons and may miss compounded disparities affecting intersectional populations. This study introduces Fairlogue, a toolkit designed to operationalize intersectional fairness assessment in observational and counterfactual contexts within clinical settings. Methods: Fairlogue is a Python-based toolkit composed of three components: 1) an observational framework extending demographic parity, equalized odds, and equal opportunity difference to intersectional populations; 2) a counterfactual framework […]

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Training interpretable concept-based policies requires practitioners to manually select which human-understandable concepts an agent should reason with when making sequential decisions. This selection demands domain expertise, is time-consuming and costly, scales poorly with the number of candidates, and provides no performance guarantees. To overcome this limitation, we propose the first algorithms for principled automatic concept selection in sequential decision-making. Our key insight is that concept selection can be viewed through the lens of state abstraction: intuitively, a concept […]

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How are you, hacker? 🪐 What’s happening in tech today, April 6, 2026? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, Pioneer 11 launches in 1973, Launch of Early Bird Satellite in 1965, and we present you with these top quality stories. From Your Work Trained the Model. The Model Replaced You. Philip K. Dick Wrote This Story in 1968. to OpenAI Bought TBPN Because PR Can’t Keep Up With AI, let’s […]

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We are back with another Company of the Week feature! Every week, we share an awesome tech brand from our tech company database, making their evergreen mark on the internet. This unique HackerNoon database ranks S&P 500 companies and top startups of the year alike. This week, we are proud to present PlayerZero, a provider of AI tools that help big companies catch software problems early, fix them faster, and stop the same issues from happening again. PlayerZero’s self-healing software for […]

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Reinforcement learning from verifiable rewards (RLVR) has improved the reasoning abilities of LLMs, yet a fundamental limitation remains: models cannot learn from problems that are too difficult to solve under their current policy, as these yield no meaningful reward signal. We propose a simple yet effective solution based on task reformulation. We transform challenging open-ended problems into cognitively simpler variants — such as multiple-choice and cloze formats — that preserve the original answer while reducing the effective search […]

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hey! NeurIPS releases these yearly challenges called MyoChallenges, that focus on human musculoskeletal research using RL. This is the official playlist (by MyoSuite) which has an overview of what to expect: https://youtube.com/playlist?list=PLq492wGha2Iwi8B7OOg5muUmIaqTnSmu8&si=QgAmv9ZvdWc9_tip The challenge would be released around July and I wanted to create a team and learn as much from the past challenges as possible till then! Hit me up if you’re interested!! anyway, thanks! submitted by /u/snailinyourmailpart2 [link] [comments]

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Machine learning models, and deep neural networks in particular, are increasingly deployed in risk-sensitive domains such as healthcare, environmental forecasting, and finance, where reliable quantification of predictive uncertainty is essential. However, many uncertainty quantification (UQ) methods remain difficult to apply due to their substantial computational cost. Sampling-based Bayesian learning approaches, such as Bayesian neural networks (BNNs), are particularly expensive since drawing and evaluating multiple parameter samples rapidly exhausts memory and compute resources. These constraints have limited the accessibility […]

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