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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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0111 · Aug 201119922001200920172026
14 results for re-balancing

Paper presents a Bayesian-decision-theory framework for long-tailed classification.

problem Heavy imbalance and asymmetric misprediction costs in long-tailed datasets.
method Bayesian-decision-theory perspective, unifying re-balancing and ensemble methods.
result Improves accuracy for all classes, especially tails, with provably optimal decisions.

In this paper, we introduce a new approach to multiclass classification problem. We decompose the problem into a series of regression tasks, that are solved with CART trees. The proposed method works significantly faster than state-of-the-art solutions while giving the same level of model quality. The algorithm is also…

2019-09-11abs ↗pdf ↗

This paper examines biases in foundation models under long-tailed data and proposes a method to mitigate parameter imbalance.

problem The bias introduced by imbalanced training data in foundation models affects long-tailed downstream tasks.
method The paper examines parameter imbalance and data imbalance, proposing a backdoor adjustment method to mitigate parameter imbalance.
result An average performance increase of about 1.67% on each dataset.

Common fairness definitions in machine learning focus on balancing notions of disparity and utility. In this work, we study fairness in the context of risk disparity among sub-populations. We are interested in learning models that minimize performance discrepancies across sensitive groups without causing unnecessary ha…

2019-11-16abs ↗pdf ↗

We introduce a new pension product that offers retirees the opportunity for a lifelong income and a bequest for their estate. Based on a tontine mechanism, the product divides pension savings between a tontine account and a bequest account. The tontine account is given up to a tontine pool upon death while the bequest …

2019-03-14abs ↗pdf ↗

This paper evaluates fairness in deep metric learning and proposes a method to reduce subgroup performance gaps.

problem The negative impact of deep metric learning representations on minority subgroup performance in downstream tasks.
method Definition of fairness in DML through inter-class, intra-class, and uniformity properties; finDML benchmark; Partial Attribute De-correlation (PARADE) method.
result Bias in DML representations propagates to downstream tasks, even with balanced training data.

Reinforcement learning for optimizing retirement plans and target dated funds.

problem Optimizing financial goals through periodic investments and withdrawals.
method G-Learner and GIRL algorithms for goal-based wealth management.
result G-Learner provides a computationally tractable solution for wealth management tasks.

Study identifies COVID-19 pneumonia from chest X-rays.

problem Identifying COVID-19 pneumonia from other types and healthy lungs using CXR images.
method Proposed a multi-class and hierarchical classification schema using CXR images, texture descriptors, and a pre-trained CNN model. Employed resampling algorithms and early/late fusion techniques.
result Achieved macro-avg F1-Score of 0.65 and F1-Score of 0.89 for COVID-19 identification in hierarchical classification scenario.