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.
Study identifies and measures biases in legal case data.
Study shows statistical biases can mislead transformer models, impairing their generalization.
The paper tackles sampling biases by ensuring minority groups are adequately represented in training data.
We introduce biased gradient oracles to capture a setting where the function measurements have an estimation error that can be controlled through a batch size parameter. Our proposed oracles are appealing in several practical contexts, for instance, risk measure estimation from a batch of independent and identically di…
The paper tackles bandit problems with biased offline data by using causal methods.
Stochastic EM with biased MCMC improves inference stability.
Many deep reinforcement learning algorithms contain inductive biases that sculpt the agent's objective and its interface to the environment. These inductive biases can take many forms, including domain knowledge and pretuned hyper-parameters. In general, there is a trade-off between generality and performance when algo…
New method learns collective variables using autoencoders for molecular simulations.
The paper argues that normalized mutual information is biased in clustering and community detection.
The study examines how social biases are reinforced in machine learning models used for credit scoring.
The effectiveness of machine learning algorithms depends on the quality and amount of data and the operationalization and interpretation by the human analyst. In humanitarian response, data is often lacking or overburdening, thus ambiguous, and the time-scarce, volatile, insecure environments of humanitarian activities…
We evaluate the folk wisdom that algorithmic decision rules trained on data produced by biased human decision-makers necessarily reflect this bias. We consider a setting where training labels are only generated if a biased decision-maker takes a particular action, and so "biased" training data arise due to discriminato…
A very simple event frequency approximation algorithm that is sensitive to event timeliness is suggested. The algorithm iteratively updates categorical click-distribution, producing (path of) a random walk on a standard -dimensional simplex. Under certain conditions, this random walk is self-similar and corresponds …
The paper addresses biased preferences in candidate selection, proposing a fair and utility-maximizing algorithm.
Study evaluates bias mitigation methods in deep learning, finds they often exploit hidden biases.
Machine learning algorithms are now frequently used in sensitive contexts that substantially affect the course of human lives, such as credit lending or criminal justice. This is driven by the idea that `objective' machines base their decisions solely on facts and remain unaffected by human cognitive biases, discrimina…
The paper analyzes time-dependent streaming data with biased gradient estimates and proposes improved stochastic optimization methods.
Many machine learning algorithms are trained and evaluated by splitting data from a single source into training and test sets. While such focus on in-distribution learning scenarios has led to interesting advancement, it has not been able to tell if models are relying on dataset biases as shortcuts for successful predi…
This work uncovers how model and data biases interact to cause unfairness in fraud detection.
Algorithm improves recommendation subset selection in the presence of biases.
Accurately predicting the outcome of sporting events has been a goal for many groups who seek to maximize profit. What makes this challenging is that the outcome of an event can be influenced by many factors that dynamically change across time. Oddsmakers attempt to estimate these factors by using both algorithmic and …
New algorithm finds unbiased subnetworks in biased datasets.
Study data biases to predict algorithmic discrimination, developing a Data Bias Profile.
Learning algorithms need bias to generalize and perform better than random guessing. We examine the flexibility (expressivity) of biased algorithms. An expressive algorithm can adapt to changing training data, altering its outcome based on changes in its input. We measure expressivity by using an information-theoretic …
This paper tackles confounding biases in data augmentation.
Algorithm identifies best arm with biased proxy and selective ground truth audits.
Survey on biases in image analysis for industrial safety.
Solves biased pseudo-labels in imbalanced SSL by refining them.
Link prediction is a popular research topic in network analysis. In the last few years, new techniques based on graph embedding have emerged as a powerful alternative to heuristics. In this article, we study the problem of systematic biases in the prediction, and show that some methods based on graph embedding offer le…
How do we learn from biased data? Historical datasets often reflect historical prejudices; sensitive or protected attributes may affect the observed treatments and outcomes. Classification algorithms tasked with predicting outcomes accurately from these datasets tend to replicate these biases. We advocate a causal mode…
Algorithm improves binary classification of biased grouped data.
Asynchronous stochastic approximations (SAs) are an important class of model-free algorithms, tools and techniques that are popular in multi-agent and distributed control scenarios. To counter Bellman's curse of dimensionality, such algorithms are coupled with function approximations. Although the learning/ control pro…
Study shows how online personalization can lead to unfair models due to biased user responses.
The paper tackles fair sequential decision making with biased linear bandit feedback.
Algorithm reduces audit costs by identifying best service configurations from biased textual evidence.
Biased mean regression estimates factors exceeding expected loss or radiation release severity.
While implicit feedback (e.g., clicks, dwell times, etc.) is an abundant and attractive source of data for learning to rank, it can produce unfair ranking policies for both exogenous and endogenous reasons. Exogenous reasons typically manifest themselves as biases in the training data, which then get reflected in the l…
We consider practical data characteristics underlying federated learning, where unbalanced and non-i.i.d. data from clients have a block-cyclic structure: each cycle contains several blocks, and each client's training data follow block-specific and non-i.i.d. distributions. Such a data structure would introduce client …
In this paper, we propose a new framework for mitigating biases in machine learning systems. The problem of the existing mitigation approaches is that they are model-oriented in the sense that they focus on tuning the training algorithms to produce fair results, while overlooking the fact that the training data can its…
The possible impact of algorithmic recommendation on the autonomy and free choice of Internet users is being increasingly discussed, especially in terms of the rendering of information and the structuring of interactions. This paper aims at reviewing and framing this issue along a double dichotomy. The first one addres…
Study incentivizes exploration in non-stationary MAB with compensation.
With the deluge of digitized information in the Big Data era, massive datasets are becoming increasingly available for learning predictive models. However, in many practical situations, the poor control of the data acquisition processes may naturally jeopardize the outputs of machine learning algorithms, and selection …
A new strategy for identifying the best arm in Gaussian bandits with improved exploration.
Conditional stochastic optimization covers a variety of applications ranging from invariant learning and causal inference to meta-learning. However, constructing unbiased gradient estimators for such problems is challenging due to the composition structure. As an alternative, we propose a biased stochastic gradient des…
NeuralRBMLE tackles explore-exploit trade-offs in contextual bandits with neural networks.
In many machine learning applications, there are multiple decision-makers involved, both automated and human. The interaction between these agents often goes unaddressed in algorithmic development. In this work, we explore a simple version of this interaction with a two-stage framework containing an automated model and…
The paper shows how the generalization curve can have multiple peaks, influenced by data and learning algorithm biases.
Estimates social network structure from random walk subgraphs.