Paper models human motivations in games using inverse RL.
problem Lack of understanding human motivations in game models.
method Multi-Motivation Behavior Modeling (MMBM) using inverse reinforcement learning.
result Reveals significant differences in value structures among player groups.
Graphical physics network learns intuitive physics using deep reinforcement learning with intrinsic motivation.
problem Teaching intuitive physics to AI agents.
method Integrates deep reinforcement learning with intrinsic reward normalization for efficient learning.
result Agent effectively learns object positions and velocities using intrinsic motivation.
Study shows how to motivate AI agents like humans through learning from demonstrations.
problem Transfer human-like intrinsic motivation to AI agents in RL.
method Inverse RL approach to learn an exploration bonus from demonstrations.
result AI agents can learn and use complex exploration behaviors from demonstrations.
Study evaluates new models using human feedback from another model.
problem Evaluate a new model using human feedback collected for another model.
method Formalize problem, propose model-based and model-free estimators, analyze unbiasedness, and empirically evaluate.
result Proposed estimators can predict absolute values, rank, and optimize evaluated policies.
Synthesizes AI and human creativity for more compelling art.
problem Making AI-generated art more human-like.
method Combining Deep Learning and Cognitive Psychology theories.
result Demonstrates how AI can incorporate human creativity theories.
A test measures artificial agents' human-like behavior in video games.
problem Measuring the believability of artificial agents' human-like behavior.
method Developed a non-parametric two-sample hypothesis test.
result The p-value correlates with human judgment of human-like behavior. Bayesian Neural Networks improve uncertainty modeling in facial emotion recognition.
problem High aleatoric uncertainty and visual ambiguity in facial emotion recognition.
method Bayesian Neural Networks approximated using MC-Dropout, MC-DropConnect, or Ensemble methods.
result Bayesian Neural Networks produce more human-like output probabilities.
Active IRL selects optimal human demonstrations for learning AI preferences.
problem Costly human demonstrations in IRL for autonomous systems.
method Information-theoretic acquisition function for selecting informative human demonstrations.
result Efficiently reduces human effort in learning AI preferences.
With an eye towards human-centered automation, we contribute to the development of a systematic means to infer features of human decision-making from behavioral data. Motivated by the common use of softmax selection in models of human decision-making, we study the maximum likelihood parameter estimation problem for sof…
Transparency, user trust, and human comprehension are popular ethical motivations for interpretable machine learning. In support of these goals, researchers evaluate model explanation performance using humans and real world applications. This alone presents a challenge in many areas of artificial intelligence. In this …
Humans take advantage of real world symmetries for various tasks, yet capturing their superb symmetry perception mechanism with a computational model remains elusive. Motivated by a new study demonstrating the extremely high inter-person accuracy of human perceived symmetries in the wild, we have constructed the first …
This work advances collaborative decision making by combining human and AI strengths in uncertainty quantification.
problem Current AI lacks robust decision-making capabilities under uncertainty, especially in high-stakes contexts.
method Introduces Human AI Collaborative Uncertainty Quantification (HACUQ) framework, formalizing AI-human collaboration and developing calibration algorithms.
result Optimal collaborative prediction sets follow a two-threshold structure, and online adaptation algorithms can adapt to evolving human behavior.
Trust-aware MAB improves learning performance by accounting for human deviation.
problem Learning performance suffers when humans deviate from recommended policies due to lack of trust.
method Integrates a dynamic trust model into MAB framework, establishing minimax regret and proposing a two-stage trust-aware procedure.
result Proves near-optimal statistical guarantees for trust-aware MAB algorithms.
Paper reviews intrinsic motivations and their role in open-ended learning.
problem Understanding intrinsic motivations and their role in open-ended learning.
method Defining intrinsic motivations, presenting psychological/neuroscientific and computational models.
result Links between psychological/neuroscientific and computational models of intrinsic motivations.
Dual active learning improves RLHF by selecting optimal conversations and teachers.
problem Efficiently aligning LLMs with human preferences using RLHF from feedback.
method Offline RL for conversation and teacher selection, dual active reward learning, pessimistic RL.
result The proposed algorithm achieves minimal generalized variance and outperforms state-of-the-arts.
Machine learning aids in shark detection at Muizenberg Beach.
problem Improving shark spotting efficiency using automated methods.
method Defined desirable properties, selected mathematical techniques, and partially implemented model.
result Extract useful information from shark images despite geometric transformations.
An approach for learning ancestral causal relationships in high dimensions, validated on human genome-wide data.
problem Learning ancestral causal relationships in high-dimensional biological data.
method Supervised learning approach with discrete indicators treated as labels, scalable to large problems.
result The approach is highly effective and scalable to the human genome-wide setting, robust to perturbations of input information.
We define a Hidden Markov Model (HMM) in which each hidden state has time-dependent activity levels that drive transitions and emissions, and show how to estimate its parameters. Our construction is motivated by the problem of inferring human mobility on sub-daily time scales from, for example, mobile phone …
Human visual object recognition is typically rapid and seemingly effortless, as well as largely independent of viewpoint and object orientation. Until very recently, animate visual systems were the only ones capable of this remarkable computational feat. This has changed with the rise of a class of computer vision algo…
Robots learn actions and language through curiosity-driven self-exploration.
problem Efficient development of actions and language in infants and robots.
method Curiosity-driven self-exploration using Q-learning to amortize active inference.
result Curiosity-driven exploration enables faster learning and compositional generalization.
This paper proposes a method to use LLMs as auxiliary evaluators in place of human judges.
problem The need for cost-effective and scalable evaluation of AI systems.
method Formulates a two-stage sampling design with LLM evaluations and human ratings, using a doubly robust estimator.
result Proposes a method to determine optimal sample sizes for human and LLM ratings.
Proposes a game-theoretic framework to motivate energy-efficient behavior in smart buildings.
problem Improving energy efficiency in smart building infrastructure through occupant behavior.
method Introduces a novel game-theoretic framework with human interaction, incorporating utility learning and deep neural networks.
result Demonstrates highly accurate prediction of occupant energy resource usage and explainable decision-making.
AMIGo uses adversarial intrinsic goals to teach RL agents new skills.
problem Learning in sparse reward environments.
method Adversarial intrinsic goals to generate a curriculum for a student policy.
result AMIGo enables agents to learn new skills without extrinsic rewards.
A new method for RLHF using proximal point Nash learning.
problem Capturing real human preferences in RLHF.
method Proximal point Nash learning, embedding self-play updates into a proximal point framework.
result High-probability last-iterate convergence for the combined method.
A new algorithm improves recommendation systems by considering repeated exposure to actions.
problem Improving recommendation systems by accounting for human memory decay.
method Introducing Weighted Tallying Bandits (WTB) and studying them under Repeated Exposure Optimality (REO).
result A simple modification of the successive elimination algorithm achieves nearly optimal complete policy regret.
Study reveals AI's spontaneous topic changes in text prediction.
problem AI's inability to spontaneously switch topics like humans.
method Defined topic as Token Priority Graphs (TPGs) and analyzed self-attention models.
result AI can only switch topics if lower-priority tokens outnumber higher-priority ones.
New method for efficient online exploration in RLHF reduces regret.
problem Efficiently collecting new preference data in RLHF to refine reward model and policy.
method Proposes a new exploration scheme that directs preference queries toward reducing uncertainty in reward differences most relevant to policy improvement.
result Establishes regret bounds of order T(β+1)/(β+2) for online RLHF, with polynomial scaling in all model parameters. Study human-machine interaction with private info using offline RL.
problem Confounding bias and distributional mismatch in offline RL for human-guided interaction.
method Developed a novel identification result and OPE method to address confounding bias, and used pessimism to tackle distributional mismatch.
result Policy pair converges to optimal one at satisfactory rate under mild assumptions.
Optimizes classifiers for varying levels of automation.
problem Supervised learning models often perform worse than human experts on specific instances.
method Focuses on convex margin-based classifiers, showing the problem is NP-hard. For SVMs, the objective function is decomposed into monotone and modular components, allowing efficient algorithms to solve the problem.
result The approach demonstrates that classifiers optimized for varying levels of automation can outperform full automation and human-only models.
Automated discovery of diverse self-organized patterns in complex systems.
problem Automated identification of interesting spatially localized patterns in self-organizing systems.
method Intrinsically motivated machine learning algorithms (POP-IMGEPs) combined with deep auto-encoders and CPPN primitives.
result Efficiency and effectiveness of the proposed method in discovering diverse patterns compared to baselines.
Study preference-based reinforcement learning in episodic kernel MDPs.
problem Learning from episodic human preferences in reinforcement learning.
method Developed preference-based value estimation and confidence sets for kernel-based MDPs.
result Proved high-probability regret bounds that converge to optimal policy value.
New research shows machine-assisted decisions can still be unfair even when the algorithm is fair.
problem Ensuring fairness in decisions made with machine-assisted human input.
method Formal model and lab experiment to analyze how machine predictions affect human decisions.
result Excluding information about protected groups from machine predictions can increase disparities.
The goal of Machine Learning to automatically learn from data, extract knowledge and to make decisions without any human intervention. Such automatic (aML) approaches show impressive success. Recent results even demonstrate intriguingly that deep learning applied for automatic classification of skin lesions is on par w…
Unified framework for human-like decision making in various sequential tasks.
problem Real-life decision-making involves diverse strategies leading to similar outcomes.
method Two-stream reward processing mechanism for flexible and unified models.
result Framework unified MAB, CB, and RL with comparable performance.
ACOL learns constraints from human preferences in driving simulations.
problem Learning constraints from human preferences in driving simulations.
method Adaptive Constraint Learning (ACOL) algorithm for constrained linear best-arm identification.
result ACOL's sample complexity matches worst-case lower bound and is significantly tighter in the average case.
Geometric framework detects concept frustration between human concepts and machine representations.
problem Aligning human concepts with machine learning representations.
method Geometric framework and similarity measures for detecting concept frustration.
result Concept frustration affects machine learning model performance and reorganizes learned concept representations.
Traditional disease surveillance can be augmented with a wide variety of real-time sources such as, news and social media. However, these sources are in general unstructured and, construction of surveillance tools such as taxonomical correlations and trace mapping involves considerable human supervision. In this paper,…
Develops new methods to create imperceptible image changes that fool classifiers.
problem Improving the robustness of image classifiers by creating subtle changes undetectable to humans.
method Two methods: Edge-Aware and Color-Aware, designed to reduce detectability of image perturbations.
result Demonstrated that the new methods effectively cause misclassification and are computationally efficient.
Study on model accuracy when human subjects adapt to predictions.
problem Model accuracy on human-adaptive data.
method Formalized transferability study with upper and lower bounds.
result Performance gap between source and induced target distributions.
Bayesian algorithms improve crowdsourcing with label and instance constraints.
problem Efficiently labeling large datasets with additional human annotator information.
method Developed Bayesian algorithms for semi-supervised crowdsourced classification under label and instance constraints.
result Improved performance compared to unsupervised crowdsourcing on various datasets.
Human cochlear models improve DNN noise suppression systems.
problem DNN-based noise suppression systems lack robustness to unseen noise conditions.
method Coupled cochlear models with DNNs to improve noise suppression.
result Cochlear models enhance DNN generalizability to various noise conditions.
Predicting delayed outcomes in recommender systems using proxies.
problem Predicting delayed outcomes in recommender systems.
method Formalized as adversarial, delayed online learning problem; proposed Factored Forecaster (FF) and Residual Factored Forecaster (RFF) neural network architectures.
result Residual Factored Forecaster (RFF) outperforms direct forecaster and Factored Forecaster (FF) in predicting human behavior.
EKH adds metrics to knot theory, enabling more detailed analysis.
problem Lack of quantitative data in knot theory.
method Integrates metric into knot theory with evolutionary Khovanov homology (EKH).
result EKH reveals non-trivial knot invariants at appropriate scales.
Paper detects changes in human behavior using smartphones.
problem Detecting changes in high-dimensional, heterogeneous, and periodic data.
method Hierarchical model with latent variables and non-stationary periodic covariance functions.
result The method accurately detects changes in human behavior using smartphones.
New research shows label refinement and weak training have limitations for aligning LLMs.
problem Limitations of refinement methods for aligning large language models.
method Analyzed probabilistic assumptions and alternative approaches to label refinement and weak training.
result Label refinement and weak training suffer from irreducible error, leaving a performance gap.
This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action selection. Therefore, computa…
Grading function evaluates Bach-style chorales, outperforming human experts.
problem Difficulty in automatically evaluating musical style correctness.
method Introduces a grading function for evaluating four-part chorales in the style of J.S. Bach.
result Transformer model output is outperformed by the grading function at discriminating Bach chorales.
Proposes a new fairness definition based on equity for machine learning classification.
problem Machine learning systems can perpetuate societal biases.
method Formalizes a new fairness definition based on equity, operationalizes it for classification, and evaluates its effectiveness.
result Demonstrates the effectiveness of the new fairness definition for equitable classification.