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Found 50 papers, 25 papers with code
Exploiting Preference Elicitation in Interactive and User-centered Algorithmic Recourse: An Initial Exploration
Our results suggest that users may recognize that the guided interaction paradigm improves efficiency.
Unveiling LLMs: The Evolution of Latent Representations in a Temporal Knowledge Graph
We propose an end-to-end framework that jointly decodes the factual knowledge embedded in the latent space of LLMs from a vector space to a set of ground predicates and represents its evolution across the layers using a temporal knowledge graph.
Can LLMs Correct Physicians, Yet? Investigating Effective Interaction Methods in the Medical Domain
We explore the potential of Large Language Models (LLMs) to assist and potentially correct physicians in medical decision-making tasks.
Learning To Guide Human Decision Makers With Vision-Language Models
As a remedy, we introduce learning to guide (LTG), an alternative framework in which - rather than taking control from the human expert - the machine provides guidance useful for decision making, and the human is entirely responsible for coming up with a decision.
BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts
Neuro-Symbolic (NeSy) predictors that conform to symbolic knowledge - encoding, e. g., safety constraints - can be affected by Reasoning Shortcuts (RSs): They learn concepts consistent with the symbolic knowledge by exploiting unintended semantics.
A Unified Framework for Probabilistic Verification of AI Systems via Weighted Model Integration
The probabilistic formal verification (PFV) of AI systems is in its infancy.
Glitter or Gold? Deriving Structured Insights from Sustainability Reports via Large Language Models
This poses a challenge in efficiently gathering and aligning the data into a unified framework to derive insights related to Corporate Social Responsibility (CSR).
Meta-Path Learning for Multi-relational Graph Neural Networks
Existing multi-relational graph neural networks use one of two strategies for identifying informative relations: either they reduce this problem to low-level weight learning, or they rely on handcrafted chains of relational dependencies, called meta-paths.
Interpretability is in the Mind of the Beholder: A Causal Framework for Human-interpretable Representation Learning
This allows us to derive a principled notion of alignment between the machine representation and the vocabulary of concepts understood by the human.
Learning to Guide Human Experts via Personalized Large Language Models
In learning to defer, a predictor identifies risky decisions and defers them to a human expert.
Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts
Neuro-Symbolic (NeSy) predictive models hold the promise of improved compliance with given constraints, systematic generalization, and interpretability, as they allow to infer labels that are consistent with some prior knowledge by reasoning over high-level concepts extracted from sub-symbolic inputs.
Egocentric Hierarchical Visual Semantics
We achieve this goal by implementing an algorithm which, for any object, recursively recognizes its visual genus and its visual differentia.
Interval Logic Tensor Networks
In this paper, we introduce Interval Real Logic (IRL), a two-sorted logic that interprets knowledge such as sequential properties (traces) and event properties using sequences of real-featured data.
Neuro-Symbolic Reasoning Shortcuts: Mitigation Strategies and their Limitations
This setup offers clear advantages in terms of consistency to symbolic prior knowledge, and is often believed to provide interpretability benefits in that - by virtue of complying with the knowledge - the learned concepts can be better understood by human stakeholders.
Enhancing SMT-based Weighted Model Integration by Structure Awareness
The development of efficient exact and approximate algorithms for probabilistic inference is a long-standing goal of artificial intelligence research.
Neuro-Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsal
We introduce Neuro-Symbolic Continual Learning, where a model has to solve a sequence of neuro-symbolic tasks, that is, it has to map sub-symbolic inputs to high-level concepts and compute predictions by reasoning consistently with prior knowledge.
Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities
Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static) graph-structured data.
Explaining the Explainers in Graph Neural Networks: a Comparative Study
Following a fast initial breakthrough in graph based learning, Graph Neural Networks (GNNs) have reached a widespread application in many science and engineering fields, prompting the need for methods to understand their decision process.
Global Explainability of GNNs via Logic Combination of Learned Concepts
While instance-level explanation of GNN is a well-studied problem with plenty of approaches being developed, providing a global explanation for the behaviour of a GNN is much less explored, despite its potential in interpretability and debugging.
Rethinking and Recomputing the Value of ML Models
In this paper, we argue that the way we have been training and evaluating ML models has largely forgotten the fact that they are applied in an organization or societal context as they provide value to people.
SMT-based Weighted Model Integration with Structure Awareness
Weighted Model Integration (WMI) is a popular formalism aimed at unifying approaches for probabilistic inference in hybrid domains, involving logical and algebraic constraints.
GlanceNets: Interpretabile, Leak-proof Concept-based Models
There is growing interest in concept-based models (CBMs) that combine high-performance and interpretability by acquiring and reasoning with a vocabulary of high-level concepts.
Concept-level Debugging of Part-Prototype Networks
We propose ProtoPDebug, an effective concept-level debugger for ProtoPNets in which a human supervisor, guided by the model's explanations, supplies feedback in the form of what part-prototypes must be forgotten or kept, and the model is fine-tuned to align with this supervision.
Personalized Algorithmic Recourse with Preference Elicitation
It is paramount that the sequence of actions does not require too much effort for users to implement.
Machine Learning for Combinatorial Optimisation of Partially-Specified Problems: Regret Minimisation as a Unifying Lens
The challenge is to learn them from available data, while taking into account a set of hard constraints that a solution must satisfy, and that solving the optimisation problem (esp.
Lifelong Personal Context Recognition
We focus on the development of AIs which live in lifelong symbiosis with a human.
Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis
Being able to provide counterfactual interventions - sequences of actions we would have had to take for a desirable outcome to happen - is essential to explain how to change an unfavourable decision by a black-box machine learning model (e. g., being denied a loan request).
The Science of Rejection: A Research Area for Human Computation
We motivate why the science of learning to reject model predictions is central to ML, and why human computation has a lead role in this effort.
Toward a Unified Framework for Debugging Concept-based Models
In this paper, we tackle interactive debugging of "gray-box" concept-based models (CBMs).
Learning compositional programs with arguments and sampling
We showcase the potential of our approach by learning the Quicksort algorithm, showing how the ability to deal with arguments is crucial for learning and generalization.
Interactive Label Cleaning with Example-based Explanations
We tackle sequential learning under label noise in applications where a human supervisor can be queried to relabel suspicious examples.
Towards Visual Semantics
In this paper we provide a theory and an algorithm for how to build substance concepts which are in a one-to-one correspondence with classifications concepts, thus paving the way to the seamless integration between natural language descriptions and visual perception.
Neuro-Symbolic Constraint Programming for Structured Prediction
We propose Nester, a method for injecting neural networks into constrained structured predictors.
Human-in-the-loop Handling of Knowledge Drift
Motivated by this, we introduce TRCKD, a novel approach that combines automated drift detection and adaptation with an interactive stage in which the user is asked to disambiguate between different kinds of KD.
Generalising via Meta-Examples for Continual Learning in the Wild
Future deep learning systems call for techniques that can deal with the evolving nature of temporal data and scarcity of annotations when new problems occur.
Learning Aggregation Functions
Learning on sets is increasingly gaining attention in the machine learning community, due to its widespread applicability.
Learning in the Wild with Incremental Skeptical Gaussian Processes
The ability to learn from human supervision is fundamental for personal assistants and other interactive applications of AI.
Few-Shot Unsupervised Continual Learning through Meta-Examples
In real-world applications, data do not reflect the ones commonly used for neural networks training, since they are usually few, unlabeled and can be available as a stream.
Efficient Generation of Structured Objects with Constrained Adversarial Networks
Generative Adversarial Networks (GANs) struggle to generate structured objects like molecules and game maps.
A chemically etched corrugated feedhorn array for D-band CMB observations
We present the design, manufacturing, and testing of a 37-element array of corrugated feedhorns for Cosmic Microwave Background (CMB) measurements between $140$ and $170$ GHz.
Instrumentation and Methods for Astrophysics
Continual egocentric object recognition
We present a framework capable of tackilng the problem of continual object recognition in a setting which resembles that under whichhumans see and learn.
Decomposition Strategies for Constructive Preference Elicitation
We propose a decomposition technique for large preference-based decision problems relying exclusively on inference and feedback over partial configurations.
Constructive Preference Elicitation over Hybrid Combinatorial Spaces
The preferences are typically learned by querying the user for choice feedback over pairs or sets of objects.
Coactive Critiquing: Elicitation of Preferences and Features
When faced with complex choices, users refine their own preference criteria as they explore the catalogue of options.
Interpretability in Linear Brain Decoding
Despite extensive studies of this type, at present, there is no formal definition for interpretability of brain decoding models.
Constructive Preference Elicitation by Setwise Max-margin Learning
In this paper we propose an approach to preference elicitation that is suitable to large configuration spaces beyond the reach of existing state-of-the-art approaches.
Learning Modulo Theories for preference elicitation in hybrid domains
CLEO iteratively alternates a preference elicitation step, where pairs of candidate solutions are selected based on the current utility model, and a refinement step where the utility is refined by incorporating the feedback received.
Structured Learning Modulo Theories
The main idea is to leverage a state-of-the-art generalized Satisfiability Modulo Theory solver for implementing the inference and separation oracles of Structured Output SVMs.
Hybrid SRL with Optimization Modulo Theories
Generally speaking, the goal of constructive learning could be seen as, given an example set of structured objects, to generate novel objects with similar properties.
Predicting the Geometry of Metal Binding Sites from Protein Sequence
Metal binding is important for the structural and functional characterization of proteins.
