Statistical learning

ACML brings together research on the theory and practice of machine learning, with conference and journal publication tracks, invited talks, tutorials and workshops. The 2026 tutorial programme covers efficient and trustworthy reasoning with foundation models, robust statistical learning with alpha-divergence, topological deep learning, multimodal time series, generative methods for causal inference, distribution shifts, and continual learning toward self-evolving agents. Researchers from all regions are welcome. The event runs 1–4 December 2026. Its registration system lists an overall 08:00

…anatomically into Complementary Learning Systems, with the hippocampus rapidly encoding individual episodes and the neocortex slowly extracting regularities over days, months, and years. But this do

Understanding the mechanisms of statistical learning, where organisms infer structure from sensory observations without explicit instruction, is crucial in comprehending how the brain processes and responds to complex and dynamic environments. This work explores the innate capabilities of mice to discern environmental regularities, focusing on the role of the hippocampus in this unsupervised learning process. The study leverages a novel comparative behavioural paradigm, termed X-detection, to manipulate the statistics of embedded tone sequences (patterns) in an auditory cover task: subjects s

A fundamental property of natural intelligence is the capacity to recognise and leverage statistical patterns in the environment to promote adaptive behaviour. This capability, often termed statistical learning (SL), enables us to construct accurate models of the world that aid in perception and decision-making, when faced with noisy and ambiguous sensory input. However, despite extensive data from human experiments and proposals from theoretical neuroscience, the neural implementation of SL remains elusive. To address this, we employed a two-alternative forced-choice (2-AFC) sound categorisat

Understanding the mechanisms of statistical learning, where organisms infer structure from sensory observations without explicit instruction, is crucial in comprehending how the brain processes and responds to complex environments. This work explores the innate capabilities of both humans and mice to discern environmental regularities, focusing on the role of the hippocampus in this implicit learning processes. We use pupillometry to measure whether animals can detect and track patterns in rapidly unfolding sounds. The study leverages a novel comparative behavioural paradigm, termed X-detectio

…response sensitivity to the unfolding statistical structure of soundscapes. A critical aspect of this form of statistical learning is the interplay between feedforward and feedback pathways forming “listening loops” in the auditory brain. We performed simultaneous recordings of neuronal activity in the inferior colliculus (IC) and the primar

The brain is sensitive to the structure of the surrounding environment (statistical learning) as reflected in differential responses to stimuli depending on their relation to other stimuli. For example, both the auditory cortex and hippocampus are sensitive to the probability of appearance of sounds in a sequence, a simple form of statistical learning. Here, we posed the question of whether these two structures are sensitive to different statistical features of the acoustic environment. Using Neuropixels probes to perform the largescale single cell electrophysiological recordings simultaneousl

…anatomically into Complementary Learning Systems, with the hippocampus rapidly encoding individual episodes and the neocortex slowly extracting regularities over days, months, and years. But this do

The ‘quantification’ of perception is arguably both one of the most important and most difficult aspects of perception study. This is particularly true in visual perception, in which the evaluation of the perceptual threshold is a pillar of the experimental process. The choice of the correct adaptive psychometric procedure, as well as the selection of the proper parameters, is a difficult but key aspect of the experimental protocol. For instance, Bayesian methods such as QUEST, require the a priori choice of a family of functions (e.g. Gaussian), which is rarely known before the experiment, as

Learning verbs is difficult and critical to learning one's native language. Children appear to benefit from seeing multiple events and comparing them to each other, and structural alignment theory provides a good theoretical framework to guide research into how preschool children may be comparing events as they learn new verbs. The talk will include 6 studies of early verb learning that make use of eye-tracking procedures as well as other behavioral (pointing) procedures, and that test key predictions from SA theory including the prediction that seeing similar examples before more varied examp

…This process is called statistical learning. Here we address two key open questions: (1) does statistical learning modulate pain perception? and (2) is it affected in people with chronic musculoskeletal pain? In a first experiment

We live in highly structured environments where patterns often repeat. Our brain forms internal models of the world by extracting the regularities. Such models are utilised to form predictions about future events and incoming information. Predictions lead to efficient neural processing of incoming stimuli and hence facilitate perception. Although the existence of prediction signals is known, much less is known about the neural mechanisms underlying the propagation of prediction signals, especially in higher-vision. Predictive processing theories propose that in cortical hierarchies, high-leve

Acting judiciously in an environment requires capitalising on the statistical structure underlying observed stimuli. However, structure is expensive to determine and exploit, so efficient learners should focus exclusively on utility-relevant characteristics. To assess this, we administered a pair of related tasks, the first ('learning') required subjects to determine and exploit relevant, and ignore irrelevant, structure to maximize reward; the second ('read-out') quantified their resulting sensitivity to different structure components. Statistical relationships were realised in sampled three-

Recent studies have shown that stimulus history can be decoded via the use of broadband sensory impulses to reactivate mnemonic representations. It has also been shown that predictive mechanisms in the auditory system demonstrate similar tonotopic organization of neural activity as that elicited by the perceived stimuli. However, it remains unclear if the mnemonic and predictive information can be decoded from cortical activity simultaneously and from overlapping neural populations. Here, we passively exposed anesthetized rats to complex auditory sequences consisting of artificial vowel triple

…collaborative research: (1) machine learning and networks, and (2) graph dimensionality. Machine learning and networks. Decades of work have produced a vast corpus of research characterising the topological, combinatorial, statistical and spectral properties of graphs. Each graph property can be thought of as a feature that captures important (and sometimes overlapping) characteristics of a network. We have d