Machine learning is a vital part of many real-world systems, but several concerns remain about the lack of interpretability, explainability and robustness of black-box AI systems. Concept-based models (CBM) address some of these challenges by learning interpretable concepts from high-dimensional data, e.g. images, which are used to predict labels. An important issue in CBMs is concept leakage, i.e., spurious information in the learned concepts, which effectively leads to learning "wrong" concepts. Current mitigating strategies are heuristic, have strong assumptions, e.g., they assume that the concepts are statistically independent of each other, or require substantial human interaction in terms of both interventions and labels provided by annotators. In this paper, we describe a framework that provides theoretical guarantees on the correctness of the learned concepts and on the number of required labels, without requiring any interventions. Our framework leverages causal representation learning (CRL) to learn high-level causal variables from low-level data, and learns to align these variables with interpretable concepts. We propose a linear and a non-parametric estimator for this mapping, providing a finite-sample high probability result in the linear case and an asymptotic consistency result for the non-parametric estimator. We implement our framework with state-of-the-art CRL methods, and show its efficacy in learning the correct concepts in synthetic and image benchmarks.

Deep CASA, an spectrogram-based model, to Conv-TasNet, which uses very short waveform frames (such as 2 ms). We find that tPIT We study permutation invariant training (PIT), which targets at the based on such short waveform frames can be challenging. Therefore, permutation ambiguity problem for speaker independent source separation we propose performing tPIT in a pre-trained latent space--which models. We extend two state-of-the-art PIT strategies. First, allows for a more meaningful feature space for tPIT than the short we look at the two-stage speaker separation and tracking algorithm waveform frames. Further, when training the clustering model, Deep based on frame level PIT (tPIT) and clustering, which was originally CASA employs a memory and computationally expensive pairwise proposed for the STFT domain, and we adapt it to work with similarity loss that does not scale for waveform inputs. We propose waveforms and over a learned latent space. Further, we propose an a loss that reduces the complexity from quadratic to linear, making efficient clustering loss scalable to waveform models.