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Researcher profile

Bernhard Schölkopf

7 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. Local Group Invariant Representations via Orbit Embeddings

    2016 · arXiv (Cornell University)

    Invariance to nuisance transformations is one of the desirable properties of effective representations. We consider transformations that form a \emph{group} and propose an approach based on kernel methods to derive local group invariant representations. Locality …

  2. Discriminative k-shot learning using probabilistic models

    2017 · arXiv (Cornell University)

    This paper introduces a probabilistic framework for k-shot image classification. The goal is to generalise from an initial large-scale classification task to a separate task comprising new classes and small numbers of examples. The new …

  3. Amortized Inference for Causal Structure Learning

    2022 · arXiv (Cornell University)

    Inferring causal structure poses a combinatorial search problem that typically involves evaluating structures with a score or independence test. The resulting search is costly, and designing suitable scores or tests that capture prior knowledge is …

  4. The Role of Pretrained Representations for the OOD Generalization of Reinforcement Learning Agents

    2021 · arXiv (Cornell University)

    Building sample-efficient agents that generalize out-of-distribution (OOD) in real-world settings remains a fundamental unsolved problem on the path towards achieving higher-level cognition. One particularly promising approach is to begin with low-dimensional, pretrained representations of our …

  5. Homomorphism Autoencoder -- Learning Group Structured Representations from Observed Transitions

    2022 · arXiv (Cornell University)

    How can agents learn internal models that veridically represent interactions with the real world is a largely open question. As machine learning is moving towards representations containing not just observational but also interventional knowledge, we …

  6. DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability

    2022 · arXiv (Cornell University)

    In representation learning, a common approach is to seek representations which disentangle the underlying factors of variation. Eastwood & Williams (2018) proposed three metrics for quantifying the quality of such disentangled representations: disentanglement (D), completeness …

  7. Limits of Transformer Language Models on Learning to Compose Algorithms

    2024 · arXiv (Cornell University)

    We analyze the capabilities of Transformer language models in learning compositional discrete tasks. To this end, we evaluate training LLaMA models and prompting GPT-4 and Gemini on four tasks demanding to learn a composition of …