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Mark Sandler 0002

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers
1 author row

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6

ICLR Conference 2025 Conference Paper

How new data permeates LLM knowledge and how to dilute it

  • Chen Sun
  • Renat Aksitov
  • Andrey Zhmoginov
  • Nolan Andrew Miller
  • Max Vladymyrov
  • Ulrich Rueckert
  • Been Kim
  • Mark Sandler 0002

Large language models continually learn through the accumulation of gradient-based updates, but how individual pieces of new information affect existing knowledge, leading to both beneficial generalization and problematic hallucination, remains poorly understood. We demonstrate that when learning new information, LLMs exhibit a "priming" effect: learning a new fact can cause the model to inappropriately apply that knowledge in unrelated contexts. To systematically study this phenomenon, we introduce "Outlandish," a carefully curated dataset of 1320 diverse text samples designed to probe how new knowledge permeates through an LLM's existing knowledge base. Using this dataset, we show that the degree of priming after learning new information can be predicted by measuring the token probability of key words before training. This relationship holds robustly across different model architectures (PALM-2, Gemma, Llama), sizes, and training stages. Finally, we develop two novel techniques to modulate how new knowledge affects existing model behavior: (1) a ``stepping-stone'' text augmentation strategy and (2) an ``ignore-k'' update pruning method. These approaches reduce undesirable priming effects by 50-95% while preserving the model's ability to learn new information. Our findings provide both empirical insights into how LLMs learn and practical tools for improving the specificity of knowledge insertion in language models. Further materials: https://sunchipsster1.github.io/projects/outlandish/

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ICML Conference 2022 Conference Paper

HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning

  • Andrey Zhmoginov
  • Mark Sandler 0002
  • Max Vladymyrov

In this work we propose a HyperTransformer, a Transformer-based model for supervised and semi-supervised few-shot learning that generates weights of a convolutional neural network (CNN) directly from support samples. Since the dependence of a small generated CNN model on a specific task is encoded by a high-capacity Transformer model, we effectively decouple the complexity of the large task space from the complexity of individual tasks. Our method is particularly effective for small target CNN architectures where learning a fixed universal task-independent embedding is not optimal and better performance is attained when the information about the task can modulate all model parameters. For larger models we discover that generating the last layer alone allows us to produce competitive or better results than those obtained with state-of-the-art methods while being end-to-end differentiable.

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ICML Conference 2021 Conference Paper

Meta-Learning Bidirectional Update Rules

  • Mark Sandler 0002
  • Max Vladymyrov
  • Andrey Zhmoginov
  • Nolan Miller
  • Tom Madams
  • Andrew Jackson 0004
  • Blaise Agüera y Arcas

In this paper, we introduce a new type of generalized neural network where neurons and synapses maintain multiple states. We show that classical gradient-based backpropagation in neural networks can be seen as a special case of a two-state network where one state is used for activations and another for gradients, with update rules derived from the chain rule. In our generalized framework, networks have neither explicit notion of nor ever receive gradients. The synapses and neurons are updated using a bidirectional Hebb-style update rule parameterized by a shared low-dimensional "genome". We show that such genomes can be meta-learned from scratch, using either conventional optimization techniques, or evolutionary strategies, such as CMA-ES. Resulting update rules generalize to unseen tasks and train faster than gradient descent based optimizers for several standard computer vision and synthetic tasks.

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FOCS Conference 2005 Conference Paper

On Learning Mixtures of Heavy-Tailed Distributions

  • Anirban Dasgupta 0001
  • John E. Hopcroft
  • Jon M. Kleinberg
  • Mark Sandler 0002

We consider the problem of learning mixtures of arbitrary symmetric distributions. We formulate sufficient separation conditions and present a learning algorithm with provable guarantees for mixtures of distributions that satisfy these separation conditions. Our bounds are independent of the variances of the distributions; to the best of our knowledge, there were no previous algorithms known with provable learning guarantees for distributions having infinite variance and/or expectation. For Gaussians and log-concave distributions, our results match the best known sufficient separation conditions by D. Achlioptas and F. McSherry (2005) and S. Vempala and G. Wang (2004). Our algorithm requires a sample of size O/spl tilde/(dk), where d is the number of dimensions and k is the number of distributions in the mixture. We also show that for isotropic power-laws, exponential, and Gaussian distributions, our separation condition is optimal up to a constant factor.

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