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Edward Raff

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34 papers
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34

AAAI Conference 2026 Conference Paper

Bayesian Meta-Analyses Could Be More: A Case Study in Trial of Labor After a Cesarean-section Outcomes and Complications

  • Ashley Klein
  • Edward Raff
  • Marcia DesJardin

The meta-analysis's utility is dependent on previous studies having accurately captured the variables of interest, but in medical studies, a key decision variable that impacts a physician's decisions was not captured. This results in an unknown effect size and unreliable conclusions. A Bayesian approach may allow analysis to determine if the claim of a positive effect is still warranted, and we build a Bayesian approach to this common medical scenario. To demonstrate its utility, we assist professional OBGYNs in evaluating Trial of Labor After a Cesarean-section (TOLAC) situations where few interventions are available for patients and find the support needed for physicians to advance patient care.

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TIST Journal 2026 Journal Article

Human-Readable Adversarial Prompts: An Investigation into LLM Vulnerabilities Using Situational Context

  • Nilanjana Das
  • Edward Raff
  • Aman Chadha
  • Manas Gaur

Large Language Models (LLMs) remain vulnerable to adversarial prompts that elicit harmful responses. While many existing safety systems are better able to detect overtly nonsensical attack strings, human-readable prompts embedded in plausible situational contexts remain harder to identify and evaluate. This paper presents an empirical investigation of human-readable, situation-driven adversarial prompts for assessing LLM robustness. First, we use movie scripts as situational contexts (e.g., crime narratives) to construct natural-looking prompts that bypass safety mechanisms. Second, we transform adversarial gibberish into coherent, innocuous-appearing text that retains exploitation capability within these contextual frameworks. Third, we enhance the AdvPrompter framework with p-nucleus sampling to generate diverse human-readable attacks, substantially improving success rates against models including GPT-3.5 and Gemma-7b. We validate our approach through multi-method evaluation: automated harmfulness scoring (GPT-4o-mini), independent human assessment by 10 raters across 80 samples, and Elo rating meta-analysis of judge reliability. These findings highlight the need for safety mechanisms that can detect not only nonsensical jailbreak strings, but also coherent adversarial content embedded in realistic narrative contexts. The code for this study is publicly available. 1

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AAAI Conference 2026 Conference Paper

Intermediate N-Gramming: Deterministic and Fast N-Grams for Large N and Large Datasets

  • Ryan R. Curtin
  • Fred Lu
  • Edward Raff
  • Priyanka Ranade

The number of n-gram features grows exponentially in n, making it computationally demanding to compute the most frequent n-grams even for n as small as 3. Motivated by our production machine learning system built on n-gram features, we ask: is it possible to accurately, deterministically, and quickly recover the top-k most frequent n-grams? We devise a multi-pass algorithm called Intergrams that constructs candidate n-grams from the preceding (n-1)-grams. By designing this algorithm with hardware in mind, our approach yields more than an order of magnitude speedup (up to 33x!) over the next known fastest algorithm, even when similar optimization are applied to the other algorithm. Using the empirical power-law distribution over n-grams, we also provide theory to inform the efficacy of our multi-pass approach.

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AAAI Conference 2025 Conference Paper

Can LLMs Obfuscate Code? A Systematic Analysis of Large Language Models into Assembly Code Obfuscation

  • Seyedreza Mohseni
  • Seyedali Mohammadi
  • Deepa Tilwani
  • Yash Saxena
  • Gerald Ketu Ndawula
  • Sriram Vema
  • Edward Raff
  • Manas Gaur

Malware authors often employ code obfuscations to make their malware harder to detect. Existing tools for generating obfuscated code often require access to the original source code (e.g., C++ or Java), and adding new obfuscations is a non-trivial, labor-intensive process. In this study, we ask the following question: Can Large Language Models (LLMs) potentially generate a new obfuscated assembly code? If so, this poses a risk to anti-virus engines and potentially increases the flexibility of attackers to create new obfuscation patterns. We answer this in the affirmative by developing the MetamorphASM benchmark comprising MetamorphASM Dataset (MAD) along with three code obfuscation techniques: dead code, register substitution, and control flow change. The MetamorphASM systematically evaluates the ability of LLMs to generate and analyze obfuscated code using MAD, which contains 328,200 obfuscated assembly code samples. We release this dataset and analyze the success rate of various LLMs (e.g., GPT-3.5/4, GPT-4o-mini, Starcoder, CodeGemma, CodeLlama, CodeT5, and LLaMA 3.1) in generating obfuscated assembly code. The evaluation was performed using established information-theoretic metrics and manual human review to ensure correctness and provide the foundation for researchers to study and develop remediations to this risk.

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ECAI Conference 2025 Conference Paper

Large Language Models and Normalized Compression Distance: Better Compression Yet Worse Accuracy

  • John Hurwitz
  • Charles Nicholas
  • Edward Raff

It is generally well understood that predictive classification and compression are intrinsically related concepts in information theory. Indeed, many deep learning methods are explained as learning a kind of compression, and that better compression leads to better performance. We interrogate this hypothesis via the Normalized Compression Distance (NCD), which explicitly relies on compression as the means of measuring similarity between sequences and thus enables nearest-neighbor classification. By turning popular large language models (LLMs) into lossless compressors, we develop a Neural NCD and compare LLMs to classic general-purpose algorithms like gzip in a compression-distance-based classification setting. We find that whether neural compressors achieve better accuracy over a gzip baseline is dataset-dependent, despite consistently superior compression ratios. Though the best neural compressor achieves up to 7. 49 times better compression, we observe an up to 22. 2% decrease in accuracy relative to the gzip baseline.

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AAAI Conference 2025 Conference Paper

What Do Machine Learning Researchers Mean by “Reproducible”?

  • Edward Raff
  • Michel Benaroch
  • Sagar Samtani
  • Andrew L. Farris

The concern that Artificial Intelligence (AI) and Machine Learning (ML) are entering a "reproducibility crisis" has spurred significant research in the past few years. Yet with each paper, it is often unclear what someone means by "reproducibility". Our work attempts to clarify the scope of "reproducibility" as displayed by the community at large. In doing so, we propose to refine the research to eight general topic areas. In this light, we see that each of these areas contains many works that do not advertise themselves as being about "reproducibility", in part because they go back decades before the matter came to broader attention.

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NeurIPS Conference 2024 Conference Paper

A Walsh Hadamard Derived Linear Vector Symbolic Architecture

  • Mohammad Mahmudul Alam
  • Alexander Oberle
  • Edward Raff
  • Stella Biderman
  • Tim Oates
  • James Holt

Vector Symbolic Architectures (VSAs) are one approach to developing Neuro-symbolic AI, where two vectors in $\mathbb{R}^d$ are 'bound' together to produce a new vector in the same space. VSAs support the commutativity and associativity of this binding operation, along with an inverse operation, allowing one to construct symbolic-style manipulations over real-valued vectors. Most VSAs were developed before deep learning and automatic differentiation became popular and instead focused on efficacy in hand-designed systems. In this work, we introduce the Hadamard-derived linear Binding (HLB), which is designed to have favorable computational efficiency, and efficacy in classic VSA tasks, and perform well in differentiable systems.

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NeurIPS Conference 2024 Conference Paper

Assemblage: Automatic Binary Dataset Construction for Machine Learning

  • Chang Liu
  • Rebecca Saul
  • Yihao Sun
  • Edward Raff
  • Maya Fuchs
  • Townsend Southard Pantano
  • James Holt
  • Kristopher Micinski

Binary code is pervasive, and binary analysis is a key task in reverse engineering, malware classification, and vulnerability discovery. Unfortunately, while there exist large corpuses of malicious binaries, obtaining high-quality corpuses of benign binaries for modern systems has proven challenging (e. g. , due to licensing issues). Consequently, machine learning based pipelines for binary analysis utilize either costly commercial corpuses (e. g. , VirusTotal) or open-source binaries (e. g. , coreutils) available in limited quantities. To address these issues, we present Assemblage: an extensible cloud-based distributed system that crawls, configures, and builds Windows PE binaries to obtain high-quality binary corpuses suitable for training state-of-the-art models in binary analysis. We have run Assemblage on AWS over the past year, producing 890k Windows PE and 428k Linux ELF binaries across 29 configurations. Assemblage is designed to be both reproducible and extensible, enabling users to publish "recipes" for their datasets, and facilitating the extraction of a wide array of features. We evaluated Assemblage by using its data to train modern learning-based pipelines for compiler provenance and binary function similarity. Our results illustrate the practical need for robust corpuses of high-quality Windows PE binaries in training modern learning-based binary analyses.

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NeurIPS Conference 2024 Conference Paper

Is Function Similarity Over-Engineered? Building a Benchmark

  • Rebecca Saul
  • Chang Liu
  • Noah Fleischmann
  • Richard Zak
  • Kristopher Micinski
  • Edward Raff
  • James Holt

Binary analysis is a core component of many critical security tasks, including reverse engineering, malware analysis, and vulnerability detection. Manual analysis is often time-consuming, but identifying commonly-used or previously-seen functions can reduce the time it takes to understand a new file. However, given the complexity of assembly, and the NP-hard nature of determining function equivalence, this task is extremely difficult. Common approaches often use sophisticated disassembly and decompilation tools, graph analysis, and other expensive pre-processing steps to perform function similarity searches over some corpus. In this work, we identify a number of discrepancies between the current research environment and the underlying application need. To remedy this, we build a new benchmark, REFuSe-Bench, for binary function similarity detection consisting of high-quality datasets and tests that better reflect real-world use cases. In doing so, we address issues like data duplication and accurate labeling, experiment with real malware, and perform the first serious evaluation of ML binary function similarity models on Windows data. Our benchmark reveals that a new, simple baseline — one which looks at only the raw bytes of a function, and requires no disassembly or other pre-processing --- is able to achieve state-of-the-art performance in multiple settings. Our findings challenge conventional assumptions that complex models with highly-engineered features are being used to their full potential, and demonstrate that simpler approaches can provide significant value.

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NeurIPS Conference 2024 Conference Paper

Stabilizing Linear Passive-Aggressive Online Learning with Weighted Reservoir Sampling

  • Skyler Wu
  • Fred Lu
  • Edward Raff
  • James Holt

Online learning methods, like the seminal Passive-Aggressive (PA) classifier, are still highly effective for high-dimensional streaming data, out-of-core processing, and other throughput-sensitive applications. Many such algorithms rely on fast adaptation to individual errors as a key to their convergence. While such algorithms enjoy low theoretical regret, in real-world deployment they can be sensitive to individual outliers that cause the algorithm to over-correct. When such outliers occur at the end of the data stream, this can cause the final solution to have unexpectedly low accuracy. We design a weighted reservoir sampling (WRS) approach to obtain a stable ensemble model from the sequence of solutions without requiring additional passes over the data, hold-out sets, or a growing amount of memory. Our key insight is that good solutions tend to be error-free for more iterations than bad solutions, and thus, the number of passive rounds provides an estimate of a solution's relative quality. Our reservoir thus contains $K$ previous intermediate weight vectors with high survival times. We demonstrate our WRS approach on the Passive-Aggressive Classifier (PAC) and First-Order Sparse Online Learning (FSOL), where our method consistently and significantly outperforms the unmodified approach. We show that the risk of the ensemble classifier is bounded with respect to the regret of the underlying online learning method.

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AAAI Conference 2023 Conference Paper

A Coreset Learning Reality Check

  • Fred Lu
  • Edward Raff
  • James Holt

Subsampling algorithms are a natural approach to reduce data size before fitting models on massive datasets. In recent years, several works have proposed methods for subsampling rows from a data matrix while maintaining relevant information for classification. While these works are supported by theory and limited experiments, to date there has not been a comprehensive evaluation of these methods. In our work, we directly compare multiple methods for logistic regression drawn from the coreset and optimal subsampling literature and discover inconsistencies in their effectiveness. In many cases, methods do not outperform simple uniform subsampling.

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ECAI Conference 2023 Conference Paper

An Easy Rejection Sampling Baseline via Gradient Refined Proposals

  • Edward Raff
  • Mark McLean
  • James Holt

Rejection sampling is a common tool for low dimensional problems (d ≤ 2), often touted as an “easy” way to obtain valid samples from a distribution f(·) of interest. In practice it is non-trivial to apply, often requiring considerable mathematical effort to devise a good proposal distribution g(·) and select a supremum C. More advanced samplers require additional mathematical derivations, limitations on f(·), or even cross-validation, making them difficult to apply. We devise a new approximate baseline approach to rejection sampling that works with less information, requiring only a differentiable f(·) be specified, making it easier to use. We propose a new approach to rejection sampling by refining a parameterized proposal distribution with a loss derived from the acceptance threshold. In this manner we obtain comparable or better acceptance rates on current benchmarks by up to 7. 3×, while requiring no extra assumptions or any derivations to use: only a differentiable f(·) is required. While approximate, the results are correct with high probability, and in all tests pass a distributional check. This makes our approach easy to use, reproduce, and efficacious.

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NeurIPS Conference 2023 Conference Paper

Emergent and Predictable Memorization in Large Language Models

  • Stella Biderman
  • USVSN PRASHANTH
  • Lintang Sutawika
  • Hailey Schoelkopf
  • Quentin Anthony
  • Shivanshu Purohit
  • Edward Raff

Memorization, or the tendency of large language models (LLMs) to output entire sequences from their training data verbatim, is a key concern for deploying language models. In particular, it is vital to minimize a model's memorization of sensitive datapoints such as those containing personal identifiable information (PII). The prevalence of such undesirable memorization can pose issues for model trainers, and may even require discarding an otherwise functional model. We therefore seek to predict which sequences will be memorized before a large model's full train-time by extrapolating the memorization behavior of lower-compute trial runs. We measure memorization in the Pythia model suite and plot scaling laws for forecasting memorization, allowing us to provide equi-compute recommendations to maximize the reliability (recall) of such predictions. We additionally provide further novel discoveries on the distribution of memorization scores across models and data. We release all code and data necessary to reproduce the results in this paper at https: //github. com/EleutherAI/pythia.

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NeurIPS Conference 2023 Conference Paper

LEACE: Perfect linear concept erasure in closed form

  • Nora Belrose
  • David Schneider-Joseph
  • Shauli Ravfogel
  • Ryan Cotterell
  • Edward Raff
  • Stella Biderman

Concept erasure aims to remove specified features from a representation. It can improve fairness (e. g. preventing a classifier from using gender or race) and interpretability (e. g. removing a concept to observe changes in model behavior). We introduce LEAst-squares Concept Erasure (LEACE), a closed-form method which provably prevents all linear classifiers from detecting a concept while changing the representation as little as possible, as measured by a broad class of norms. We apply LEACE to large language models with a novel procedure called concept scrubbing, which erases target concept information from every layer in the network. We demonstrate our method on two tasks: measuring the reliance of language models on part-of-speech information, and reducing gender bias in BERT embeddings. Our code is available at https: //github. com/EleutherAI/concept-erasure.

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TMLR Journal 2023 Journal Article

Multimodal Language Learning for Object Retrieval in Low Data Regimes in the Face of Missing Modalities

  • Kasra Darvish
  • Edward Raff
  • Francis Ferraro
  • Cynthia Matuszek

Our study is motivated by robotics, where when dealing with robots or other physical systems, we often need to balance competing concerns of relying on complex, multimodal data coming from a variety of sensors with a general lack of large representative datasets. Despite the complexity of modern robotic platforms and the need for multimodal interaction, there has been little research on integrating more than two modalities in a low data regime with the real-world constraint that sensors fail due to obstructions or adverse conditions. In this work, we consider a case in which natural language is used as a retrieval query against objects, represented across multiple modalities, in a physical environment. We introduce extended multimodal alignment (EMMA), a method that learns to select the appropriate object while jointly refining modality-specific embeddings through a geometric (distance-based) loss. In contrast to prior work, our approach is able to incorporate an arbitrary number of views (modalities) of a particular piece of data. We demonstrate the efficacy of our model on a grounded language object retrieval scenario. We show that our model outperforms state-of-the-art baselines when little training data is available. Our code is available at https://github.com/kasraprime/EMMA.

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ICLR Conference 2023 Conference Paper

Neural Bregman Divergences for Distance Learning

  • Fred Lu
  • Edward Raff
  • Francis Ferraro

Many metric learning tasks, such as triplet learning, nearest neighbor retrieval, and visualization, are treated primarily as embedding tasks where the ultimate metric is some variant of the Euclidean distance (e.g., cosine or Mahalanobis), and the algorithm must learn to embed points into the pre-chosen space. The study of non-Euclidean geometries is often not explored, which we believe is due to a lack of tools for learning non-Euclidean measures of distance. Recent work has shown that Bregman divergences can be learned from data, opening a promising approach to learning asymmetric distances. We propose a new approach to learning arbitrary Bergman divergences in a differentiable manner via input convex neural networks and show that it overcomes significant limitations of previous works. We also demonstrate that our method more faithfully learns divergences over a set of both new and previously studied tasks, including asymmetric regression, ranking, and clustering. Our tests further extend to known asymmetric, but non-Bregman tasks, where our method still performs competitively despite misspecification, showing the general utility of our approach for asymmetric learning.

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ICML Conference 2023 Conference Paper

Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling

  • Stella Biderman
  • Hailey Schoelkopf
  • Quentin Gregory Anthony
  • Herbie Bradley
  • Kyle O'Brien
  • Eric Hallahan
  • Mohammad Aflah Khan
  • Shivanshu Purohit

How do large language models (LLMs) develop and evolve over the course of training? How do these patterns change as models scale? To answer these questions, we introduce Pythia, a suite of 16 LLMs all trained on public data seen in the exact same order and ranging in size from 70M to 12B parameters. We provide public access to 154 checkpoints for each one of the 16 models, alongside tools to download and reconstruct their exact training dataloaders for further study. We intend Pythia to facilitate research in many areas, and we present several case studies including novel results in memorization, term frequency effects on few-shot performance, and reducing gender bias. We demonstrate that this highly controlled setup can be used to yield novel insights toward LLMs and their training dynamics. Trained models, analysis code, training code, and training data can be found at https: //github. com/EleutherAI/pythia.

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ICML Conference 2023 Conference Paper

Recasting Self-Attention with Holographic Reduced Representations

  • Mohammad Mahmudul Alam
  • Edward Raff
  • Stella Biderman
  • Tim Oates 0001
  • James Holt

In recent years, self-attention has become the dominant paradigm for sequence modeling in a variety of domains. However, in domains with very long sequence lengths the $\mathcal{O}(T^2)$ memory and $\mathcal{O}(T^2 H)$ compute costs can make using transformers infeasible. Motivated by problems in malware detection, where sequence lengths of $T \geq 100, 000$ are a roadblock to deep learning, we re-cast self-attention using the neuro-symbolic approach of Holographic Reduced Representations (HRR). In doing so we perform the same high-level strategy of the standard self-attention: a set of queries matching against a set of keys, and returning a weighted response of the values for each key. Implemented as a “Hrrformer” we obtain several benefits including $\mathcal{O}(T H \log H)$ time complexity, $\mathcal{O}(T H)$ space complexity, and convergence in $10\times$ fewer epochs. Nevertheless, the Hrrformer achieves near state-of-the-art accuracy on LRA benchmarks and we are able to learn with just a single layer. Combined, these benefits make our Hrrformer the first viable Transformer for such long malware classification sequences and up to $280\times$ faster to train on the Long Range Arena benchmark.

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NeurIPS Conference 2023 Conference Paper

Reproducibility in Multiple Instance Learning: A Case For Algorithmic Unit Tests

  • Edward Raff
  • James Holt

Multiple Instance Learning (MIL) is a sub-domain of classification problems with positive and negative labels and a "bag" of inputs, where the label is positive if and only if a positive element is contained within the bag, and otherwise is negative. Training in this context requires associating the bag-wide label to instance-level information, and implicitly contains a causal assumption and asymmetry to the task (i. e. , you can't swap the labels without changing the semantics). MIL problems occur in healthcare (one malignant cell indicates cancer), cyber security (one malicious executable makes an infected computer), and many other tasks. In this work, we examine five of the most prominent deep-MIL models and find that none of them respects the standard MIL assumption. They are able to learn anti-correlated instances, i. e. , defaulting to "positive" labels until seeing a negative counter-example, which should not be possible for a correct MIL model. We suspect that enhancements and other works derived from these models will share the same issue. In any context in which these models are being used, this creates the potential for learning incorrect models, which creates risk of operational failure. We identify and demonstrate this problem via a proposed ``algorithmic unit test'', where we create synthetic datasets that can be solved by a MIL respecting model, and which clearly reveal learning that violates MIL assumptions. The five evaluated methods each fail one or more of these tests. This provides a model-agnostic way to identify violations of modeling assumptions, which we hope will be useful for future development and evaluation of MIL models.

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NeurIPS Conference 2023 Conference Paper

Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterations

  • Edward Raff
  • Amol Khanna
  • Fred Lu

To the best of our knowledge, there are no methods today for training differentially private regression models on sparse input data. To remedy this, we adapt the Frank-Wolfe algorithm for $L_1$ penalized linear regression to be aware of sparse inputs and to use them effectively. In doing so, we reduce the training time of the algorithm from $\mathcal{O}( T D S + T N S)$ to $\mathcal{O}(N S + T \sqrt{D} \log{D} + T S^2)$, where $T$ is the number of iterations and a sparsity rate $S$ of a dataset with $N$ rows and $D$ features. Our results demonstrate that this procedure can reduce runtime by a factor of up to $2, 200\times$, depending on the value of the privacy parameter $\epsilon$ and the sparsity of the dataset.

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NeurIPS Conference 2022 Conference Paper

A General Framework for Auditing Differentially Private Machine Learning

  • Fred Lu
  • Joseph Munoz
  • Maya Fuchs
  • Tyler LeBlond
  • Elliott Zaresky-Williams
  • Edward Raff
  • Francis Ferraro
  • Brian Testa

We present a framework to statistically audit the privacy guarantee conferred by a differentially private machine learner in practice. While previous works have taken steps toward evaluating privacy loss through poisoning attacks or membership inference, they have been tailored to specific models or have demonstrated low statistical power. Our work develops a general methodology to empirically evaluate the privacy of differentially private machine learning implementations, combining improved privacy search and verification methods with a toolkit of influence-based poisoning attacks. We demonstrate significantly improved auditing power over previous approaches on a variety of models including logistic regression, Naive Bayes, and random forest. Our method can be used to detect privacy violations due to implementation errors or misuse. When violations are not present, it can aid in understanding the amount of information that can be leaked from a given dataset, algorithm, and privacy specification.

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AAAI Conference 2022 Conference Paper

Bridging the Gap: Using Deep Acoustic Representations to Learn Grounded Language from Percepts and Raw Speech

  • Gaoussou Youssouf Kebe
  • Luke E. Richards
  • Edward Raff
  • Francis Ferraro
  • Cynthia Matuszek

Learning to understand grounded language, which connects natural language to percepts, is a critical research area. Prior work in grounded language acquisition has focused primarily on textual inputs. In this work, we demonstrate the feasibility of performing grounded language acquisition on paired visual percepts and raw speech inputs. This will allow interactions in which language about novel tasks and environments is learned from end-users, reducing dependence on textual inputs and potentially mitigating the effects of demographic bias found in widely available speech recognition systems. We leverage recent work in self-supervised speech representation models and show that learned representations of speech can make language grounding systems more inclusive towards specific groups while maintaining or even increasing general performance.

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ICML Conference 2022 Conference Paper

Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations

  • Mohammad Mahmudul Alam
  • Edward Raff
  • Tim Oates 0001
  • James Holt

Due to the computational cost of running inference for a neural network, the need to deploy the inferential steps on a third party’s compute environment or hardware is common. If the third party is not fully trusted, it is desirable to obfuscate the nature of the inputs and outputs, so that the third party can not easily determine what specific task is being performed. Provably secure protocols for leveraging an untrusted party exist but are too computational demanding to run in practice. We instead explore a different strategy of fast, heuristic security that we call Connectionist Symbolic Pseudo Secrets. By leveraging Holographic Reduced Representations (HRRs), we create a neural network with a pseudo-encryption style defense that empirically shows robustness to attack, even under threat models that unrealistically favor the adversary.

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AAAI Conference 2022 Conference Paper

Out of Distribution Data Detection Using Dropout Bayesian Neural Networks

  • Andre T. Nguyen
  • Fred Lu
  • Gary Lopez Munoz
  • Edward Raff
  • Charles Nicholas
  • James Holt

We explore the utility of information contained within a dropout based Bayesian neural network (BNN) for the task of detecting out of distribution (OOD) data. We first show how previous attempts to leverage the randomized embeddings induced by the intermediate layers of a dropout BNN can fail due to the distance metric used. We introduce an alternative approach to measuring embedding uncertainty, justify its use theoretically, and demonstrate how incorporating embedding uncertainty improves OOD data identification across three tasks: image classification, language classification, and malware detection.

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NeurIPS Conference 2021 Conference Paper

A Spoken Language Dataset of Descriptions for Speech-Based Grounded Language Learning

  • Gaoussou Kebe
  • Padraig Higgins
  • Patrick Jenkins
  • Kasra Darvish
  • Rishabh Sachdeva
  • Ryan Barron
  • John Winder
  • Donald Engel

Grounded language acquisition is a major area of research combining aspects of natural language processing, computer vision, and signal processing, compounded by domain issues requiring sample efficiency and other deployment constraints. In this work, we present a multimodal dataset of RGB+depth objects with spoken as well as textual descriptions. We analyze the differences between the two types of descriptive language and our experiments demonstrate that the different modalities affect learning. This will enable researchers studying the intersection of robotics, NLP, and HCI to better investigate how the multiple modalities of image, depth, text, speech, and transcription interact, as well as how differences in the vernacular of these modalities impact results.

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AAAI Conference 2021 Conference Paper

Bringing UMAP Closer to the Speed of Light with GPU Acceleration

  • Corey J. Nolet
  • Victor Lafargue
  • Edward Raff
  • Thejaswi Nanditale
  • Tim Oates
  • John Zedlewski
  • Joshua Patterson

The Uniform Manifold Approximation and Projection (UMAP) algorithm has become widely popular for its ease of use, quality of results, and support for exploratory, unsupervised, supervised, and semi-supervised learning. While many algorithms can be ported to a GPU in a simple and direct fashion, such efforts have resulted in inefficient and inaccurate versions of UMAP. We show a number of techniques that can be used to make a faster and more faithful GPU version of UMAP, and obtain speedups of up to 100x in practice. Many of these design choices/lessons are general purpose and may inform the conversion of other graph and manifold learning algorithms to use GPUs. Our implementation has been made publicly available as part of the open source RAPIDS cuML library (https: //github. com/rapidsai/cuml).

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AAAI Conference 2021 Conference Paper

Classifying Sequences of Extreme Length with Constant Memory Applied to Malware Detection

  • Edward Raff
  • William Fleshman
  • Richard Zak
  • Hyrum S. Anderson
  • Bobby Filar
  • Mark McLean

Recent works within machine learning have been tackling inputs of ever-increasing size, with cybersecurity presenting sequence classification problems of particularly extreme lengths. In the case of Windows executable malware detection, inputs may exceed 100 MB, which corresponds to a time series with T = 100, 000, 000 steps. To date, the closest approach to handling such a task is MalConv, a convolutional neural network capable of processing up to T = 2, 000, 000 steps. The O(T) memory of CNNs has prevented further application of CNNs to malware. In this work, we develop a new approach to temporal max pooling that makes the required memory invariant to the sequence length T. This makes MalConv 116× more memory efficient, and up to 25. 8× faster to train on its original dataset, while removing the input length restrictions to MalConv. We re-invest these gains into improving the Mal- Conv architecture by developing a new Global Channel Gating design, giving us an attention mechanism capable of learning feature interactions across 100 million time steps in an efficient manner, a capability lacked by the original MalConv CNN. Our implementation can be found at https: //github. com/ NeuromorphicComputationResearchProgram/MalConv2

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IJCAI Conference 2021 Conference Paper

Exact Acceleration of K-Means++ and K-Means||

  • Edward Raff

K-Means++ and its distributed variant K-Means|| have become de facto tools for selecting the initial seeds of K-means. While alternatives have been developed, the effectiveness, ease of implementation, and theoretical grounding of the K-means++ and || methods have made them difficult to "best" from a holistic perspective. We focus on using triangle inequality based pruning methods to accelerate both of these algorithms to yield comparable or better run-time without sacrificing any of the benefits of these approaches. For both algorithms we are able to reduce distance computations by over 500×. For K-means++ this results in up to a 17×speedup in run-time and a551×speedup for K-means||. We achieve this with simple, but carefully chosen, modifications to known techniques which makes it easy to integrate our approach into existing implementations of these algorithms.

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NeurIPS Conference 2021 Conference Paper

Intelligent Sight and Sound: A Chronic Cancer Facial Pain Dataset

  • Catherine Ordun
  • Alexandra Cha
  • Edward Raff
  • Byron Gaskin
  • Alexander Hanson
  • Mason Rule
  • Sanjay Purushotham
  • James Gulley

Cancer patients experience high rates of chronic pain throughout the treatment process. Assessing pain for this patient population is a vital component of psychological and functional well-being, as it can cause a rapid deterioration of quality of life. Existing work in facial pain detection often have deficiencies in labeling or methodology that prevent them from being clinically relevant. This paper introduces the first chronic cancer pain dataset, collected as part of the Intelligent Sight and Sound (ISS) clinical trial, guided by clinicians to help ensure that model findings yield clinically relevant results. The data collected to date consists of 29 patients, 509 smartphone videos, 189, 999 frames, and self-reported affective and activity pain scores adopted from the Brief Pain Inventory (BPI). Using static images and multi-modal data to predict self-reported pain levels, early models show significant gaps between current methods available to predict pain today, with room for improvement. Due to the especially sensitive nature of the inherent Personally Identifiable Information (PII) of facial images, the dataset will be released under the guidance and control of the National Institutes of Health (NIH).

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NeurIPS Conference 2021 Conference Paper

Learning with Holographic Reduced Representations

  • Ashwinkumar Ganesan
  • Hang Gao
  • Sunil Gandhi
  • Edward Raff
  • Tim Oates
  • James Holt
  • Mark McLean

Holographic Reduced Representations (HRR) are a method for performing symbolic AI on top of real-valued vectors by associating each vector with an abstract concept, and providing mathematical operations to manipulate vectors as if they were classic symbolic objects. This method has seen little use outside of older symbolic AI work and cognitive science. Our goal is to revisit this approach to understand if it is viable for enabling a hybrid neural-symbolic approach to learning as a differential component of a deep learning architecture. HRRs today are not effective in a differential solution due to numerical instability, a problem we solve by introducing a projection step that forces the vectors to exist in a well behaved point in space. In doing so we improve the concept retrieval efficacy of HRRs by over $100\times$. Using multi-label classification we demonstrate how to leverage the symbolic HRR properties to develop a output layer and loss function that is able to learn effectively, and allows us to investigate some of the pros and cons of an HRR neuro-symbolic learning approach.

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AAAI Conference 2021 Conference Paper

Research Reproducibility as a Survival Analysis

  • Edward Raff

There has been increasing concern within the machine learning community that we are in a reproducibility crisis. As many have begun to work on this problem, all work we are aware of treat the issue of reproducibility as an intrinsic binary property: a paper is or is not reproducible. Instead, we consider modeling the reproducibility of a paper as a survival analysis problem. We argue that this perspective represents a more accurate model of the underlying meta-science question of reproducible research, and we show how a survival analysis allows us to draw new insights that better explain prior longitudinal data. The data and code can be found at https: //github. com/EdwardRaff/Research-Reproducibility- Survival-Analysis

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AAAI Conference 2020 Conference Paper

A New Burrows Wheeler Transform Markov Distance

  • Edward Raff
  • Charles Nicholas
  • Mark McLean

Prior work inspired by compression algorithms has described how the Burrows Wheeler Transform can be used to create a distance measure for bioinformatics problems. We describe issues with this approach that were not widely known, and introduce our new Burrows Wheeler Markov Distance (BWMD) as an alternative. The BWMD avoids the shortcomings of earlier efforts, and allows us to tackle problems in variable length DNA sequence clustering. BWMD is also more adaptable to other domains, which we demonstrate on malware classification tasks. Unlike other compression-based distance metrics known to us, BWMD works by embedding sequences into a fixed-length feature vector. This allows us to provide significantly improved clustering performance on larger malware corpora, a weakness of prior methods.

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NeurIPS Conference 2019 Conference Paper

A Step Toward Quantifying Independently Reproducible Machine Learning Research

  • Edward Raff

What makes a paper independently reproducible? Debates on reproducibility center around intuition or assumptions but lack empirical results. Our field focuses on releasing code, which is important, but is not sufficient for determining reproducibility. We take the first step toward a quantifiable answer by manually attempting to implement 255 papers published from 1984 until 2017, recording features of each paper, and performing statistical analysis of the results. For each paper, we did not look at the authors code, if released, in order to prevent bias toward discrepancies between code and paper.

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JMLR Journal 2017 Journal Article

JSAT: Java Statistical Analysis Tool, a Library for Machine Learning

  • Edward Raff

Java Statistical Analysis Tool (JSAT) is a Machine Learning library written in pure Java. It works to fill a void in the Java ecosystem for a general purpose library that is relatively high performance and flexible, which is not adequately fulfilled by Weka (Hall et al., 2009) and Java-ML (Abeel et al., 2009). Almost all of the algorithms are independently implemented using an Object- Oriented framework. JSAT is made available under the GNU GPL license here: github.com/EdwardRaff/JSAT. [abs] [ pdf ][ bib ] [ code ] [ webpage ] &copy JMLR 2017. ( edit, beta )

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