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An Investigation of Memorization Risk in Healthcare Foundation Models | OpenReview
Foundation models trained on large-scale de-identified electronic health records (EHRs) hold promise for clinical applications. However, their capacity to...
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AI Control: Improving Safety Despite Intentional Subversion | OpenReview
As large language models (LLMs) become more powerful and are deployed more autonomously, it will be increasingly important to prevent them from causing harmful...
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Safety Alignment Should be Made More Than Just a Few Tokens Deep | OpenReview
The safety alignment of current Large Language Models (LLMs) is vulnerable. Simple attacks, or even benign fine-tuning, can jailbreak aligned models. We note...
just a few
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Sampling Protein Language Models for Functional Protein Design | OpenReview
Protein language models have emerged as powerful ways to learn complex representations of proteins, thereby improving their performance on several downstream...
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A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning | OpenReview
We propose an expansion-based approach for task-free continual learning for the first time. Our model consists of a set of neural network experts and expands...
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CoCon: A Self-Supervised Approach for Controlled Text Generation | OpenReview
Pretrained Transformer-based language models (LMs) display remarkable natural language generation capabilities. With their immense potential, controlling text...
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Turning Up the Heat: Min-p Sampling for Creative and Coherent LLM Outputs | OpenReview
Large Language Models (LLMs) generate text by sampling the next token from a probability distribution over the vocabulary at each decoding step. Popular...
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Byzantine Machine Learning: MultiKrum and an Optimal Notion of Robustness | OpenReview
Aggregation rules are the cornerstone of distributed (or federated) learning in the presence of adversaries, under the so-called Byzantine threat model. They...
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Transport Clustering: Solving Low-Rank Optimal Transport via Clustering | OpenReview
Optimal transport (OT) finds a least cost transport plan between two probability distributions using a cost matrix over pairs of points. Constraining the rank...
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Inverting Gradient Attacks Makes Powerful Data Poisoning | OpenReview
Gradient attacks and data poisoning tamper with the training of machine learning algorithms to maliciously alter them and have been proven to be equivalent in...
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EPITOME: Experimental Protocol Inventory for Theory Of Mind Evaluation | OpenReview
We address a growing debate about the extent to which large language models (LLMs) produce behavior consistent with Theory of Mind (ToM) in humans. We present...
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Data Taggants: Dataset Ownership Verification Via Harmless Targeted Data Poisoning | OpenReview
Dataset ownership verification, the process of determining if a dataset is used in a model's training data, is necessary for detecting unauthorized data usage...
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Implementation Matters in Deep RL: A Case Study on PPO and TRPO | OpenReview
We study the roots of algorithmic progress in deep policy gradient algorithms through a case study on two popular algorithms: Proximal Policy Optimization...
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MV2Cyl: Reconstructing 3D Extrusion Cylinders from Multi-View Images | OpenReview
We present MV2Cyl, a novel method for reconstructing 3D from 2D multi-view images, not merely as a field or raw geometry but as a sketch-extrude CAD....
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Non-Vacuous Generalization Bounds for Large Language Models | OpenReview
Modern language models can contain billions of parameters, raising the question of whether they can generalize beyond the training data or simply regurgitate...
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Reasoning and Tools for Forecasting | OpenReview
Language models (LMs) trained on web-scale datasets are largely successful due to their ability to memorize large amounts of training data, even if only...
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Best Arm Identification for Prompt Learning under a Limited Budget | OpenReview
The remarkable instruction-following capability of large language models (LLMs) has sparked a growing interest in automatically learning suitable prompts....
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Toxicity Detection for Free | OpenReview
Current LLMs are generally aligned to follow safety requirements and tend to refuse toxic prompts. However, LLMs can fail to refuse toxic prompts or be...
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Generalizable Insights for Graph Transformers in Theory and Practice | OpenReview
Graph transformers (GTs) have demonstrated strong empirical performance; however, current architectures exhibit significant variations in their utilization of...
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Hopfield Boosting for Out-of-Distribution Detection | OpenReview
Out-of-distribution (OOD) detection is crucial for real-world machine learning. Outlier exposure methods, which use auxiliary outlier data, can significantly...
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Generalization of Scaled Deep ResNets in the Mean-Field Regime | OpenReview
Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this...
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Combinatorial Pure Exploration with Bottleneck Reward Function | OpenReview
In this paper, we study the Combinatorial Pure Exploration problem with the Bottleneck reward function (CPE-B) under the fixed-confidence (FC) and fixed-budget...
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CCVS: Context-aware Controllable Video Synthesis | OpenReview
This presentation introduces a self-supervised learning approach to the synthesis of new videos clips from old ones, with several new key elements for improved...
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Learning Label Encodings for Deep Regression | OpenReview
We propose an end-to-end automated approach to learn label encodings for deep regression.
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Combined Task and Motion Planning Via Sketch Decompositions | OpenReview
The challenge in combined task and motion planning (TAMP) is the effective integration of a search over a combinatorial space, usually carried out by a task...
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Revisiting Source-Free Domain Adaptation: a New Perspective via Uncertainty Control | OpenReview
Source-Free Domain Adaptation (SFDA) seeks to adapt a pre-trained source model to the target domain using only unlabeled target data, without access to the...
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Segment Any Point Cloud Sequences by Distilling Vision Foundation Models | OpenReview
Recent advancements in vision foundation models (VFMs) have opened up new possibilities for versatile and efficient visual perception. In this work, we...
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What is the Visual Cognition Gap between Humans and Multimodal LLMs? | OpenReview
Recently, Multimodal Large Language Models (MLLMs) and Vision Language Models (VLMs) have shown great promise in language-guided perceptual tasks such as...
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A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems | OpenReview
In order for agents in multi-agent systems (MAS) to be safe, they need to take into account the risks posed by the actions of other agents. However, the...
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Object-Flow Machine Learning: Active learning framework utilizing protocols information | OpenReview
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Analyzing and Improving Chain-of-Thought Monitorability Through Information Theory | OpenReview
Chain-of-thought (CoT) monitors are LLM-based systems that analyze reasoning traces to detect when outputs may exhibit attributes of interest such as...
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On Margins and Generalisation for Voting Classifiers | OpenReview
We study the generalisation properties of majority voting on finite ensembles of classifiers, proving margin-based generalisation bounds via the PAC-Bayes...
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Understanding Addition in Transformers | OpenReview
Understanding the inner workings of machine learning models like Transformers is vital for their safe and ethical use. This paper provides a comprehensive...
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Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets | OpenReview
Combinatorial optimization (CO) problems are often NP-hard and thus out of reach for exact algorithms, making them a tempting domain to apply machine learning...
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Latte: Latent Diffusion Transformer for Video Generation | OpenReview
We propose Latte, a novel Latent Diffusion Transformer for video generation. Latte first extracts spatio-temporal tokens from input videos and then adopts a...
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Strategyproof Voting under Correlated Beliefs | OpenReview
In voting theory, when voters have ranked preferences over candidates, the celebrated Gibbard-Satterthwaite Theorem essentially rules out the existence of...
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Identifiable Object-Centric Representation Learning via Probabilistic Slot Attention | OpenReview
Learning modular object-centric representations is said to be crucial for systematic generalization. Existing methods show promising object-binding...
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Twisting Lids Off with Two Hands | OpenReview
Manipulating objects with two multi-fingered hands has been a long-standing challenge in robotics, due to the contact-rich nature of many manipulation tasks...
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ProtoBrainMaps: Prototypical Brain Maps for Alzheimer's Disease Progression Modeling | OpenReview
We propose a novel interpretable framework named Prototypical Brain Maps (ProtoBrainMaps) for modeling Alzheimer's disease progressions through a set of...
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FearNet: Brain-Inspired Model for Incremental Learning | OpenReview
FearNet is a memory efficient neural-network, inspired by memory formation in the mammalian brain, that is capable of incremental class learning without...
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Offline vs. Online Learning in Model-based RL: Lessons for Data Collection Strategies | OpenReview
Data collection is crucial for learning robust world models in model-based reinforcement learning. The most prevalent strategies are to actively collect...
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On Narrative Information and the Distillation of Stories | OpenReview
We introduce narrative information and use contrastive learning to distill the narrative essence out of music albums.
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CATs: Cost Aggregation Transformers for Visual Correspondence | OpenReview
We propose a novel cost aggregation network, called Cost Aggregation Transformers (CATs), to find dense correspondences between semantically similar images...
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The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation | OpenReview
The goal of understanding decision-making behaviours in clinical environments is of paramount importance if we are to bring the strengths of machine learning...
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Predicting Deep Neural Network Generalization with Perturbation Response Curves | OpenReview
We introduce a new approach to predict generalization and invariance of trained neural nets, by generating perturbation response curves and statistics inspired...
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Gaussian Splatting to Real World Flight Navigation Transfer with Liquid Networks | OpenReview
Simulators are powerful tools for autonomous robot learning as they offer scalable data generation, flexible design, and optimization of trajectories. However,...
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Decoupled Weight Decay Regularization | OpenReview
L$_2$ regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we...
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Can Language Models Safeguard Themselves, Instantly and For Free? | OpenReview
Aligning pretrained language models (LMs) to handle a new safety scenario is normally difficult and expensive, often requiring access to large amounts of...
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