Publications

Leveraging Per-Instance Privacy for Machine Unlearning
Nazanin Mohammadi Sepahvand
Anvith Thudi
Berivan Isik
Ashmita Bhattacharyya
Nicolas Papernot
Eleni Triantafillou
Daniel M. Roy
Gintare Karolina Dziugaite
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (published)
doi.org
proceedings.mlr.press
LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
Rashid Mushkani
Shravan Nayak
Hugo Berard
Allison Cohen
Shin Koseki
Hadrien Bertrand
We introduce the Local Intersectional Visual Spaces (LIVS) dataset, a benchmark for multi-criteria alignment, developed through a two-year p… (see more)articipatory process with 30 community organizations to support the pluralistic alignment of text-to-image (T2I) models in inclusive urban planning. The dataset encodes 37,710 pairwise comparisons across 13,462 images, structured along six criteria - Accessibility, Safety, Comfort, Invitingness, Inclusivity, and Diversity - derived from 634 community-defined concepts. Using Direct Preference Optimization (DPO), we fine-tune Stable Diffusion XL to reflect multi-criteria spatial preferences and evaluate the LIVS dataset and the fine-tuned model through four case studies: (1) DPO increases alignment with annotated preferences, particularly when annotation volume is high; (2) preference patterns vary across participant identities, underscoring the need for intersectional data; (3) human-authored prompts generate more distinctive visual outputs than LLM-generated ones, influencing annotation decisiveness; and (4) intersectional groups assign systematically different ratings across criteria, revealing the limitations of single-objective alignment. While DPO improves alignment under specific conditions, the prevalence of neutral ratings indicates that community values are heterogeneous and often ambiguous. LIVS provides a benchmark for developing T2I models that incorporate local, stakeholder-driven preferences, offering a foundation for context-aware alignment in spatial design.
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (published)
doi.org
proceedings.mlr.press
Locate 3D: Real-World Object Localization via Self-Supervised Learning in 3D
Sergio Arnaud
Paul McVay
Ada Martin
Arjun Majumdar
Krishna Murthy
Phillip Thomas
Ruslan Partsey
Daniel Dugas
Abha Gejji
Alexander Sax
Vincent-Pierre Berges
Mikael Henaff
Ayush Jain
Ang Cao
Ishita Prasad
Mrinal Kalakrishnan
Michael G. Rabbat
Nicolas Ballas
Mahmoud Assran
Oleksandr Maksymets … (see 2 more)
Aravind Rajeswaran
Franziska Meier
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (published)
doi.org
proceedings.mlr.press
Mechanistic Unlearning: Robust Knowledge Unlearning and Editing via Mechanistic Localization
Phillip Huang Guo
Aaquib Syed
Abhay Sheshadri
Aidan Ewart
Gintare Karolina Dziugaite
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (published)
doi.org
proceedings.mlr.press
Monte Carlo Tree Diffusion for System 2 Planning
Jaesik Yoon
Hyeonseo Cho
Doojin Baek
Yoshua Bengio
Sungjin Ahn
Diffusion models have recently emerged as a powerful tool for planning. However, unlike Monte Carlo Tree Search (MCTS)-whose performance nat… (see more)urally improves with inference-time computation scaling-standard diffusion-based planners offer only limited avenues for the scalability. In this paper, we introduce Monte Carlo Tree Diffusion (MCTD), a novel framework that integrates the generative strength of diffusion models with the adaptive search capabilities of MCTS. Our method reconceptualizes denoising as a tree-structured process, allowing partially denoised plans to be iteratively evaluated, pruned, and refined. By selectively expanding promising trajectories while retaining the flexibility to revisit and improve suboptimal branches, MCTD achieves the benefits of MCTS such as controlling exploration-exploitation trade-offs within the diffusion framework. Empirical results on challenging long-horizon tasks show that MCTD outperforms diffusion baselines, yielding higher-quality solutions as inference-time computation increases.
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (published)
doi.org
proceedings.mlr.press
Multivariate Conformal Selection
Tian Bai
Yue Zhao
Xiang Yu
Archer Yang
Selecting high-quality candidates from large datasets is critical in applications such as drug discovery, precision medicine, and alignment … (see more)of large language models (LLMs). While Conformal Selection (CS) provides rigorous uncertainty quantification, it is limited to univariate responses and scalar criteria. To address this, we propose Multivariate Conformal Selection (mCS), a generalization of CS designed for multivariate response settings. Our method introduces regional monotonicity and employs multivariate nonconformity scores to construct conformal
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (published)
proceedings.mlr.press
proceedings.mlr.press
Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning
Guozheng Ma
Lu Li
Zilin Wang
Li Shen
Pierre-Luc Bacon
Dacheng Tao
Effectively scaling up deep reinforcement learning models has proven notoriously difficult due to network pathologies during training, motiv… (see more)ating various targeted interventions such as periodic reset and architectural advances such as layer normalization. Instead of pursuing more complex modifications, we show that introducing static network sparsity alone can unlock further scaling potential beyond their dense counterparts with state-of-the-art architectures. This is achieved through simple one-shot random pruning, where a predetermined percentage of network weights are randomly removed once before training. Our analysis reveals that, in contrast to naively scaling up dense DRL networks, such sparse networks achieve both higher parameter efficiency for network expressivity and stronger resistance to optimization challenges like plasticity loss and gradient interference. We further extend our evaluation to visual and streaming RL scenarios, demonstrating the consistent benefits of network sparsity.
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (published)
doi.org
proceedings.mlr.press
PANDAS: Improving Many-shot Jailbreaking via Positive Affirmation, Negative Demonstration, and Adaptive Sampling
Avery Ma
Yangchen Pan
Amir-massoud Farahmand
Many-shot jailbreaking circumvents the safety alignment of large language models by exploiting their ability to process long input sequences… (see more). To achieve this, the malicious target prompt is prefixed with hundreds of fabricated conversational turns between the user and the model. These fabricated exchanges are randomly sampled from a pool of malicious questions and responses, making it appear as though the model has already complied with harmful instructions. In this paper, we present PANDAS: a hybrid technique that improves many-shot jailbreaking by modifying these fabricated dialogues with positive affirmations, negative demonstrations, and an optimized adaptive sampling method tailored to the target prompt's topic. Extensive experiments on AdvBench and HarmBench, using state-of-the-art LLMs, demonstrate that PANDAS significantly outperforms baseline methods in long-context scenarios. Through an attention analysis, we provide insights on how long-context vulnerabilities are exploited and show how PANDAS further improves upon many-shot jailbreaking.
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (published)
doi.org
proceedings.mlr.press
PoisonBench: Assessing Language Model Vulnerability to Poisoned Preference Data
Tingchen Fu
Mrinank Sharma
Philip Torr
Shay B. Cohen
David M. Krueger
Fazl Barez
Preference learning is a central component for aligning current LLMs, but this process can be vulnerable to data poisoning attacks. To addre… (see more)ss this concern, we introduce PoisonBench, a benchmark for evaluating large language models' susceptibility to data poisoning during preference learning. Data poisoning attacks can manipulate large language model responses to include hidden malicious content or biases, potentially causing the model to generate harmful or unintended outputs while appearing to function normally. We deploy two distinct attack types across eight realistic scenarios, assessing 22 widely-used models. Our findings reveal concerning trends: (1) Scaling up parameter size does not always enhance resilience against poisoning attacks and the influence on model resilience varies among different model suites. (2) There exists a log-linear relationship between the effects of the attack and the data poison ratio; (3) The effect of data poisoning can generalize to extrapolated triggers that are not included in the poisoned data. These results expose weaknesses in current preference learning techniques, highlighting the urgent need for more robust defenses against malicious models and data manipulation.
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (published)
proceedings.mlr.press
proceedings.mlr.press
Position: Evaluating Generative AI Systems is a Social Science Measurement Challenge
Hanna Wallach
Meera Desai
A. Feder Cooper
Angelina Wang
Chad Atalla
Solon Barocas
Su Lin Blodgett
Alexandra Chouldechova
Emily Corvi
P. A. Dow
Jean Garcia-Gathright
A.R. Olteanu
Nicholas Pangakis
Stefanie Reed
Emily Sheng
Dan Vann
Jennifer Wortman Vaughan
Matthew Vogel
Hannah Washington
Abigail Z. Jacobs
The measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult, leading to what has been described as"a… (see more) tangle of sloppy tests [and] apples-to-oranges comparisons"(Roose, 2024). In this position paper, we argue that the ML community would benefit from learning from and drawing on the social sciences when developing and using measurement instruments for evaluating GenAI systems. Specifically, our position is that evaluating GenAI systems is a social science measurement challenge. We present a four-level framework, grounded in measurement theory from the social sciences, for measuring concepts related to the capabilities, behaviors, and impacts of GenAI. This framework has two important implications for designing and evaluating evaluations: First, it can broaden the expertise involved in evaluating GenAI systems by enabling stakeholders with different perspectives to participate in conceptual debates. Second, it brings rigor to both conceptual and operational debates by offering a set of lenses for interrogating the validity of measurement instruments and their resulting measurements.
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (published)
doi.org
proceedings.mlr.press
Position: Humanity Faces Existential Risk from Gradual Disempowerment
Jan Kulveit
Raymond Douglas
Nora Ammann
Deger Turan
David M. Krueger
David Duvenaud
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (published)
proceedings.mlr.press
proceedings.mlr.press
Position: Probabilistic Modelling is Sufficient for Causal Inference
Bruno Mlodozeniec
David M. Krueger
Richard E. Turner
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (published)
proceedings.mlr.press
proceedings.mlr.press