Publications

Sample compression unleashed : New generalization bounds for real valued losses
Mathieu Bazinet
Valentina Zantedeschi
Pascal Germain
2025-01-21
aistats.org/AISTATS/2025/Conference (poster)
proceedings.mlr.press
proceedings.mlr.press
Selective Unlearning via Representation Erasure Using Domain Adversarial Training
Nazanin Mohammadi Sepahvand
Eleni Triantafillou
Hugo Larochelle
Doina Precup
James J. Clark
Daniel M. Roy
Gintare Karolina Dziugaite
2025-01-21
ICLR.cc/2025/Conference (poster)
openreview.net
openreview.net
Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced Reasoning
Md Rifat Arefin
Gopeshh Subbaraj
Nicolas Gontier
Yann Lecun
Irina Rish
Ravid Shwartz-Ziv
Christopher Pal
Decoder-only Transformers often struggle with complex reasoning tasks, particularly arithmetic reasoning requiring multiple sequential opera… (see more)tions. In this work, we identify representation collapse in the model's intermediate layers as a key factor limiting their reasoning capabilities. To address this, we propose Sequential Variance-Covariance Regularization (Seq-VCR), which enhances the entropy of intermediate representations and prevents collapse. Combined with dummy pause tokens as substitutes for chain-of-thought (CoT) tokens, our method significantly improves performance in arithmetic reasoning problems. In the challenging
2025-01-21
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
Solving Hidden Monotone Variational Inequalities with Surrogate Losses
Ryan D'Orazio
Danilo Vucetic
Zichu Liu
Junhyung Lyle Kim
Ioannis Mitliagkas
Gauthier Gidel
Deep learning has proven to be effective in a wide variety of loss minimization problems. However, many applications of interest, like minim… (see more)izing projected Bellman error and min-max optimization, cannot be modelled as minimizing a scalar loss function but instead correspond to solving a variational inequality (VI) problem. This difference in setting has caused many practical challenges as naive gradient-based approaches from supervised learning tend to diverge and cycle in the VI case. In this work, we propose a principled surrogate-based approach compatible with deep learning to solve VIs. We show that our surrogate-based approach has three main benefits: (1) under assumptions that are realistic in practice (when hidden monotone structure is present, interpolation, and sufficient optimization of the surrogates), it guarantees convergence, (2) it provides a unifying perspective of existing methods, and (3) is amenable to existing deep learning optimizers like ADAM. Experimentally, we demonstrate our surrogate-based approach is effective in min-max optimization and minimizing projected Bellman error. Furthermore, in the deep reinforcement learning case, we propose a novel variant of TD(0) which is more compute and sample efficient.
2025-01-21
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
Structure Language Models for Protein Conformation Generation
Jiarui Lu
Xiaoyin Chen
Stephen Z. Lu
Chence Shi
Hongyu Guo
Yoshua Bengio
Jian Tang
Proteins adopt multiple structural conformations to perform their diverse biological functions, and understanding these conformations is cru… (see more)cial for advancing drug discovery. Traditional physics-based simulation methods often struggle with sampling equilibrium conformations and are computationally expensive. Recently, deep generative models have shown promise in generating protein conformations as a more efficient alternative. However, these methods predominantly rely on the diffusion process within a 3D geometric space, which typically centers around the vicinity of metastable states and is often inefficient in terms of runtime. In this paper, we introduce Structure Language Modeling (SLM) as a novel framework for efficient protein conformation generation. Specifically, the protein structures are first encoded into a compact latent space using a discrete variational auto-encoder, followed by conditional language modeling that effectively captures sequence-specific conformation distributions. This enables a more efficient and interpretable exploration of diverse ensemble modes compared to existing methods. Based on this general framework, we instantiate SLM with various popular LM architectures as well as proposing the ESMDiff, a novel BERT-like structure language model fine-tuned from ESM3 with masked diffusion. We verify our approach in various scenarios, including the equilibrium dynamics of BPTI, conformational change pairs, and intrinsically disordered proteins. SLM provides a highly efficient solution, offering a 20-100x speedup than existing methods in generating diverse conformations, shedding light on promising avenues for future research.
2025-01-21
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
Studying the Interplay Between the Actor and Critic Representations in Reinforcement Learning
Samuel Garcin
Trevor McInroe
Pablo Samuel Castro
Prakash Panangaden
Christopher G. Lucas
David Abel
Stefano V Albrecht
2025-01-21
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models
Daniel Levy
Siba Smarak Panigrahi
Sékou-Oumar Kaba
Qiang Zhu
Kin Long Kelvin Lee
Mikhail Galkin
Santiago Miret
Siamak Ravanbakhsh
Generating novel crystalline materials has the potential to lead to advancements in fields such as electronics, energy storage, and catalysi… (see more)s. The defining characteristic of crystals is their symmetry, which plays a central role in determining their physical properties. However, existing crystal generation methods either fail to generate materials that display the symmetries of real-world crystals, or simply replicate the symmetry information from examples in a database. To address this limitation, we propose SymmCD, a novel diffusion-based generative model that explicitly incorporates crystallographic symmetry into the generative process. We decompose crystals into two components and learn their joint distribution through diffusion: 1) the asymmetric unit, the smallest subset of the crystal which can generate the whole crystal through symmetry transformations, and; 2) the symmetry transformations needed to be applied to each atom in the asymmetric unit. We also use a novel and interpretable representation for these transformations, enabling generalization across different crystallographic symmetry groups. We showcase the competitive performance of SymmCD on a subset of the Materials Project, obtaining diverse and valid crystals with realistic symmetries and predicted properties.
2025-01-21
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo
João Loula
Benjamin LeBrun
Li Du
Ben Lipkin
Clemente Pasti
Gabriel Grand
Tianyu Liu
Yahya Emara
Marjorie Freedman
Jason Eisner
Ryan Cotterell
Vikash Mansinghka
Alexander K. Lew
Tim Vieira
Timothy J. O'Donnell
A wide range of LM applications require generating text that conforms to syntactic or semantic constraints. Imposing such constraints can be… (see more) naturally framed as probabilistic conditioning, but exact generation from the resulting distribution—which can differ substantially from the LM’s base distribution—is generally intractable. In this work, we develop an architecture for controlled LM generation based on sequential Monte Carlo (SMC). This SMC framework allows us to flexibly incorporate domain- and problem-specific constraints at inferencetime, and efficiently reallocate computational resources in light of new information during the course of generation. By comparing to a number of alternatives and ablations on four challenging domains—Python code generation for data science, text-to-SQL, goal inference, and molecule synthesis—we demonstrate that, with little overhead, our approach allows small open-source language models to outperform models over 8× larger, as well as closed-source, fine-tuned ones. In support of the probabilistic perspective, we show that these performance improvements are driven by better approximation to the posterior distribution. [Our system](https://github.com/probcomp/gen-parse) builds on the framework of Lew et al. (2023) and integrates with its language model probabilistic programming language, giving users a simple, programmable way to apply SMC to a broad variety of controlled generation problems.
2025-01-21
ICLR.cc/2025/Conference (oral)
openreview.net
openreview.net
TeD-Loc: Text Distillation for Weakly Supervised Object Localization
Shakeeb Murtaza
Soufiane Belharbi
Marco Pedersoli
Eric Granger
Weakly supervised object localization (WSOL) using classification models trained with only image-class labels remains an important challenge… (see more) in computer vision. Given their reliance on classification objectives, traditional WSOL methods like class activation mapping focus on the most discriminative object parts, often missing the full spatial extent. In contrast, recent WSOL methods based on vision-language models like CLIP require ground truth classes or external classifiers to produce a localization map, limiting their deployment in downstream tasks. Moreover, methods like GenPromp attempt to address these issues but introduce considerable complexity due to their reliance on conditional denoising processes and intricate prompt learning. This paper introduces Text Distillation for Localization (TeD-Loc), an approach that directly distills knowledge from CLIP text embeddings into the model backbone and produces patch-level localization. Multiple instance learning of these image patches allows for accurate localization and classification using one model without requiring external classifiers. Such integration of textual and visual modalities addresses the longstanding challenge of achieving accurate localization and classification concurrently, as WSOL methods in the literature typically converge at different epochs. Extensive experiments show that leveraging text embeddings and localization cues provides a cost-effective WSOL model. TeD-Loc improves Top-1 LOC accuracy over state-of-the-art models by about 5% on both CUB and ILSVRC datasets, while significantly reducing computational complexity compared to GenPromp.
2025-01-21
ArXiv (preprint)
doi.org
arxiv.org
The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws
Tian Jin
Ahmed Imtiaz Humayun
Utku Evci
Suvinay Subramanian
Amir Yazdanbakhsh
Dan Alistarh
Gintare Karolina Dziugaite
Pruning eliminates unnecessary parameters in neural networks; it offers a promising solution to the growing computational demands of large l… (see more)anguage models (LLMs). While many focus on post-training pruning, sparse pre-training--which combines pruning and pre-training into a single phase--provides a simpler alternative. In this work, we present the first systematic exploration of optimal sparse pre-training configurations for LLMs through an examination of 80 unique pruning schedules across different sparsity levels and training durations. We find that initiating pruning at 25% of total training compute and concluding at 75% achieves near-optimal final evaluation loss. These findings provide valuable insights for efficient and effective sparse pre-training of LLMs. Furthermore, we propose a new scaling law that modifies the Chinchilla scaling law to use the average parameter count over pre-training. Through empirical and theoretical validation, we demonstrate that this modified scaling law accurately models evaluation loss for both sparsely and densely pre-trained LLMs, unifying scaling laws across pre-training paradigms. Our findings indicate that while sparse pre-training achieves the same final model quality as dense pre-training for equivalent compute budgets, it provides substantial benefits through reduced model size, enabling significant potential computational savings during inference.
2025-01-21
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
On the Modeling Capabilities of Large Language Models for Sequential Decision Making
Martin Klissarov
R Devon Hjelm
Alexander T Toshev
Bogdan Mazoure
2025-01-21
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
The Pitfalls of Memorization: When Memorization Hurts Generalization
Reza Bayat
Mohammad Pezeshki
Elvis Dohmatob
David Lopez-Paz
Pascal Vincent
Neural networks often learn simple explanations that fit the majority of the data while memorizing exceptions that deviate from these explan… (see more)ations.This behavior leads to poor generalization when the learned explanations rely on spurious correlations. In this work, we formalize the interplay between memorization and generalization, showing that spurious correlations would particularly lead to poor generalization when are combined with memorization. Memorization can reduce training loss to zero, leaving no incentive to learn robust, generalizable patterns. To address this, we propose memorization-aware training (MAT), which uses held-out predictions as a signal of memorization to shift a model's logits. MAT encourages learning robust patterns invariant across distributions, improving generalization under distribution shifts.
2025-01-21
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net