GenAI4ELab/papercli-papers huggingface dataset:
Hyperbolic Self-paced Learning for Self-supervised Skeleton-based Action Representations
Authors: Luca Franco; Paolo Mandica; Bharti Munjal; Fabio Galasso
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Self-paced learning has been beneficial for tasks where some initial knowledge is available, such as weakly supervised learning and domain adaptation, to select and order the training sample sequence, from easy to complex. However its applicability remains unexplored in unsupervised learning, whereby the knowledge of the task matures during training.
We propose a novel HYperbolic Self-Paced model (HYSP) for learning skeletonbased action representations. HYSP adopts self-supervision: it uses data augmentations to generate two views of the same sample, and it learns by matching one (named online) to the other (the target). We propose to use hyperbolic uncertainty to determine the algorithmic learning pace, under the assumption that less uncertain samples should be more strongly driving the training, with a larger weight and pace. Hyperbolic uncertainty is a by-product of the adopted hyperbolic neural networks, it matures during training and it comes with no extra cost, compared to the established Euclidean SSL framework counterparts.
When tested on three established skeleton-based action recognition datasets, HYSP outperforms the state-of-the-art on PKU-MMD I, as well as on 2 out of 3 downstream tasks on NTU-60 and NTU-120. Additionally, HYSP only uses positive pairs and bypasses therefore the complex and computationally-demanding mining procedures required for the negatives in contrastive techniques.
Code is available at https://github.com/paolomandica/HYSP.
Learning to reason with relational abstractions
Authors: Andrew Joohun Nam; Mengye Ren; Chelsea Finn; James Lloyd McClelland
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Large language models have recently shown promising progress in mathematical reasoning when fine-tuned with human-generated sequences walking through a sequence of solution steps. However, the solution sequences are not formally structured and the resulting model-generated sequences may not reflect the kind of systematic reasoning we might expect an expert human to produce.
In this paper, we study how to build stronger reasoning capability in language models using the idea of relational abstractions. We introduce new types of sequences that more explicitly provide an abstract characterization of the transitions through intermediate solution steps to the goal state. We found that models that are supplied with such sequences as prompts can solve tasks with a significantly higher accuracy, and models that are trained to produce such sequences solve problems better than those that are trained with previously used human-generated sequences and other baselines. Our work thus takes several steps toward elucidating and improving how language models perform on tasks requiring multi-step mathematical reasoning.
Don’t forget the nullspace! Nullspace occupancy as a mechanism for out of distribution failure
Authors: Daksh Idnani; Vivek Madan; Naman Goyal; David J. Schwab; Shanmukha Ramakrishna Vedantam
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Out of distribution (OoD) generalization has received considerable interest in recent years. In this work, we identify a particular failure mode of OoD generalization for discriminative classifiers that is based on test data (from a new domain) lying in the nullspace of features learnt from source data. We demonstrate the existence of this failure mode across multiple networks trained across RotatedMNIST, PACS, TerraIncognita, DomainNet and ImageNet-R datasets. We then study different choices for characterizing the feature space and show that projecting intermediate representations onto the span of directions that obtain maximum training accuracy provides consistent improvements in OoD performance. Finally, we show that such nullspace behavior also provides an insight into neural networks trained on poisoned data. We hope our work galvanizes interest in the relationship between the nullspace occupancy failure mode and generalization.
Push and Pull:  Competing Feature-Prototype Interactions  Improve Semi-supervised Semantic Segmentation
Authors: Yuhang Ding; Yifan Sun; Yi Yang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: This paper challenges semi-supervised segmentation with a rethink on the feature-prototype interaction in the classification head. Specifically, we view each weight vector in the classification head as the prototype of a semantic category. The basic practice in the softmax classifier is to pull a feature towards its positive prototype (i.e., the prototype of its class), as well as to push it away from its negative prototypes. In this paper, we focus on the interaction between the feature and its negative prototypes, which is always “pushing” to make them dissimilar. While the pushing-away interaction is necessary, this paper reveals a new mechanism that the contrary interaction of pulling close negative prototypes is also beneficial. We have two insights for this counter-intuitive interaction: 1) some pseudo negative prototypes might actually be positive so that the pulling interaction can help resisting the pseudo-label noises, and 2) some true negative prototypes might contain contextual information that is beneficial. Therefore, we integrate these two competing interactions into a Push-and-Pull Learning (PPL) method. On the one hand, PPL introduces the novel pulling-close interaction between features and negative prototypes with a feature-to-prototype attention. On the other hand, PPL reinforces the original pushing-away interaction with a multi-prototype contrastive learning. While PPL is very simple, experiments show that it substantially improves semi-supervised segmentation and sets a new state of the art.
Federated Neural Bandits
Authors: Zhongxiang Dai; Yao Shu; Arun Verma; Flint Xiaofeng Fan; Bryan Kian Hsiang Low; Patrick Jaillet
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Recent works on neural contextual bandits have achieved compelling performances due to their ability to leverage the strong representation power of neural networks (NNs) for reward prediction. Many applications of contextual bandits involve multiple agents who collaborate without sharing raw observations, thus giving rise to the setting of federated contextual bandits}. Existing works on federated contextual bandits rely on linear or kernelized bandits, which may fall short when modeling complex real-world reward functions. So, this paper introduces the federated neural-upper confidence bound (FN-UCB) algorithm. To better exploit the federated setting, FN-UCB adopts a weighted combination of two UCBs: $\text{UCB}^{a}$ allows every agent to additionally use the observations from the other agents to accelerate exploration (without sharing raw observations), while $\text{UCB}^{b}$ uses an NN with aggregated parameters for reward prediction in a similar way to federated averaging for supervised learning. Notably, the weight between the two UCBs required by our theoretical analysis is amenable to an interesting interpretation, which emphasizes $\text{UCB}^{a}$ initially for accelerated exploration and relies more on $\text{UCB}^{b}$ later after enough observations have been collected to train the NNs for accurate reward prediction (i.e., reliable exploitation). We prove sub-linear upper bounds on both the cumulative regret and the number of communication rounds of FN-UCB, and empirically demonstrate its competitive performance.
Learning Mixture Models with Simultaneous Data Partitioning and Parameter Estimation
Authors: Parth Vipul Sangani; Arjun Shashank Kashettiwar; Durga S; Ganesh Ramakrishnan; Rishabh K Iyer; Abir De
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We study a new framework of learning mixture models via data partitioning called PRESTO, wherein we optimize a joint objective function on the model parameters and the partitioning, with each model tailored to perform well on its specific partition. We connect PRESTO to a number of past works in data partitioning, mixture models, and clustering, and show that PRESTO generalizes several loss functions including the k-means and Bregman clustering objective, the Gaussian mixture model objective, mixtures of support vector machines, and mixtures of linear regression. We then propose a new joint discrete-continuous optimization algorithm which achieves a bounded approximation guarantee for any general loss function, thereby achieving guarantees for the afore-mentioned problems as well. We study PRESTO in the context of resource efficient deep learning, where we train smaller resource constrained models on each partition and show that it outperforms existing data partitioning and model pruning/knowledge distillation approaches, which in contrast to PRESTO, require large initial (teacher) models.
ESCHER: Eschewing Importance Sampling in Games by Computing a History Value Function to Estimate Regret
Authors: Stephen Marcus McAleer; Gabriele Farina; Marc Lanctot; Tuomas Sandholm
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Recent techniques for approximating Nash equilibria in very large games leverage neural networks to learn approximately optimal policies (strategies). One promis- ing line of research uses neural networks to approximate counterfactual regret minimization (CFR) or its modern variants. DREAM, the only current CFR-based neural method that is model free and therefore scalable to very large games, trains a neural network on an estimated regret target that can have extremely high variance due to an importance sampling term inherited from Monte Carlo CFR (MCCFR). In this paper we propose an unbiased model-free method that does not require any importance sampling. Our method, ESCHER, is principled and is guaranteed to converge to an approximate Nash equilibrium with high probability. We show that the variance of the estimated regret of ESCHER is orders of magnitude lower than DREAM and other baselines. We then show that ESCHER outperforms the prior state of the art—DREAM and neural fictitious self play (NFSP)—on a number of games and the difference becomes dramatic as game size increases. In the very large game of dark chess, ESCHER is able to beat DREAM and NFSP in a head-to-head competition over 90% of the time.
Energy Consumption-Aware Tabular Benchmarks for Neural Architecture Search
Authors: Pedram Bakhtiarifard; Christian Igel; Raghavendra Selvan
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The demand for large-scale computational resources for  Neural Architecture Search (NAS) has been lessened by tabular benchmarks for NAS. Evaluating NAS strategies is now possible on extensive search spaces and at a moderate computational cost. But so far, NAS has mainly focused on maximising performance on some hold-out validation/test set. However, energy consumption is a partially conflicting objective that should not be neglected. We hypothesise that constraining NAS to include the energy consumption of training the models could reveal a sub-space of undiscovered architectures that are more computationally efficient with a smaller carbon footprint. To support the hypothesis, an existing tabular benchmark for NAS is augmented with the energy consumption of each architecture. We then perform multi-objective optimisation that includes energy consumption as an additional objective. We demonstrate the usefulness of multi-objective NAS for uncovering the trade-off between performance and energy consumption as well as for finding more energy-efficient architectures. The updated tabular benchmark is open-sourced to encourage the further exploration of energy consumption-aware NAS.
DLP: Data-Driven Label-Poisoning Backdoor Attack
Authors: Xun Xian; Xuan Bi; Mingyi Hong; Jie Ding
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Backdoor attacks, which aim to disrupt or paralyze classifiers on specific tasks, are becoming an emerging concern in several learning scenarios, e.g., Machine Learning as a Service (MLaaS). Various backdoor attacks have been introduced in the literature, including perturbation-based methods, which modify a subset of training data; and clean-sample methods, which relabel only a proportion of training samples. Indeed, clean-sample  attacks can be particularly stealthy since they never require modifying the samples at the training and test stages. However, the state-of-the-art clean-sample attack of relabelling training data based on their semantic meanings could be ineffective and inefficient in test performances due to heuristic selections of semantic patterns. In this work, we introduce a new type of clean-sample backdoor attack, named as DLP backdoor attack, allowing attackers to backdoor effectively, as measured by test performances, for an arbitrary backdoor sample size. The critical component of DLP is a data-driven backdoor scoring mechanism embedding in a multi-task formulation, which enables attackers to simultaneously perform well on the normal learning tasks and the backdoor tasks. Systematic empirical evaluations show the superior performance of the proposed DLP to state-of-the-art clean-sample attacks.
Homotopy-based training of NeuralODEs for accurate dynamics discovery
Authors: Joon-Hyuk Ko; Hankyul Koh; Nojun Park; Wonho Jhe
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Conceptually, Neural Ordinary Differential Equations (NeuralODEs) pose an attractive way to extract dynamical laws from time series data, as they are natural extensions of the traditional differential equation-based modeling paradigm of the physical sciences. In practice, NeuralODEs display long training times and suboptimal results, especially for longer duration data where they may fail to fit the data altogether. While methods have been proposed to stabilize NeuralODE training, many of these involve placing a strong constraint on the functional form the trained NeuralODE can take that the actual underlying governing equation does not guarantee satisfaction. In this work, we present a novel NeuralODE training algorithm that leverages tools from the chaos and mathematical optimization communities -- synchronization and homotopy optimization -- for a breakthrough in tackling the NeuralODE training obstacle. We demonstrate architectural changes are unnecessary for effective NeuralODE training. Compared to the conventional training methods, our algorithm achieves drastically lower loss values without any changes to the model architectures. Experiments on both simulated and real systems with complex temporal behaviors demonstrate NeuralODEs trained with our algorithm are able to accurately capture true long term behaviors and correctly extrapolate into the future.
The Cost of Privacy in Fair Machine Learning
Authors: Songkai Xue; Yuekai Sun
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: A common task in fair machine learning is training ML models that preserve certain summary statistics across subpopulations defined by sensitive attributes. However, access to such sensitive attributes in training data is restricted and the learner must rely on noisy proxies for the sensitive attributes. In this paper, we study the effect of a privacy mechanism that obfuscates the sensitive attributes from the learner on the fairness of the resulting classifier. We show that the cost of privacy in fair ML is a decline in the generalizability of fairness constraints.
Probing into the Fine-grained Manifestation in Multi-modal Image Synthesis
Authors: Qianyu Feng; Peike Li; Yulei Sui; Hongyu Zhang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The ever-growing development of multi-modal image synthesis brings unprecedented realism to generation tasks.  In practice, it is straightforward to judge the visual quality and reality of an image. However, it is labor-consuming to verify the correctness of semantic consistency in the auto-generation, which requires a comprehensive understanding and mapping of different modalities. The results of existing models are sorted and displayed largely relying on the global visual-text similarity. However, this coarse-grained approach does not capture the fine-grained semantic alignment between image regions and text spans. To address this issue, we first present a new method to evaluate the cross-modal consistency by inspecting the decomposed semantic concepts. We then introduce a new metric, called MIS-Score, which is designed to measure the fine-grained semantic alignment between a prompt and its generation quantitatively. Moreover, we have also developed an automated robustness testing technique with referential transforms to test and measure the robustness of multi-modal synthesis models. We have conducted comprehensive experiments to evaluate the performance of recent popular models for text-to-image generation. Our study demonstrates that the proposed metric MIS-Score represents better evaluation criteria than existing coarse-grained ones (e.g., CLIP) to understand the semantic consistency of the synthesized results. Our robustness testing method also proves the existence of biases embedded in the models, hence uncovering their limitations in real applications.
Unsupervised Learning of Causal Relationships from Unstructured Data
Authors: Marian Longa; Joao F. Henriques
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Endowing deep neural networks with the ability to reason about cause and effect would be an important step to make them more robust and interpretable. In this work we propose a variational framework that allows deep networks to learn latent variables and their causal relationships from unstructured data, with no supervision, or labeled interventions. Starting from an abstract Structural Equation Model (SEM), we show that maximizing its posterior probability yields a similar construction to a Variational Auto-Encoder (VAE), but with a structured prior coupled by non-linear equations. This prior represents an interpretable SEM with learnable parameters (such as a physical model or dependence structure), which can be fitted to data while simultaneously learning the latent variables. Unfortunately, computing KL-divergences with this non-linear prior is intractable. We show how linearizing arbitrary SEMs via back-propagation produces local non-isotropic Gaussian priors, for which the KL-divergences can be computed efficiently and differentiably. We propose two versions, one for IID data (such as images) which detects related causal variables within a sample, and one for non-IID data (such as video) which detects variables that are also related over time. Our proposal is complementary to causal discovery techniques, which assume given variables, and instead discovers both variables and their causal relationships. We experiment with recovering causal models from images, and learning temporal relations based on the Super Mario Bros videogame.
Fine-Tuning Offline Policies With Optimistic Action Selection
Authors: Max Sobol Mark; Ali Ghadirzadeh; Xi Chen; Chelsea Finn
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Offline reinforcement learning algorithms can train performant policies for hard tasks using previously-collected datasets. However, the quality of the offline dataset often limits the levels of performance possible. We consider the problem of improving offline policies through online fine-tuning. Offline RL requires a pessimistic training objective to mitigate distributional shift between the trained policy and the offline behavior policy, which will make the trained policy averse to picking novel actions. In contrast, online RL requires exploration, or optimism. Thus, fine-tuning online policies with the offline training objective is not ideal. Additionally, loosening the fine-tuning objective to allow for more exploration can potentially destroy the behaviors learned in the offline phase because of the sudden and significant change in the optimization objective. To mitigate this challenge, we propose a method to facilitate exploration during online fine-tuning that maintains the same training objective throughout both offline and online phases, while encouraging exploration. We accomplish this by changing the action-selection method to be more optimistic with respect to the Q-function. By choosing to take actions in the environment with higher expected Q-values, our method is able to explore and improve behaviors more efficiently, obtaining 56% more returns on average than the alternative approaches on several locomotion, navigation, and manipulation tasks.
Learning to Estimate Single-View Volumetric Flow Motions without 3D Supervision
Authors: Aleksandra Franz; Barbara Solenthaler; Nils Thuerey
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We address the challenging problem of jointly inferring the 3D flow and volumetric densities moving in a fluid from a monocular input video with a deep neural network. Despite the complexity of this task, we show that it is possible to train the corresponding networks without requiring any 3D ground truth for training. In the absence of ground truth data we can train our model with observations from real-world capture setups instead of relying on synthetic reconstructions. We make this unsupervised training approach possible by first generating an initial prototype volume which is then moved and transported over time without the need for volumetric supervision. Our approach relies purely on image-based losses, an adversarial discriminator network, and regularization. Our method can estimate long-term sequences in a stable manner, while achieving closely matching targets for inputs such as rising smoke plumes.
PathFusion: Path-consistent Lidar-Camera Deep Feature Fusion
Authors: Lemeng Wu; Dilin Wang; Meng Li; Yunyang Xiong; Raghuraman Krishnamoorthi; qiang liu; Vikas Chandra
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Fusing camera with LiDAR is a promising technique to improve the accuracy of 3D detection due to its complementary physical properties.
While most existing methods focus on fusing camera features directly with raw LiDAR point clouds or shallow 3D features, it is observed that direct deep 3D feature fusion achieves inferior accuracy due to feature mis-alignment. The mis-alignment that originates from the feature aggregation across large receptive fields becomes increasingly severe for deep network stages. In this paper, we propose PathFusion to enable path-consistent LiDAR-camera deep feature fusion. PathFusion introduces a path consistency loss between shallow and deep features, which encourages the 2D backbone and its fusion path to transform 2D features in a way that is semantically aligned with the transform of the 3D backbone. We apply PathFusion to the prior-art fusion baseline, Focals Conv, and observe more than 1.2% mAP improvements on the nuScenes test split consistently with and without testing-time augmentations. Moreover, PathFusion also improves KITTI AP 3D (R11) by more than 0.6% on moderate level.