GenAI4ELab/papercli-papers huggingface dataset:
On the Existence of a Trojaned Twin Model
Authors: Songzhu Zheng; Yikai Zhang; Weimin Lyu; Mayank Goswami; Anderson Schneider; Yuriy Nevmyvaka; Haibin Ling; Chao Chen
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We study the Trojan Attack problem, where malicious attackers sabotage deep
neural network models with poisoned training data. In most existing works, the
effectiveness of the attack is largely overlooked; many attacks can be ineffective
or inefficient for certain training schemes, e.g., adversarial training. In this paper,
we adopt a novel perspective and look into the quantitative relationship between a
clean model and its Trojaned counterpart. We formulate a successful attack using
classic machine learning language. Under mild assumptions, we show theoretically
that there exists a Trojaned model, named Trojaned Twin, that is very close to the
clean model. This attack can be achieved by simply using a universal Trojan trigger
intrinsic to the data distribution. This has powerful implications in practice; the
Trojaned twin model has enhanced attack efficacy and strong resiliency against
detection. Empirically, we show that our method achieves consistent attack efficacy
across different training schemes, including the challenging adversarial training
scheme. Furthermore, this Trojaned twin model is robust against SoTA
detection methods
Learning Latent Structural Causal Models
Authors: Jithendaraa Subramanian; Yashas Annadani; Ivaxi Sheth; Nan Rosemary Ke; Tristan Deleu; Stefan Bauer; Derek Nowrouzezahrai; Samira Ebrahimi Kahou
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Causal learning has long concerned itself with the accurate recovery of underlying causal mechanisms. Such causal modelling enables better explanations of out-of-distribution data. Prior works on causal learning assume that the high-level causal variables are given. However, in machine learning tasks, one often operates on low-level data like image pixels or high-dimensional vectors. In such settings, the entire Structural Causal Model (SCM) -- structure, parameters, \textit{and} high-level causal variables -- is unobserved and needs to be learnt from low-level data. We treat this problem as Bayesian inference of the latent SCM, given low-level data. For linear Gaussian additive noise SCMs, we present a tractable approximate inference method which performs joint inference over the causal variables, structure and parameters of the latent SCM from random, known interventions. Experiments are performed on synthetic datasets and a causally generated image dataset to demonstrate the efficacy of our approach. We also perform image generation from unseen interventions, thereby verifying out of distribution generalization for the proposed causal model.
Expressive Monotonic Neural Networks
Authors: Niklas Nolte; Ouail Kitouni; Mike Williams
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: The monotonic dependence of the outputs of a neural network on some of its inputs is a crucial inductive bias in many scenarios where domain knowledge dictates such behavior. This is especially important for interpretability and fairness considerations. In a broader context, scenarios in which monotonicity is important can be found in finance, medicine, physics, and other disciplines. It is thus desirable to build neural network architectures that implement this inductive bias provably. In this work, we propose a weight-constrained architecture with a single residual connection to achieve exact monotonic dependence in any subset of the inputs. The weight constraint scheme directly controls the Lipschitz constant of the neural network and thus provides the additional benefit of robustness. Compared to currently existing techniques used for monotonicity, our method is simpler in implementation and in theory foundations, has negligible computational overhead, is guaranteed to produce monotonic dependence, and is highly expressive. We show how the algorithm is used to train powerful, robust, and interpretable discriminators that achieve competitive performance compared to current state-of-the-art methods across various benchmarks, from social applications to the classification of the decays of subatomic particles produced at the CERN Large Hadron Collider.
The Benefits of Model-Based Generalization in Reinforcement Learning
Authors: Kenny John Young; Aditya Ramesh; Louis Kirsch; Jürgen Schmidhuber
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Model-Based Reinforcement Learning (RL) is widely believed to have the potential to improve sample efficiency by allowing an agent to synthesize large amounts of imagined experience. Experience Replay (ER) can be considered a simple kind of model, which has proved extremely effective at improving the stability and efficiency of deep RL. In principle, a learned parametric model could improve on ER by generalizing from real experience to augment the dataset with additional plausible experience. However, owing to the many design choices involved in empirically successful algorithms, it can be very hard to establish where the benefits are actually coming from. Here, we provide theoretical and empirical insight into when, and how, we can expect data generated by a learned model to be useful. First, we provide a general theorem motivating how learning a model as an intermediate step can narrow down the set of possible value functions more than learning a value function directly from data using the Bellman equation.  Second, we provide an illustrative example showing empirically how a similar effect occurs in a more concrete setting with neural network function approximation.  Finally, we provide extensive experiments showing the benefit of model-based learning for online RL in environments with combinatorial complexity, but factored structure that allows a learned model to generalize. In these experiments, we take care to control for other factors in order to isolate, insofar as possible, the benefit of using experience generated by a learned model relative to ER alone.
Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformers
Authors: Tianlong Chen; Zhenyu Zhang; AJAY KUMAR JAISWAL; Shiwei Liu; Zhangyang Wang
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Despite their remarkable achievement, gigantic transformers encounter significant drawbacks, including exorbitant computational and memory footprints during training, as well as severe collapse evidenced by a high degree of parameter redundancy. Sparsely-activated Mixture-of-Experts (SMoEs) have shown promise to mitigate the issue of training efficiency, yet they are prone to (1) $\textit{redundant experts}$ due to representational collapse; and (2) $\textit{poor expert scalability for inference and downstream fine-tuning}$, primarily due to overfitting of the learned routing policy to the number of activated experts during training. As recent research efforts are predominantly focused on improving routing policies to encourage expert specializations, this work focuses on $\textit{exploring the overlooked scalability bottleneck of SMoEs}$ and leveraging it to effectively $\textbf{scale dense transformers}$. To this end, we propose a new plug-and-play training framework, $\textbf{SMoE-Dropout}$, to enable scaling transformers to better accuracy in their full capacity without collapse. Specifically, SMoE-Dropout consists of a $\textit{randomly initialized and fixed}$ router network to activate experts and gradually increases the activated expert number as training progresses over time. Transformers trained by SMoE-Dropout naturally exhibit a $\textbf{``self-slimmable”}$ property subject to resource availability, offering smooth and consistent performance boosts with an increase in activated experts during inference or fine-tuning. Our extensive experiments across diverse transformer architectures on a variety of tasks demonstrate the superior performance and substantial computation savings of SMoE-Dropout, compared to dense training baselines with equivalent parameter counts. In particular, our trained BERT outperforms its densely trained counterpart with consistent improvements of {$1.03\%$, $0.78\%$, $1.09\%$} on challenging reasoning tasks {$\texttt{ASDiv-A}$, $\texttt{MAWPS}$, $\texttt{SVAMP}$}, respectively. Codes and models are available in https://github.com/VITA-Group/Random-MoE-as-Dropout.
The Modality Focusing Hypothesis: Towards Understanding Crossmodal Knowledge Distillation
Authors: Zihui Xue; Zhengqi Gao; Sucheng Ren; Hang Zhao
Venue: ICLR 2023 (ICLR 2023 notable top 5%)

Abstract: Crossmodal knowledge distillation (KD) extends traditional knowledge distillation to the area of multimodal learning and demonstrates great success in various applications. To achieve knowledge transfer across modalities, a pretrained network from one modality is adopted as the teacher to provide supervision signals to a student network learning from the other modality. In contrast to the empirical success reported in prior works, the working mechanism of crossmodal KD remains a mystery. In this paper, we present a thorough understanding of crossmodal KD. We begin by providing two failure cases and demonstrate that KD is not a universal cure in crossmodal knowledge transfer. We then present the modality Venn diagram to understand modality relationships and the modality focusing hypothesis revealing the decisive factor in the efficacy of crossmodal KD. Experimental results on 6 multimodal datasets help justify our hypothesis, diagnose failure cases, and point directions to improve crossmodal knowledge transfer in the future.
Advancing Radiograph Representation Learning with Masked Record Modeling
Authors: Hong-Yu Zhou; Chenyu Lian; Liansheng Wang; Yizhou Yu
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Modern studies in radiograph representation learning (R$^2$L) rely on either self-supervision to encode invariant semantics or associated radiology reports to incorporate medical expertise, while the complementarity between them is barely noticed. To explore this, we formulate the self- and report-completion as two complementary objectives and present a unified framework based on masked record modeling (MRM). In practice, MRM reconstructs masked image patches and masked report tokens following a multi-task scheme to learn knowledge-enhanced semantic representations. With MRM pre-training, we obtain pre-trained models that can be well transferred to various radiography tasks. Specifically, we find that MRM offers superior performance in label-efficient fine-tuning. For instance, MRM achieves 88.5% mean AUC on CheXpert using 1% labeled data, outperforming previous R$^2$L methods with 100% labels. On NIH ChestX-ray, MRM outperforms the best performing counterpart by about 3% under small labeling ratios. Besides, MRM surpasses self- and report-supervised pre-training in identifying the pneumonia type and the pneumothorax area, sometimes by large margins.
FARE: Provably Fair Representation Learning
Authors: Nikola Jovanović; Mislav Balunovic; Dimitar Iliev Dimitrov; Martin Vechev
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Fair representation learning (FRL) is a popular class of methods aiming to produce fair classifiers via data preprocessing. However, recent work has shown that prior methods achieve worse accuracy-fairness tradeoffs than originally suggested by their results. This dictates the need for FRL methods that provide provable upper bounds on unfairness of any downstream classifier, a challenge yet unsolved. In this work we address this challenge and propose Fairness with Restricted Encoders (FARE), the first FRL method with provable fairness guarantees. Our key insight is that restricting the representation space of the encoder enables us to derive suitable fairness guarantees, while allowing empirical accuracy-fairness tradeoffs comparable to prior work. FARE instantiates this idea with a tree-based encoder, a choice motivated by inherent advantages of decision trees when applied in our setting. Crucially, we develop and apply a practical statistical procedure that computes a high-confidence upper bound on the unfairness of any downstream classifier. In our experimental evaluation on several datasets and settings we demonstrate that FARE produces tight upper bounds, often comparable with empirical results of prior methods, which establishes the practical value of our approach.
Untangling Effect and Side Effect: Consistent Causal Inference in Non-Targeted Trials
Authors: Georgios Mavroudeas; Malik Magdon-Ismail; Kristin Bennett; Jason Kuruzovich
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: A treatment is usually appropriate for some group (the ``sick" group) on whom it has an effect, but it can also have a side-effect when given to subjects from another group (the ``healthy" group). In a non-targeted trial both sick and healthy subjects may be treated, producing
heterogeneous effects within the treated group. Inferring the correct treatment effect on the sick population is then 
difficult, because the effect and side-effect are tangled. We propose an efficient nonparametric approach to untangling the effect and side-effect, called  PCM (pre-cluster and merge). We prove its asymptotic consistency in a general setting and
show, on synthetic data, 
more than a 10x improvement in accuracy over existing state-of-the-art.
A Non-monotonic Self-terminating Language Model
Authors: Eugene Choi; Kyunghyun Cho; Cheolhyoung Lee
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Recent large-scale neural autoregressive sequence models have shown impressive performances on a variety of natural language generation tasks. However, their generated sequences often exhibit degenerate properties such as non-termination, undesirable repetition, and premature termination, when generated with decoding algorithms such as greedy search, beam search, top-$k$ sampling, and nucleus sampling. In this paper, we focus on the problem of non-terminating sequences resulting from an incomplete decoding algorithm. We first define an incomplete probable decoding algorithm which includes greedy search, top-$k$ sampling, and nucleus sampling, beyond the incomplete decoding algorithm originally put forward by Welleck et al. (2020). We then propose a non-monotonic self-terminating language model, which significantly relaxes the constraint of monotonically increasing termination probability in the originally proposed self-terminating language model by Welleck et al. (2020), to address the issue of non-terminating sequences when using incomplete probable decoding algorithms. We prove that our proposed model prevents non-terminating sequences when using not only incomplete probable decoding algorithms but also beam search. We empirically validate our model on sequence completion tasks with various architectures.
Minimum Variance Unbiased N:M Sparsity for the Neural Gradients
Authors: Brian Chmiel; Itay Hubara; Ron Banner; Daniel Soudry
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: In deep learning, fine-grained N:M sparsity reduces the data footprint and bandwidth of a General Matrix multiply (GEMM) up to x2,  and doubles throughput by skipping computation of zero values. So far, it was mainly only used to prune weights to accelerate the forward and backward phases. We examine how this method can be used also for the neural gradients (i.e. loss gradients with respect to the intermediate neural layer outputs). To this end, we first establish a tensor-level optimality criteria. Previous works aimed to minimize the mean-square-error (MSE) of each pruned block. We show that while minimization of the MSE works fine for pruning the weights and activations, it catastrophically fails for the neural gradients. Instead, we show that accurate pruning of the neural gradients requires an unbiased minimum-variance pruning mask. We design such specialized masks, and find that in most cases, 1:2 sparsity is sufficient for training, and 2:4 sparsity is usually enough when this is not the case. Further, we suggest combining several such methods together in order to potentially speed up training even more. A reference implementation is supplied in the supplementary material.
AANG : Automating Auxiliary Learning
Authors: Lucio M. Dery; Paul Michel; Mikhail Khodak; Graham Neubig; Ameet Talwalkar
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Auxiliary objectives, supplementary learning signals that are introduced to help aid learning on data-starved or highly complex end-tasks, are commonplace in machine learning. Whilst much work has been done to formulate useful auxiliary objectives, their construction is still an art which proceeds by slow and tedious hand-design. Intuition for how and when these objectives improve end-task performance has also had limited theoretical backing. In this work, we present an approach for automatically generating a suite of auxiliary objectives. We achieve this by deconstructing existing objectives within a novel unified taxonomy, identifying connections between them, and generating new ones based on the uncovered structure. Next, we theoretically formalize widely-held intuitions about how auxiliary learning improves generalization on the end-task. This leads us to a principled and efficient algorithm for searching the space of generated objectives to find those most useful to a specified end-task.
With natural language processing (NLP) as our domain of study, we demonstrate that our automated auxiliary learning pipeline leads to strong improvements over competitive baselines across continued training experiments on a pre-trained model on 5 NLP end-tasks.
A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks
Authors: Marc Anton Finzi; Andres Potapczynski; Matthew Choptuik; Andrew Gordon Wilson
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Unlike conventional grid and mesh based methods for solving partial differential equations (PDEs), neural networks have the potential to break the curse of dimensionality, providing approximate solutions to problems where using classical solvers is difficult or impossible. While global minimization of the PDE residual over the network parameters works well for boundary value problems, catastrophic forgetting impairs applicability to initial value problems (IVPs). In an alternative local-in-time approach, the optimization problem can be converted into an ordinary differential equation (ODE) on the network parameters and the solution propagated forward in time; however, we demonstrate that current methods based on this approach suffer from two key issues. First, following the ODE produces an uncontrolled growth in the conditioning of the problem, ultimately leading to unacceptably large numerical errors. Second, as the ODE methods scale cubically with the number of model parameters, they are restricted to small neural networks, significantly limiting their ability to represent intricate PDE initial conditions and solutions. Building on these insights, we develop Neural-IVP, an ODE based IVP solver which prevents the network from getting ill-conditioned and runs in time linear in the number of parameters, enabling us to evolve the dynamics of challenging PDEs with neural networks.
Towards Robust Object Detection Invariant to Real-World Domain Shifts
Authors: Qi Fan; Mattia Segu; Yu-Wing Tai; Fisher Yu; Chi-Keung Tang; Bernt Schiele; Dengxin Dai
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Safety-critical applications such as autonomous driving require robust object detection invariant to real-world domain shifts. Such shifts can be regarded as different domain styles, which can vary substantially due to environment changes and sensor noises, but deep models only know the training domain style. Such domain style gap impedes object detection generalization on diverse real-world domains. Existing classification domain generalization (DG) methods cannot effectively solve the robust object detection problem, because they either rely on multiple source domains with large style variance or destroy the content structures of the original images. In this paper, we analyze and investigate effective solutions to overcome domain style overfitting for robust object detection without the above shortcomings. Our method, dubbed as Normalization Perturbation (NP), perturbs the channel statistics of source domain low-level features to synthesize various latent styles, so that the trained deep model can perceive diverse potential domains and generalizes well even without observations of target domain data in training. This approach is motivated by the observation that feature channel statistics of the target domain images deviate around the source domain statistics. We further explore the style-sensitive channels for effective style synthesis. Normalization Perturbation only relies on a single source domain and is surprisingly simple and effective, contributing a practical solution by effectively adapting or generalizing classification DG methods to robust object detection. Extensive experiments demonstrate the effectiveness of our method for generalizing object detectors under real-world domain shifts.
Neural Causal Models for Counterfactual Identification and Estimation
Authors: Kevin Muyuan Xia; Yushu Pan; Elias Bareinboim
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Evaluating hypothetical statements about how the world would be had a different course of action been taken is arguably one key capability expected from modern AI systems. Counterfactual reasoning underpins discussions in fairness, the determination of blame and responsibility, credit assignment, and regret. In this paper, we study the evaluation of counterfactual statements through neural models. Specifically, we tackle two causal problems required to make such evaluations, i.e., counterfactual identification and estimation from an arbitrary combination of observational and experimental data. First, we show that neural causal models (NCMs) are expressive enough and encode the structural constraints necessary for performing counterfactual reasoning. Second, we develop an algorithm for simultaneously identifying and estimating counterfactual distributions. We show that this algorithm is sound and complete for deciding counterfactual identification in general settings. Third, considering the practical implications of these results, we introduce a new strategy for modeling NCMs using generative adversarial networks. Simulations corroborate with the proposed methodology.
Moving Forward by Moving Backward: Embedding Action Impact over Action Semantics
Authors: Kuo-Hao Zeng; Luca Weihs; Roozbeh Mottaghi; Ali Farhadi
Venue: ICLR 2023 (ICLR 2023 notable top 5%)

Abstract: A common assumption when training embodied agents is that the impact of taking an action is stable; for instance, executing the ``move ahead'' action will always move the agent forward by a fixed distance, perhaps with some small amount of actuator-induced noise. This assumption is limiting; an agent may encounter settings that dramatically alter the impact of actions: a move ahead action on a wet floor may send the agent twice as far as it expects and using the same action with a broken wheel might transform the expected translation into a rotation. Instead of relying that the impact of an action stably reflects its pre-defined semantic meaning, we propose to model the impact of actions on-the-fly using latent embeddings. By combining these latent action embeddings with a novel, transformer-based, policy head, we design an Action Adaptive Policy (AAP). We evaluate our AAP on two challenging visual navigation tasks in the AI2-THOR and Habitat environments and show that our AAP is highly performant even when faced, at inference-time, with missing actions and, previously unseen, perturbed action spaces. Moreover, we observe significant improvement in robustness against these actions when evaluating in real-world scenarios.