diff --git a/docker/mlflow/docker-compose.yml b/docker/mlflow/docker-compose.yml
index cb1acbbc8..2b11bb5e2 100644
--- a/docker/mlflow/docker-compose.yml
+++ b/docker/mlflow/docker-compose.yml
@@ -64,8 +64,6 @@ services:
       - "5000:5000"
     environment:
       - MLFLOW_S3_ENDPOINT_URL=http://minio:9000
-      - AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID}
-      - AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY}
     command: mlflow server --backend-store-uri mysql+pymysql://${MYSQL_USER}:${MYSQL_PASSWORD}@db:3306/${MYSQL_DATABASE} --default-artifact-root s3://mlflow/ --host 0.0.0.0
 
 volumes:
diff --git a/params.yaml b/params.yaml
index 51cce0865..7a26f0c32 100644
--- a/params.yaml
+++ b/params.yaml
@@ -9,15 +9,46 @@ active:
   - noise_removal
   models:
   - logistic_regression
+  - knn
+  - svm
+  - gradient_boosting_classifier
   datasets:
-  - diabetes
+  - cifar10
+  - click
+  - covertype
   - cpu
+  - diabetes
+  - fmnist_binary
+  - mnist_binary
+  - mnist_multi
+  - phoneme
   valuation_methods:
   - random
   - loo
+  - beta_shapley
+  - tmc_shapley
+  - classwise_shapley
   repetitions:
   - 1
   - 2
+  - 3
+  - 4
+  - 5
+  - 6
+  - 7
+  - 8
+  - 9
+  - 10
+  - 11
+  - 12
+  - 13
+  - 14
+  - 15
+  - 16
+  - 17
+  - 18
+  - 19
+  - 20
 experiments:
   point_removal:
     sampler: default
@@ -27,11 +58,36 @@ experiments:
         metric: accuracy
         eval_model: logistic_regression
         len_curve_perc: 0.5
+      weighted_accuracy_drop_logistic_regression_balanced:
+        idx: weighted_metric_drop
+        metric: accuracy
+        eval_model: logistic_regression_balanced
+        len_curve_perc: 0.5
       weighted_accuracy_drop_knn:
         idx: weighted_metric_drop
         metric: accuracy
         eval_model: knn
         len_curve_perc: 0.5
+      weighted_accuracy_drop_gradient_boosting_classifier:
+        idx: weighted_metric_drop
+        metric: accuracy
+        eval_model: gradient_boosting_classifier
+        len_curve_perc: 0.5
+      weighted_accuracy_drop_svm:
+        idx: weighted_metric_drop
+        metric: accuracy
+        eval_model: svm
+        len_curve_perc: 0.5
+      weighted_accuracy_drop_svm_balanced:
+        idx: weighted_metric_drop
+        metric: accuracy
+        eval_model: svm_balanced
+        len_curve_perc: 0.5
+      weighted_accuracy_drop_mlp:
+        idx: weighted_metric_drop
+        metric: accuracy
+        eval_model: mlp
+        len_curve_perc: 0.5
 
   noise_removal:
     sampler: default
@@ -60,6 +116,51 @@ datasets:
       threshold_y:
         threshold: 89
 
+  click:
+    openml_id: 1216
+
+  covertype:
+    openml_id: 1596
+
+  phoneme:
+    openml_id: 1489
+
+  fmnist_binary:
+    openml_id: 40996
+    filters:
+      binarization:
+        label_zero: '0'
+        label_one: '1'
+    preprocessor:
+      principal_resnet_components:
+        n_components: 32
+        grayscale: true
+        seed: 101
+
+  cifar10:
+    openml_id: 40927
+    filters:
+      binarization:
+        label_zero: '1'
+        label_one: '9'
+    preprocessor:
+      principal_resnet_components:
+        n_components: 32
+        grayscale: false
+        seed: 102
+
+  mnist_binary:
+    openml_id: 554
+    filters:
+      binarization:
+        label_zero: '1'
+        label_one: '7'
+    preprocessor:
+      principal_resnet_components:
+        n_components: 32
+        grayscale: true
+        seed: 103
+
   mnist_multi:
     openml_id: 554
     preprocessor:
@@ -73,11 +174,34 @@ models:
     model: logistic_regression
     solver: liblinear
 
+  logistic_regression_balanced:
+    model: logistic_regression
+    solver: liblinear
+    class_weight: balanced
+
+  gradient_boosting_classifier:
+    model: gradient_boosting_classifier
+    n_estimators: 40
+    min_samples_split: 6
+    max_depth: 2
+
   knn:
     model: knn
     n_neighbors: 5
     weights: uniform
 
+  svm:
+    model: svm
+    kernel: rbf
+
+  svm_balanced:
+    model: svm
+    kernel: rbf
+    class_weight: balanced
+
+  mlp:
+    model: mlp
+
 valuation_methods:
   random:
     algorithm: random
@@ -86,3 +210,53 @@ valuation_methods:
     algorithm: loo
     progress: true
     cache_group: acc
+
+  classwise_shapley:
+    algorithm: classwise_shapley
+    cache_group: disc_acc
+    normalize_values: true
+    n_resample_complement_sets: 1
+    n_updates: 500
+    rtol: 1e-4
+    progress: true
+    use_default_scorer_value: false
+
+
+  beta_shapley:
+    algorithm: beta_shapley
+    alpha: 16.0
+    beta: 1.0
+    n_updates: 500
+    progress: true
+    cache_group: acc
+
+
+  banzhaf_shapley:
+    algorithm: banzhaf_shapley
+    n_updates: 500
+    progress: true
+    cache_group: acc
+
+
+  tmc_shapley:
+    algorithm: tmc_shapley
+    rtol: 1e-4
+    n_updates: 500
+    progress: true
+    cache_group: acc
+
+
+  owen_sampling_shapley:
+    algorithm: owen_sampling_shapley
+    n_updates: 10
+    max_q: 50
+    progress: false
+    cache_group: acc
+
+
+  least_core:
+    algorithm: least_core
+    n_updates: 5000
+    progress: false
+    cache_group: acc
+
diff --git a/poetry.lock b/poetry.lock
index a5587d21e..ab1177dd0 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2867,6 +2867,147 @@ files = [
     {file = "numpy-1.26.3.tar.gz", hash = "sha256:697df43e2b6310ecc9d95f05d5ef20eacc09c7c4ecc9da3f235d39e71b7da1e4"},
 ]
 
+[[package]]
+name = "nvidia-cublas-cu12"
+version = "12.1.3.1"
+description = "CUBLAS native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl", hash = "sha256:ee53ccca76a6fc08fb9701aa95b6ceb242cdaab118c3bb152af4e579af792728"},
+    {file = "nvidia_cublas_cu12-12.1.3.1-py3-none-win_amd64.whl", hash = "sha256:2b964d60e8cf11b5e1073d179d85fa340c120e99b3067558f3cf98dd69d02906"},
+]
+
+[[package]]
+name = "nvidia-cuda-cupti-cu12"
+version = "12.1.105"
+description = "CUDA profiling tools runtime libs."
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:e54fde3983165c624cb79254ae9818a456eb6e87a7fd4d56a2352c24ee542d7e"},
+    {file = "nvidia_cuda_cupti_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:bea8236d13a0ac7190bd2919c3e8e6ce1e402104276e6f9694479e48bb0eb2a4"},
+]
+
+[[package]]
+name = "nvidia-cuda-nvrtc-cu12"
+version = "12.1.105"
+description = "NVRTC native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:339b385f50c309763ca65456ec75e17bbefcbbf2893f462cb8b90584cd27a1c2"},
+    {file = "nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:0a98a522d9ff138b96c010a65e145dc1b4850e9ecb75a0172371793752fd46ed"},
+]
+
+[[package]]
+name = "nvidia-cuda-runtime-cu12"
+version = "12.1.105"
+description = "CUDA Runtime native Libraries"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:6e258468ddf5796e25f1dc591a31029fa317d97a0a94ed93468fc86301d61e40"},
+    {file = "nvidia_cuda_runtime_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:dfb46ef84d73fababab44cf03e3b83f80700d27ca300e537f85f636fac474344"},
+]
+
+[[package]]
+name = "nvidia-cudnn-cu12"
+version = "8.9.2.26"
+description = "cuDNN runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl", hash = "sha256:5ccb288774fdfb07a7e7025ffec286971c06d8d7b4fb162525334616d7629ff9"},
+]
+
+[package.dependencies]
+nvidia-cublas-cu12 = "*"
+
+[[package]]
+name = "nvidia-cufft-cu12"
+version = "11.0.2.54"
+description = "CUFFT native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl", hash = "sha256:794e3948a1aa71fd817c3775866943936774d1c14e7628c74f6f7417224cdf56"},
+    {file = "nvidia_cufft_cu12-11.0.2.54-py3-none-win_amd64.whl", hash = "sha256:d9ac353f78ff89951da4af698f80870b1534ed69993f10a4cf1d96f21357e253"},
+]
+
+[[package]]
+name = "nvidia-curand-cu12"
+version = "10.3.2.106"
+description = "CURAND native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:9d264c5036dde4e64f1de8c50ae753237c12e0b1348738169cd0f8a536c0e1e0"},
+    {file = "nvidia_curand_cu12-10.3.2.106-py3-none-win_amd64.whl", hash = "sha256:75b6b0c574c0037839121317e17fd01f8a69fd2ef8e25853d826fec30bdba74a"},
+]
+
+[[package]]
+name = "nvidia-cusolver-cu12"
+version = "11.4.5.107"
+description = "CUDA solver native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl", hash = "sha256:8a7ec542f0412294b15072fa7dab71d31334014a69f953004ea7a118206fe0dd"},
+    {file = "nvidia_cusolver_cu12-11.4.5.107-py3-none-win_amd64.whl", hash = "sha256:74e0c3a24c78612192a74fcd90dd117f1cf21dea4822e66d89e8ea80e3cd2da5"},
+]
+
+[package.dependencies]
+nvidia-cublas-cu12 = "*"
+nvidia-cusparse-cu12 = "*"
+nvidia-nvjitlink-cu12 = "*"
+
+[[package]]
+name = "nvidia-cusparse-cu12"
+version = "12.1.0.106"
+description = "CUSPARSE native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:f3b50f42cf363f86ab21f720998517a659a48131e8d538dc02f8768237bd884c"},
+    {file = "nvidia_cusparse_cu12-12.1.0.106-py3-none-win_amd64.whl", hash = "sha256:b798237e81b9719373e8fae8d4f091b70a0cf09d9d85c95a557e11df2d8e9a5a"},
+]
+
+[package.dependencies]
+nvidia-nvjitlink-cu12 = "*"
+
+[[package]]
+name = "nvidia-nccl-cu12"
+version = "2.18.1"
+description = "NVIDIA Collective Communication Library (NCCL) Runtime"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_nccl_cu12-2.18.1-py3-none-manylinux1_x86_64.whl", hash = "sha256:1a6c4acefcbebfa6de320f412bf7866de856e786e0462326ba1bac40de0b5e71"},
+]
+
+[[package]]
+name = "nvidia-nvjitlink-cu12"
+version = "12.3.101"
+description = "Nvidia JIT LTO Library"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_nvjitlink_cu12-12.3.101-py3-none-manylinux1_x86_64.whl", hash = "sha256:64335a8088e2b9d196ae8665430bc6a2b7e6ef2eb877a9c735c804bd4ff6467c"},
+    {file = "nvidia_nvjitlink_cu12-12.3.101-py3-none-win_amd64.whl", hash = "sha256:1b2e317e437433753530792f13eece58f0aec21a2b05903be7bffe58a606cbd1"},
+]
+
+[[package]]
+name = "nvidia-nvtx-cu12"
+version = "12.1.105"
+description = "NVIDIA Tools Extension"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:dc21cf308ca5691e7c04d962e213f8a4aa9bbfa23d95412f452254c2caeb09e5"},
+    {file = "nvidia_nvtx_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:65f4d98982b31b60026e0e6de73fbdfc09d08a96f4656dd3665ca616a11e1e82"},
+]
+
 [[package]]
 name = "oauthlib"
 version = "3.2.2"
@@ -4678,77 +4819,91 @@ files = [
 
 [[package]]
 name = "torch"
-version = "2.0.1"
+version = "2.1.2"
 description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
 filelock = "*"
+fsspec = "*"
 jinja2 = "*"
 networkx = "*"
+nvidia-cublas-cu12 = {version = "12.1.3.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-cupti-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-nvrtc-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-runtime-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cudnn-cu12 = {version = "8.9.2.26", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cufft-cu12 = {version = "11.0.2.54", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-curand-cu12 = {version = "10.3.2.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cusolver-cu12 = {version = "11.4.5.107", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cusparse-cu12 = {version = "12.1.0.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nccl-cu12 = {version = "2.18.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 sympy = "*"
+triton = {version = "2.1.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 typing-extensions = "*"
 
 [package.extras]
+dynamo = ["jinja2"]
 opt-einsum = ["opt-einsum (>=3.3)"]
 
 [[package]]
 name = "torchvision"
-version = "0.15.2"
+version = "0.16.2"
 description = "image and video datasets and models for torch deep learning"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "torchvision-0.16.2-cp311-cp311-win_amd64.whl", hash = "sha256:8692ab1e48807e9604046a6f4beeb67b523294cee1b00828654bb0df2cfce2b2"},
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+    {file = "torchvision-0.16.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:4b065143d1a720fe8a9077fd4be35d491f98819ec80b3dbbc3ec64d0b707a906"},
+    {file = "torchvision-0.16.2-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:bc5f274e4ecd1b86062063cdf4fd385a1d39d147a3a2685fbbde9ff08bb720b8"},
+    {file = "torchvision-0.16.2-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:335959c43b371c0474af34c1ef2a52efdc7603c45700d29e4475eeb02984170c"},
+    {file = "torchvision-0.16.2-cp38-cp38-win_amd64.whl", hash = "sha256:7fd22d86e08eba321af70cad291020c2cdeac069b00ce88b923ca52e06174769"},
+    {file = "torchvision-0.16.2-cp39-cp39-macosx_10_13_x86_64.whl", hash = "sha256:56115268b37f0b75364e3654e47ad9abc66ac34c1f9e5e3dfa89a22d6a40017a"},
+    {file = "torchvision-0.16.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:82805f8445b094f9d1e770390ee6cc86855e89955e08ce34af2e2274fc0e5c45"},
+    {file = "torchvision-0.16.2-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:3f4bd5fcbc361476e2e78016636ac7d5509e59d9962521f06eb98e6803898182"},
+    {file = "torchvision-0.16.2-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:8199acdf8ab066a28b84a5b6f4d97b58976d9e164b1acc3a9d14fccfaf74bb3a"},
+    {file = "torchvision-0.16.2-cp39-cp39-win_amd64.whl", hash = "sha256:41dd4fa9f176d563fe9f1b9adef3b7e582cdfb60ce8c9bc51b094a025be687c9"},
 ]
 
 [package.dependencies]
 numpy = "*"
 pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0"
 requests = "*"
-torch = "2.0.1"
+torch = "2.1.2"
 
 [package.extras]
 scipy = ["scipy"]
@@ -4808,6 +4963,31 @@ files = [
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
 test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<7.5)", "pytest-mock", "pytest-mypy-testing"]
 
+[[package]]
+name = "triton"
+version = "2.1.0"
+description = "A language and compiler for custom Deep Learning operations"
+optional = false
+python-versions = "*"
+files = [
+    {file = "triton-2.1.0-0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:66439923a30d5d48399b08a9eae10370f6c261a5ec864a64983bae63152d39d7"},
+    {file = "triton-2.1.0-0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:919b06453f0033ea52c13eaf7833de0e57db3178d23d4e04f9fc71c4f2c32bf8"},
+    {file = "triton-2.1.0-0-cp37-cp37m-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ae4bb8a91de790e1866405211c4d618379781188f40d5c4c399766914e84cd94"},
+    {file = "triton-2.1.0-0-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:39f6fb6bdccb3e98f3152e3fbea724f1aeae7d749412bbb1fa9c441d474eba26"},
+    {file = "triton-2.1.0-0-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:21544e522c02005a626c8ad63d39bdff2f31d41069592919ef281e964ed26446"},
+    {file = "triton-2.1.0-0-pp37-pypy37_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:143582ca31dd89cd982bd3bf53666bab1c7527d41e185f9e3d8a3051ce1b663b"},
+    {file = "triton-2.1.0-0-pp38-pypy38_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:82fc5aeeedf6e36be4e4530cbdcba81a09d65c18e02f52dc298696d45721f3bd"},
+    {file = "triton-2.1.0-0-pp39-pypy39_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:81a96d110a738ff63339fc892ded095b31bd0d205e3aace262af8400d40b6fa8"},
+]
+
+[package.dependencies]
+filelock = "*"
+
+[package.extras]
+build = ["cmake (>=3.18)", "lit"]
+tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)"]
+tutorials = ["matplotlib", "pandas", "tabulate"]
+
 [[package]]
 name = "typer"
 version = "0.9.0"
@@ -5129,4 +5309,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = "~3.10"
-content-hash = "ad115920c240c9e66e499d554dff120dc9872810d5581cef745b590ca2dad778"
+content-hash = "18ef6e5a8d5464091ddce33bfa5fb78987db53d7506037ccc92170d70aac6ddf"
diff --git a/pyproject.toml b/pyproject.toml
index e048743c1..e65232e87 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -16,8 +16,8 @@ matplotlib = "^3.6.3"
 seaborn = "^0.12.2"
 dvc = "^3.15.2"
 tqdm = "^4.64.1"
-torch = "^2.0.0"
-torchvision = "^0.15.1"
+torch = "2.1.2"
+torchvision = "0.16.2"
 openml = "^0.13.0"
 click = "^8.1.3"
 mlflow = "^2.9.2"
diff --git a/scripts/determine_in_out_of_cls_marginal_accuracies.py b/scripts/determine_in_out_of_cls_marginal_accuracies.py
index 5a58660aa..c4db3cabd 100644
--- a/scripts/determine_in_out_of_cls_marginal_accuracies.py
+++ b/scripts/determine_in_out_of_cls_marginal_accuracies.py
@@ -37,7 +37,7 @@
 @click.command()
 @click.option("--experiment-name", type=str, required=True)
 @click.option("--model-name", type=str, required=True)
-@click.option("--valuation-method-name", type=str, default="loo")
+@click.option("--valuation-method-name", type=str, default="tmc_shapley")
 @click.option("--max-plotting-percentage", type=float, default=1e-4)
 def determine_in_cls_out_of_cls_marginal_accuracies(
     experiment_name: str,
@@ -62,7 +62,7 @@ def determine_in_cls_out_of_cls_marginal_accuracies(
 def _determine_in_cls_out_of_cls_marginal_accuracies(
     experiment_name: str,
     model_name: str = "logistic_regression",
-    valuation_method_name: str = "loo",
+    valuation_method_name: str = "tmc_shapley",
     max_plotting_percentage: float = 1e-4,
 ):
     output_dir = Accessor.INFO_PATH / experiment_name
diff --git a/scripts/render_plots.py b/scripts/render_plots.py
index 67c9d85c8..a09061c9a 100644
--- a/scripts/render_plots.py
+++ b/scripts/render_plots.py
@@ -104,7 +104,7 @@ def _render_plots(experiment_name: str, model_name: str):
         )
         for method_name in method_names:
             logger.info(f"Plot histogram for method {method_name} values.")
-            with plot_histogram(valuation_results, [method_name, "tmc_shapley"]) as fig:
+            with plot_histogram(valuation_results, [method_name]) as fig:
                 log_figure(
                     fig, output_folder, f"density.{method_name}.svg", "densities"
                 )
diff --git a/src/re_classwise_shapley/metric.py b/src/re_classwise_shapley/metric.py
index 21d05cc0f..c2d87767d 100644
--- a/src/re_classwise_shapley/metric.py
+++ b/src/re_classwise_shapley/metric.py
@@ -10,7 +10,7 @@
     init_executor,
     init_parallel_backend,
 )
-from pydvl.utils import Dataset, Scorer, Seed, Utility
+from pydvl.utils import CacheBackend, Dataset, Scorer, Seed, Utility
 from pydvl.value.result import ValuationResult
 from sklearn.metrics import auc
 from tqdm import tqdm
@@ -57,7 +57,8 @@ def metric_weighted_metric_drop(
     n_jobs: int = 1,
     config: ParallelConfig = ParallelConfig(),
     progress: bool = False,
-    seed: Optional[Seed] = None,
+    seed: Seed | None = None,
+    cache: CacheBackend | None = None,
 ) -> Tuple[float, pd.Series]:
     r"""
     Calculates the weighted reciprocal difference average of a valuation function. The
@@ -89,7 +90,7 @@ def metric_weighted_metric_drop(
         model=model,
         scorer=Scorer(metric, default=np.nan),
         catch_errors=True,
-        enable_cache=True,
+        cache_backend=cache,
     )
     curve = _curve_score_over_point_removal_or_addition(
         u_eval,
diff --git a/src/re_classwise_shapley/model.py b/src/re_classwise_shapley/model.py
index 5f03a51f5..463de9f36 100644
--- a/src/re_classwise_shapley/model.py
+++ b/src/re_classwise_shapley/model.py
@@ -43,7 +43,7 @@ def instantiate_model(
         "mlp_classifier": MLPClassifier,
     }
     model_class = model_dict[model_idx]
-    model = maybe_add_argument(model_class, random_state)(
+    model = maybe_add_argument(model_class, "random_state")(
         **model_kwargs, random_state=random_state
     )
     pipeline = make_pipeline(StandardScaler(), model)